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-[{"text": "The gr.UndoData class is a subclass of gr.Event data that specifically\ncarries information about the `.undo()` event. When gr.UndoData is added as a\ntype hint to an argument of an event listener method, a gr.UndoData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/undodata", "source_page_title": "Gradio - Undodata Docs"}, {"text": "import gradio as gr\n    \n    def undo(retry_data: gr.UndoData, history: list[gr.MessageDict]):\n        history_up_to_retry = history[:retry_data.index]\n        return history_up_to_retry\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot()\n        chatbot.undo(undo, chatbot, chatbot)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/undodata", "source_page_title": "Gradio - Undodata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the user message that should be undone.\n\n\n    \n    \n    value: Any\n\nThe value of the user message that should be undone.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/undodata", "source_page_title": "Gradio - Undodata Docs"}, {"text": "Creates a slider that ranges from `minimum` to `maximum` with a step size\nof `step`.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "**As input component** : Passes slider value as a `float` into the\nfunction.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: float\n    )\n    \t...\n\n  \n\n**As output component** : Expects an `int` or `float` returned from\nfunction and sets slider value to it as long as it is within range (otherwise,\nsets to minimum value).\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    minimum: float\n\ndefault `= 0`\n\nminimum value for slider. When used as an input, if a user provides a smaller\nvalue, a gr.Error exception is raised by the backend.\n\n\n    \n    \n    maximum: float\n\ndefault `= 100`\n\nmaximum value for slider. When used as an input, if a user provides a larger\nvalue, a gr.Error exception is raised by the backend.\n\n\n    \n    \n    value: float | Callable | None\n\ndefault `= None`\n\ndefault value for slider. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\nIgnored if randomized=True.\n\n\n    \n    \n    step: float | None\n\ndefault `= None`\n\nincrement between slider values.\n\n\n    \n    \n    precision: int | None\n\ndefault `= None`\n\nPrecision to round input/output to. If set to 0, will round to nearest integer\nand convert type to int. If None, no rounding happens.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "sed as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, slider will be adjustable; if False, adjusting will be disabled. If\nnot provided, this is inferred based on whether the component is used as an\ninput or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same ke", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": " intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    randomize: bool\n\ndefault `= False`\n\nIf True, the value of the slider when the app loads is taken uniformly at\nrandom from the range given by the minimum and maximum.\n\n\n    \n    \n    show_reset_button: bool\n\ndefault `= True`\n\nif False, will hide button to reset slider to default value.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Slider` | \"slider\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "sentence_builderslider_releaseinterface_random_sliderblocks_random_slider\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.Checkb", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "abel=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def identity(x, state): state += 1 return x,\nstate, state with gr.Blocks() as demo: slider = gr.Slider(0, 100, step=0.1)\nstate = gr.State(value=0) with gr.Row(): number = gr.Number(label=\"On\nrelease\") number2 = gr.Number(label=\"Number of events fired\")\nslider.release(identity, inputs=[slider, state], outputs=[number, state,\nnumber2], api_name=\"predict\") if __name__ == \"__main__\": print(\"here\")\ndemo.launch() print(demo.server_port)\n\n    \n    \n    import gradio as gr\n    \n    def identity(x, state):\n        state += 1\n        return x, state, state\n    \n    with gr.Blocks() as demo:\n        slider = gr.Slider(0, 100, step=0.1)\n        state = gr.State(value=0)\n        with gr.Row():\n            number = gr.Number(label=\"On release\")\n            number2 = gr.Number(label=\"Number o", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "        slider = gr.Slider(0, 100, step=0.1)\n        state = gr.State(value=0)\n        with gr.Row():\n            number = gr.Number(label=\"On release\")\n            number2 = gr.Number(label=\"Number of events fired\")\n        slider.release(identity, inputs=[slider, state], outputs=[number, state, number2], api_name=\"predict\")\n    \n    if __name__ == \"__main__\":\n        print(\"here\")\n        demo.launch()\n        print(demo.server_port)\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def func(slider_1, slider_2, *args): return\nslider_1 + slider_2 * 5 demo = gr.Interface( func, [ gr.Slider(minimum=1.5,\nmaximum=250000.89, randomize=True, label=\"Random Big Range\"),\ngr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only\nmultiple of 0.05 allowed\"), gr.Slider(minimum=0, maximum=1, randomize=True,\nstep=0.25, label=\"Random only multiples of 0.25 allowed\"),\ngr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random\nbetween -100 and 100 step 3\"), gr.Slider(minimum=-100, maximum=100,\nrandomize=True, label=\"Random between -100 and 100\"), gr.Slider(value=0.25,\nminimum=5, maximum=30, step=-1), ], \"number\", ) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def func(slider_1, slider_2, *args):\n        return slider_1 + slider_2 * 5\n    \n    demo = gr.Interface(\n        func,\n        [\n            gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n            gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n            gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n            gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n            gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n            gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n        ],\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "d 100 step 3\"),\n            gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n            gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n        ],\n        \"number\",\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def func(slider_1, slider_2): return slider_1\n* 5 + slider_2 with gr.Blocks() as demo: slider = gr.Slider(minimum=-10.2,\nmaximum=15, label=\"Random Slider (Static)\", randomize=True) slider_1 =\ngr.Slider(minimum=100, maximum=200, label=\"Random Slider (Input 1)\",\nrandomize=True) slider_2 = gr.Slider(minimum=10, maximum=23.2, label=\"Random\nSlider (Input 2)\", randomize=True) slider_3 = gr.Slider(value=3, label=\"Non\nrandom slider\") btn = gr.Button(\"Run\") btn.click(func, inputs=[slider_1,\nslider_2], outputs=gr.Number()) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def func(slider_1, slider_2):\n        return slider_1 * 5 + slider_2\n    \n    with gr.Blocks() as demo:\n        slider = gr.Slider(minimum=-10.2, maximum=15, label=\"Random Slider (Static)\", randomize=True)\n        slider_1 = gr.Slider(minimum=100, maximum=200, label=\"Random Slider (Input 1)\", randomize=True)\n        slider_2 = gr.Slider(minimum=10, maximum=23.2, label=\"Random Slider (Input 2)\", randomize=True)\n        slider_3 = gr.Slider(value=3, label=\"Non random slider\")\n        btn = gr.Button(\"Run\")\n        btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Slider component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Slider.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Slider changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Slider.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Slider.  \n`Slider.release(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user releases the mouse on this Slider.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "one\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": " been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n  ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": " unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "Creates a numeric field for user to enter numbers as input or display\nnumeric output.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "**As input component** : Passes field value as a `float` or `int` into the\nfunction, depending on `precision`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: float | int | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects an `int` or `float` returned from the\nfunction and sets field value to it.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> float | int | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: float | Callable | None\n\ndefault `= None`\n\ndefault value. If None, the component will be empty and show the `placeholder`\nif is set. If no `placeholder` is set, the component will show 0. If a\nfunction is provided, the function will be called each time the app loads to\nset the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    placeholder: str | I18nData | None\n\ndefault `= None`\n\nplaceholder hint to provide behind number input.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applie", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be editable; if False, editing will be disabled. If not\nprovided, this is inferred based on whether the component is used as an input\nor output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "rameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    precision: int | None\n\ndefault `= None`\n\nPrecision to round input/output to. If set to 0, will round to nearest integer\nand convert type to int. If None, no rounding happens.\n\n\n    \n    \n    minimum: float | None\n\ndefault `= None`\n\nMinimum value. Only applied when component is used as an input. If a user\nprovides a smaller value, a gr.Error exception is raised by the backend.\n\n\n    \n    \n    maximum: float | None\n\ndefault `= None`\n\nMaximum value. Only applied when component is used as an input. If a user\nprovides a larger value, a gr.Error exception is raised by the backend.\n\n\n    \n    \n    step: float\n\ndefault `= 1`\n\nThe interval between allowed numbers in the component. Can be used along with\noptional parameters `minimum` and `maximum` to create a range of legal values\nstarting from `minimum` and incrementing according to this parameter.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Number` | \"number\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "tax_calculatorblocks_simple_squares\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def tax_calculator(income, marital_status,\nassets): tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\ntotal_deductible = sum(assets[\"Cost\"]) taxable_income = income -\ntotal_deductible total_tax = 0 for bracket, rate in tax_brackets: if\ntaxable_income > bracket: total_tax += (taxable_income - bracket) * rate / 100\nif marital_status == \"Married\": total_tax *= 0.75 elif marital_status ==\n\"Divorced\": total_tax *= 0.8 return round(total_tax) demo = gr.Interface(\ntax_calculator, [ \"number\", gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\ngr.Dataframe( headers=[\"Item\", \"Cost\"], datatype=[\"str\", \"number\"],\nlabel=\"Assets Purchased this Year\", ), ], \"number\", examples=[ [10000,\n\"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]], [80000,\n\"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]], ], ) demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def tax_calculator(income, marital_status, assets):\n        tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\n        total_deductible = sum(assets[\"Cost\"])\n        taxable_income = income - total_deductible\n    \n        total_tax = 0\n        for bracket, rate in tax_brackets:\n            if taxable_income > bracket:\n                total_tax += (taxable_income - bracket) * rate / 100\n    \n        if marital_status == \"Married\":\n            total_tax *= 0.75\n        elif marital_status == \"Divorced\":\n            total_tax *= 0.8\n    \n        return round(total_tax)\n    \n    demo = gr.Interface(\n        tax_calculator,\n        [\n            \"number\",\n            gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\n            gr.Dataframe(\n                headers=[\"Item\", \"Cost\"],\n                datatype=[\"str\", \"number\"],\n                label=\"Assets Purchased this Year\",\n            ),\n        ],\n        \"number\",\n        examples=[\n            [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n         ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "          label=\"Assets Purchased this Year\",\n            ),\n        ],\n        \"number\",\n        examples=[\n            [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n            [80000, \"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]],\n        ],\n    )\n    \n    demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr demo = gr.Blocks(css=\"\"\"btn {color: red} .abc\n{font-family: \"Comic Sans MS\", \"Comic Sans\", cursive !important}\"\"\") with\ndemo: default_json = {\"a\": \"a\"} num = gr.State(value=0) squared =\ngr.Number(value=0) btn = gr.Button(\"Next Square\", elem_id=\"btn\",\nelem_classes=[\"abc\", \"def\"]) stats = gr.State(value=default_json) table =\ngr.JSON() def increase(var, stats_history): var += 1 stats_history[str(var)] =\nvar**2 return var, var**2, stats_history, stats_history btn.click(increase,\n[num, stats], [num, squared, stats, table]) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    demo = gr.Blocks(css=\"\"\"btn {color: red} .abc {font-family: \"Comic Sans MS\", \"Comic Sans\", cursive !important}\"\"\")\n    \n    with demo:\n        default_json = {\"a\": \"a\"}\n    \n        num = gr.State(value=0)\n        squared = gr.Number(value=0)\n        btn = gr.Button(\"Next Square\", elem_id=\"btn\", elem_classes=[\"abc\", \"def\"])\n    \n        stats = gr.State(value=default_json)\n        table = gr.JSON()\n    \n        def increase(var, stats_history):\n            var += 1\n            stats_history[str(var)] = var**2\n            return var, var**2, stats_history, stats_history\n    \n        btn.click(increase, [num, stats], [num, squared, stats, table])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Number component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Number.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Number changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Number.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Number.  \n`Number.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the Number is focused.  \n`Number.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Number is focused.  \n`Number.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Number is unfocused/blurred.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefau", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": " inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enab", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "n on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "Group is a layout element within Blocks which groups together children so\nthat they do not have any padding or margin between them.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "with gr.Group():\n        gr.Textbox(label=\"First\")\n        gr.Textbox(label=\"Last\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, group will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "This class is a wrapper over the Dataset component and can be used to\ncreate Examples for Blocks / Interfaces. Populates the Dataset component with\nexamples and assigns event listener so that clicking on an example populates\nthe input/output components. Optionally handles example caching for fast\ninference.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    examples: list[Any] | list[list[Any]] | str\n\nexample inputs that can be clicked to populate specific components. Should be\nnested list, in which the outer list consists of samples and each inner list\nconsists of an input corresponding to each input component. A string path to a\ndirectory of examples can also be provided but it should be within the\ndirectory with the python file running the gradio app. If there are multiple\ninput components and a directory is provided, a log.csv file must be present\nin the directory to link corresponding inputs.\n\n\n    \n    \n    inputs: Component | list[Component]\n\nthe component or list of components corresponding to the examples\n\n\n    \n    \n    outputs: Component | list[Component] | None\n\ndefault `= None`\n\noptionally, provide the component or list of components corresponding to the\noutput of the examples. Required if `cache_examples` is not False.\n\n\n    \n    \n    fn: Callable | None\n\ndefault `= None`\n\noptionally, provide the function to run to generate the outputs corresponding\nto the examples. Required if `cache_examples` is not False. Also required if\n`run_on_click` is True.\n\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES\nenvironment variable, which should be either \"true\" or \"false\". In HuggingFace\nSpaces, this parameter is True (as long as `fn` and `outputs` are also\nprovided). The default option otherwise is False. Note that examples are\ncached separately from Gradio's queue() so certain features, such as\ngr.Progress(), gr.Info(), gr.Warning(), etc. will not be displayed in Gradio's\nUI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "d in Gradio's\nUI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare cached at app launch. If None, will use the GRADIO_CACHE_MODE environment\nvariable if defined, or default to \"eager\".\n\n\n    \n    \n    examples_per_page: int\n\ndefault `= 10`\n\nhow many examples to show per page.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= \"Examples\"`\n\nthe label to use for the examples component (by default, \"Examples\")\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM.\n\n\n    \n    \n    run_on_click: bool\n\ndefault `= False`\n\nif cache_examples is False, clicking on an example does not run the function\nwhen an example is clicked. Set this to True to run the function when an\nexample is clicked. Has no effect if cache_examples is True.\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nif True, preprocesses the example input before running the prediction function\nand caching the output. Only applies if `cache_examples` is not False.\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nif True, postprocesses the example output after running the prediction\nfunction and before caching. Only applies if `cache_examples` is not False.\n\n\n    \n    \n    show_api: bool\n\ndefault `= False`\n\nWhether to show the event associated with clicking on the examples in the\n\"view API\" page of the Gradio app, or in the \".view_api()\" method of the\nGradio clients.\n\n\n    \n    \n    api_name: str | Literal[False]\n\ndefault `= \"load_example\"`\n\nDefines how the event associated with clicking on the examples appears in the\nAPI docs. Can be a string or False. If set to a string, the endpoint will be\nexposed in the API docs with the given name. If False, the endpoint will not\nbe exposed in the API docs and downstream apps (including those that `gr.load`\nthis app) will not be able to use the example ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "posed in the API docs with the given name. If False, the endpoint will not\nbe exposed in the API docs and downstream apps (including those that `gr.load`\nthis app) will not be able to use the example function.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the event associated with clicking on the examples in the API\ndocs. Can be a string, None, or False. If set to a string, the endpoint will\nbe exposed in the API docs with the given description. If None, the function's\ndocstring will be used as the API endpoint description. If False, then no\ndescription will be displayed in the API docs.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. Used only if\ncache_examples is not False.\n\n\n    \n    \n    example_labels: list[str] | None\n\ndefault `= None`\n\nA list of labels for each example. If provided, the length of this list should\nbe the same as the number of examples, and these labels will be used in the UI\ninstead of rendering the example values.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, the examples component will be hidden in the UI.\n\n\n    \n    \n    preload: int | Literal[False]\n\ndefault `= False`\n\nIf an integer is provided (and examples are being cached), the example at that\nindex in the examples list will be preloaded when the Gradio app is loaded. If\nFalse, no example will be preloaded.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    dataset: gradio.Dataset\n\nThe `gr.Dataset` component corresponding to this Examples object.\n\n\n    \n    \n    load_input_event: gradio.events.Dependency\n\nThe Gradio event that populates the input values when the examples are\nclicked. You can attach a `.then()` or a `.success()` to this event to trigger\nsubsequent events to fire after this event.\n\n\n    \n    \n    cache_event: gradio.events.Dependency | None\n\nThe Gradio event that populates the cached output values when the examples are\nclicked. You can attach a `.then()` or a `.success()` to this event to trigger\nsubsequent events to fire after this event. This event is `None` if\n`cache_examples` if False, and is the same as `load_input_event` if\n`cache_examples` is `'lazy'`.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "**Updating Examples**\n\nIn this demo, we show how to update the examples by updating the samples of\nthe underlying dataset. Note that this only works if `cache_examples=False` as\nupdating the underlying dataset does not update the cache.\n\n    \n    \n    import gradio as gr\n    \n    def update_examples(country):\n        if country == \"USA\":\n            return gr.Dataset(samples=[[\"Chicago\"], [\"Little Rock\"], [\"San Francisco\"]])\n        else:\n            return gr.Dataset(samples=[[\"Islamabad\"], [\"Karachi\"], [\"Lahore\"]])\n    \n    with gr.Blocks() as demo:\n        dropdown = gr.Dropdown(label=\"Country\", choices=[\"USA\", \"Pakistan\"], value=\"USA\")\n        textbox = gr.Textbox()\n        examples = gr.Examples([[\"Chicago\"], [\"Little Rock\"], [\"San Francisco\"]], textbox)\n        dropdown.change(update_examples, dropdown, examples.dataset)\n        \n    demo.launch()\n\n", "heading1": "Examples", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "calculator_blocks\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def calculator(num1, operation, num2): if\noperation == \"add\": return num1 + num2 elif operation == \"subtract\": return\nnum1 - num2 elif operation == \"multiply\": return num1 * num2 elif operation ==\n\"divide\": return num1 / num2 with gr.Blocks() as demo: with gr.Row(): with\ngr.Column(): num_1 = gr.Number(value=4) operation = gr.Radio([\"add\",\n\"subtract\", \"multiply\", \"divide\"]) num_2 = gr.Number(value=0) submit_btn =\ngr.Button(value=\"Calculate\") with gr.Column(): result = gr.Number()\nsubmit_btn.click( calculator, inputs=[num_1, operation, num_2],\noutputs=[result], api_name=False ) examples = gr.Examples( examples=[ [5,\n\"add\", 3], [4, \"divide\", 2], [-4, \"multiply\", 2.5], [0, \"subtract\", 1.2], ],\ninputs=[num_1, operation, num_2], ) if __name__ == \"__main__\":\ndemo.launch(show_api=False)\n\n    \n    \n    import gradio as gr\n    \n    def calculator(num1, operation, num2):\n        if operation == \"add\":\n            return num1 + num2\n        elif operation == \"subtract\":\n            return num1 - num2\n        elif operation == \"multiply\":\n            return num1 * num2\n        elif operation == \"divide\":\n            return num1 / num2\n    \n    with gr.Blocks() as demo:\n        with gr.Row():\n            with gr.Column():\n                num_1 = gr.Number(value=4)\n                operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n                num_2 = gr.Number(value=0)\n                submit_btn = gr.Button(value=\"Calculate\")\n            with gr.Column():\n                result = gr.Number()\n    \n        submit_btn.click(\n            calculator, inputs=[num_1, operation, num_2], outputs=[result], api_name=False\n        )\n        examples = gr.Examples(\n            examples=[\n                [5, \"add\", 3],\n                [4, \"divide\", 2],\n                [-4, \"multiply\", 2.5],\n                [0, \"subtract\", 1.2],\n            ],\n            inputs=[num_1, operation, num_2],\n        )\n    \n    if __name__ == \"__mai", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "    [4, \"divide\", 2],\n                [-4, \"multiply\", 2.5],\n                [0, \"subtract\", 1.2],\n            ],\n            inputs=[num_1, operation, num_2],\n        )\n    \n    if __name__ == \"__main__\":\n        demo.launch(show_api=False)\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "Button that clears the value of a component or a list of components when\nclicked. It is instantiated with the list of components to clear.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "**As input component** : (Rarely used) the `str` corresponding to the\nbutton label when the button is clicked\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : string corresponding to the button label\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    components: None | list[Component] | Component\n\ndefault `= None`\n\n\n    \n    \n    value: str\n\ndefault `= \"Clear\"`\n\ndefault text for the button to display. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop']\n\ndefault `= \"secondary\"`\n\nsets the background and text color of the button. Use 'primary' for main call-\nto-action buttons, 'secondary' for a more subdued style, 'stop' for a stop\nbutton, 'huggingface' for a black background with white text, consistent with\nHugging Face's button styles.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | Path | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    link: str | None\n\ndefault `= None`\n\nURL to open when the button is clicked. If None, no link will be used.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nif False, the Button will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefa", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "one\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    api_name: str | None | Literal['False']\n\ndefault `= None`\n\n\n    \n    \n    show_api: bool\n\ndefault `= False`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "width parameter will be respected first.\n\n\n    \n    \n    api_name: str | None | Literal['False']\n\ndefault `= None`\n\n\n    \n    \n    show_api: bool\n\ndefault `= False`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ClearButton` | \"clearbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ClearButton component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ClearButton.add(fn, \u00b7\u00b7\u00b7)` | Adds a component or list of components to the list of components that will be cleared when the button is clicked.  \n`ClearButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Button is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    components: None | Component | list[Component]\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "The gr.LikeData class is a subclass of gr.EventData that specifically\ncarries information about the `.like()` event. When gr.LikeData is added as a\ntype hint to an argument of an event listener method, a gr.LikeData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "import gradio as gr\n    \n    def test(value, like_data: gr.LikeData):\n        return {\n            \"chatbot_value\": value,\n            \"liked_message\": like_data.value,\n            \"liked_index\": like_data.index,\n            \"liked_or_disliked_as_bool\": like_data.liked\n        }\n    \n    with gr.Blocks() as demo:\n        c = gr.Chatbot([(\"abc\", \"def\")])\n        t = gr.JSON()\n        c.like(test, c, t)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the liked/disliked item. Is a tuple if the component is two\ndimensional.\n\n\n    \n    \n    value: Any\n\nThe value of the liked/disliked item.\n\n\n    \n    \n    liked: bool\n\nTrue if the item was liked, False if disliked, or string value if any other\nfeedback.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "chatbot_core_components_simple\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import random Chatbot demo with multimodal\ninput (text, markdown, LaTeX, code blocks, image, audio, video, & model3d).\nPlus shows support for streaming text. color_map = { \"harmful\": \"crimson\",\n\"neutral\": \"gray\", \"beneficial\": \"green\", } def html_src(harm_level): return\nf\"\"\" <div style=\"display: flex; gap: 5px;\"> <div style=\"background-color:\n{color_map[harm_level]}; padding: 2px; border-radius: 5px;\"> {harm_level}\n</div> </div> \"\"\" def print_like_dislike(x: gr.LikeData): print(x.index,\nx.value, x.liked) def add_message(history, message): for x in\nmessage[\"files\"]: history.append(((x,), None)) if message[\"text\"] is not None:\nhistory.append((message[\"text\"], None)) return history,\ngr.MultimodalTextbox(value=None, interactive=False) def bot(history,\nresponse_type): if response_type == \"gallery\": history[-1][1] = gr.Gallery( [\n\"https://raw.githubusercontent.com/gradio-\napp/gradio/main/test/test_files/bus.png\",\n\"https://raw.githubusercontent.com/gradio-\napp/gradio/main/test/test_files/bus.png\", ] ) elif response_type == \"image\":\nhistory[-1][1] = gr.Image( \"https://raw.githubusercontent.com/gradio-\napp/gradio/main/test/test_files/bus.png\" ) elif response_type == \"video\":\nhistory[-1][1] = gr.Video( \"https://github.com/gradio-\napp/gradio/raw/main/demo/video_component/files/world.mp4\" ) elif response_type\n== \"audio\": history[-1][1] = gr.Audio( \"https://github.com/gradio-\napp/gradio/raw/main/test/test_files/audio_sample.wav\" ) elif response_type ==\n\"html\": history[-1][1] = gr.HTML( html_src(random.choice([\"harmful\",\n\"neutral\", \"beneficial\"])) ) elif response_type == \"model3d\": history[-1][1] =\ngr.Model3D( \"https://github.com/gradio-\napp/gradio/raw/main/test/test_files/Fox.gltf\" ) else: history[-1][1] = \"Cool!\"\nreturn history with gr.Blocks(fill_height=True) as demo: chatbot = gr.Chatbot(\nelem_id=\"chatbot\", bubble_full_width=False, scale=1, ) response_type =\ngr.Radio( [ \"image\", \"text\", \"gallery\", \"video\", \"aud", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "urn history with gr.Blocks(fill_height=True) as demo: chatbot = gr.Chatbot(\nelem_id=\"chatbot\", bubble_full_width=False, scale=1, ) response_type =\ngr.Radio( [ \"image\", \"text\", \"gallery\", \"video\", \"audio\", \"html\", \"model3d\",\n], value=\"text\", label=\"Response Type\", ) chat_input = gr.MultimodalTextbox(\ninteractive=True, placeholder=\"Enter message or upload file...\",\nshow_label=False, ) chat_msg = chat_input.submit( add_message, [chatbot,\nchat_input], [chatbot, chat_input] ) bot_msg = chat_msg.then( bot, [chatbot,\nresponse_type], chatbot, api_name=\"bot_response\" ) bot_msg.then(lambda:\ngr.MultimodalTextbox(interactive=True), None, [chat_input])\nchatbot.like(print_like_dislike, None, None) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    import random\n    \n    Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, video, & model3d). Plus shows support for streaming text.\n    \n    color_map = {\n        \"harmful\": \"crimson\",\n        \"neutral\": \"gray\",\n        \"beneficial\": \"green\",\n    }\n    \n    def html_src(harm_level):\n        return f\"\"\"\n    \n    \n    \n      \n    \n    \n      {harm_level}\n      \n    \n    \n    \n    \n    \n    \"\"\"\n    \n    def print_like_dislike(x: gr.LikeData):\n        print(x.index, x.value, x.liked)\n    \n    def add_message(history, message):\n        for x in message[\"files\"]:\n            history.append(((x,), None))\n        if message[\"text\"] is not None:\n            history.append((message[\"text\"], None))\n        return history, gr.MultimodalTextbox(value=None, interactive=False)\n    \n    def bot(history, response_type):\n        if response_type == \"gallery\":\n            history[-1][1] = gr.Gallery(\n                [\n                    \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\",\n                    \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\",\n                ]\n            )\n        elif response_type == \"image\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "/test/test_files/bus.png\",\n                    \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\",\n                ]\n            )\n        elif response_type == \"image\":\n            history[-1][1] = gr.Image(\n                \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\"\n            )\n        elif response_type == \"video\":\n            history[-1][1] = gr.Video(\n                \"https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4\"\n            )\n        elif response_type == \"audio\":\n            history[-1][1] = gr.Audio(\n                \"https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav\"\n            )\n        elif response_type == \"html\":\n            history[-1][1] = gr.HTML(\n                html_src(random.choice([\"harmful\", \"neutral\", \"beneficial\"]))\n            )\n        elif response_type == \"model3d\":\n            history[-1][1] = gr.Model3D(\n               \"https://github.com/gradio-app/gradio/raw/main/test/test_files/Fox.gltf\"\n            )\n        else:\n            history[-1][1] = \"Cool!\"\n        return history\n    \n    with gr.Blocks(fill_height=True) as demo:\n        chatbot = gr.Chatbot(\n            elem_id=\"chatbot\",\n            bubble_full_width=False,\n            scale=1,\n        )\n        response_type = gr.Radio(\n            [\n                \"image\",\n                \"text\",\n                \"gallery\",\n                \"video\",\n                \"audio\",\n                \"html\",\n                \"model3d\",\n            ],\n            value=\"text\",\n            label=\"Response Type\",\n        )\n    \n        chat_input = gr.MultimodalTextbox(\n            interactive=True,\n            placeholder=\"Enter message or upload file...\",\n            show_label=False,\n        )\n    \n        chat_msg = chat_input.submit(\n            add_message, [chatbot, chat_input], [chatbot, chat_input]\n        )\n        bot_msg = chat_msg.then(\n            bo", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "  show_label=False,\n        )\n    \n        chat_msg = chat_input.submit(\n            add_message, [chatbot, chat_input], [chatbot, chat_input]\n        )\n        bot_msg = chat_msg.then(\n            bot, [chatbot, response_type], chatbot, api_name=\"bot_response\"\n        )\n        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n    \n        chatbot.like(print_like_dislike, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "Creates a set of checkboxes. Can be used as an input to pass a set of\nvalues to a function or as an output to display values, a subset of which are\nselected.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "**As input component** : Passes the list of checked checkboxes as a `list[str | int | float]` or their indices as a `list[int]` into the function, depending on `type`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: list[str | int | float] | list[int | None]\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `list[str | int | float]` of values or a single `str | int | float` value, the checkboxes with these values are checked.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> list[str | int | float] | str | int | float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    choices: list[str | int | float | tuple[str, str | int | float]] | None\n\ndefault `= None`\n\nA list of string or numeric options to select from. An option can also be a\ntuple of the form (name, value), where name is the displayed name of the\ncheckbox button and value is the value to be passed to the function, or\nreturned by the function.\n\n\n    \n    \n    value: list[str | float | int] | str | float | int | Callable | None\n\ndefault `= None`\n\nDefault selected list of options. If a single choice is selected, it can be\npassed in as a string or numeric type. If a function is provided, the function\nwill be called each time the app loads to set the initial value of this\ncomponent.\n\n\n    \n    \n    type: Literal['value', 'index']\n\ndefault `= \"value\"`\n\nType of value to be returned by component. \"value\" returns the list of strings\nof the choices selected, \"index\" returns the list of indices of the choices\nselected.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n   ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "nt] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nIf True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nRelative width compared to adjacent Components in a Row. For example, if\nComponent A has scale=2, and Component B has scale=1, A will be twice as wide\nas B. Should be an integer.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nMinimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, choices in this checkbox group will be checkable; if False, checking\nwill be disabled. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-render", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "ssign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.CheckboxGroup` | \"checkboxgroup\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "sentence_builder\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "  gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe CheckboxGroup component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`CheckboxGroup.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the CheckboxGroup changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`CheckboxGroup.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the CheckboxGroup.  \n`CheckboxGroup.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the CheckboxGroup. Uses event data gradio.SelectData to carry `value` referring to the label of the CheckboxGroup, and `selected` to refer to state of the CheckboxGroup. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | Bl", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "ne\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will p", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "onent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions w", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "ed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.rende", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "Sets up an API or MCP endpoint for a generic function without needing\ndefine events listeners or components. Derives its typing from type hints in\nthe provided function's signature rather than the components.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/api", "source_page_title": "Gradio - Api Docs"}, {"text": "import gradio as gr\n    with gr.Blocks() as demo:\n        with gr.Row():\n            input = gr.Textbox()\n            button = gr.Button(\"Submit\")\n        output = gr.Textbox()\n        def fn(a: int, b: int, c: list[str]) -> tuple[int, str]:\n            return a + b, c[a:b]\n        gr.api(fn, api_name=\"add_and_slice\")\n    _, url, _ = demo.launch()\n    \n    from gradio_client import Client\n    client = Client(url)\n    result = client.predict(\n            a=3,\n            b=5,\n            c=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n            api_name=\"/add_and_slice\"\n    )\n    print(result)\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/api", "source_page_title": "Gradio - Api Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    fn: Callable | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. The function should be fully typed, and the type\nhints will be used to derive the typing information for the API/MCP endpoint.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\nDefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If False, the endpoint will not be exposed in the api docs. If set to\nNone, will use the functions name as the endpoint route. If set to a string,\nthe endpoint will be exposed in the api docs with the given name.\n\n\n    \n    \n    api_description: str | None\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nI", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/api", "source_page_title": "Gradio - Api Docs"}, {"text": "\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\nThe time limit for the function to run. Parameter only used for the\n`.stream()` event.\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for the `.stream()` event.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/api", "source_page_title": "Gradio - Api Docs"}, {"text": "Sets up an event listener that triggers a function when the specified\nevent(s) occur. This is especially useful when the same function should be\ntriggered by multiple events. Only a single API endpoint is generated for all\nevents in the triggers list.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "import gradio as gr\n    \n    with gr.Blocks() as demo:\n        with gr.Row():\n            input = gr.Textbox()\n            button = gr.Button(\"Submit\")\n        output = gr.Textbox()\n        gr.on(\n            triggers=[button.click, input.submit],\n            fn=lambda x: x,\n            inputs=[input],\n            outputs=[output]\n        )\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    triggers: list[EventListenerCallable] | EventListenerCallable | None\n\ndefault `= None`\n\nList of triggers to listen to, e.g. [btn.click, number.change]. If None, will\nrun on app load and changes to any inputs.\n\n\n    \n    \n    fn: Callable[..., Any] | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\nDefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If False, the endpoint will not be exposed in the api docs. If set to\nNone, will use the functions name as the endpoint route. If set to a string,\nthe endpoint will be exposed in the api docs with the given name.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\n\n    \n    \n    scroll_to_output: bool\n\nde", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "to a string,\nthe endpoint will be exposed in the api docs with the given name.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all,\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run pos", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "onent data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs', return should be a list of values for output\ncomponents.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowes", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\nThe time limit for the function to run. Parameter only used for the\n`.stream()` event.\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for the `.stream()` event.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "Load a chat interface from an OpenAI API chat compatible endpoint.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/load_chat", "source_page_title": "Gradio - Load_Chat Docs"}, {"text": "import gradio as gr\n    demo = gr.load_chat(\"http://localhost:11434/v1\", model=\"deepseek-r1\")\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/load_chat", "source_page_title": "Gradio - Load_Chat Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    base_url: str\n\nThe base URL of the endpoint, e.g. \"http://localhost:11434/v1/\"\n\n\n    \n    \n    model: str\n\nThe name of the model you are loading, e.g. \"llama3.2\"\n\n\n    \n    \n    token: str | None\n\ndefault `= None`\n\nThe API token or a placeholder string if you are using a local model, e.g.\n\"ollama\"\n\n\n    \n    \n    file_types: Literal['text_encoded', 'image'] | list[Literal['text_encoded', 'image']] | None\n\ndefault `= \"text_encoded\"`\n\nThe file types allowed to be uploaded by the user. \"text_encoded\" allows\nuploading any text-encoded file (which is simply appended to the prompt), and\n\"image\" adds image upload support. Set to None to disable file uploads.\n\n\n    \n    \n    system_message: str | None\n\ndefault `= None`\n\nThe system message to use for the conversation, if any.\n\n\n    \n    \n    streaming: bool\n\ndefault `= True`\n\nWhether the response should be streamed.\n\n\n    \n    \n    kwargs: <class 'inspect._empty'>\n\nAdditional keyword arguments to pass into ChatInterface for customization.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/load_chat", "source_page_title": "Gradio - Load_Chat Docs"}, {"text": "Interface is Gradio's main high-level class, and allows you to create a\nweb-based GUI / demo around a machine learning model (or any Python function)\nin a few lines of code. You must specify three parameters: (1) the function to\ncreate a GUI for (2) the desired input components and (3) the desired output\ncomponents. Additional parameters can be used to control the appearance and\nbehavior of the demo.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "import gradio as gr\n    \n    def image_classifier(inp):\n        return {'cat': 0.3, 'dog': 0.7}\n    \n    demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\")\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    fn: Callable\n\nthe function to wrap an interface around. Often a machine learning model's\nprediction function. Each parameter of the function corresponds to one input\ncomponent, and the function should return a single value or a tuple of values,\nwith each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of input components should match the number of parameters in fn. If\nset to None, then only the output components will be displayed.\n\n\n    \n    \n    outputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of output components should match the number of values returned by\nfn. If set to None, then only the input components will be displayed.\n\n\n    \n    \n    examples: list[Any] | list[list[Any]] | str | None\n\ndefault `= None`\n\nsample inputs for the function; if provided, appear below the UI components\nand can be clicked to populate the interface. Should be nested list, in which\nthe outer list consists of samples and each inner list consists of an input\ncorresponding to each input component. A string path to a directory of\nexamples can also be provided, but it should be within the directory with the\npython file running the gradio app. If there are multiple input components and\na directory is provided, a log.csv file must be present in the directory to\nlink corresponding inputs.\n\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES\nenvironment variable, which should be either \"true\" or \"false\". In HuggingFace\nSpaces, this parameter defaults to True (as long as `fn` and `outputs` are\nalso provided). Note that examples are cached separately from Gradio's queue()\nso certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will\nnot be displayed in Gradio's UI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare cached at app launch. If None, will use the GRADIO_CACHE_MODE environment\nvariable if defined, or default to \"eager\". In HuggingFace Spaces, this\nparameter defaults to \"eager\" except for ZeroGPU Spaces, in which case it\ndefaults to \"lazy\".\n\n\n    \n    \n    examples_per_page: int\n\ndefault `= 10`\n\nif examples are provided, how many to display per page.\n\n\n    \n    \n    example_labels: list[str] | None\n\ndefault `= None`\n\na list of labels for each example. If provided, the length of this list should\nbe the same as the number of examples, and these labels will be used in the UI\ninstead of rendering the example values.\n\n\n    \n    \n    preload_example: int | Literal[False]\n\ndefault `= False`\n\nIf an integer is provided (and examples are being cached), the example at that\nindex in the examples list will be preloaded when the Gradio app is first\nloaded. If False, no example will be preloaded.\n\n\n    \n    \n    live: bool\n\ndefault `= False`\n\nwhether the interface should automatically rerun if any of the inputs change.\n\n\n    \n    \n    title: str | I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n    \n    \n    description: str | None\n\ndefault `= None`\n\na description for the interface; if provided, appears above the input and\noutput components and beneath the title in regular font. Accepts Markdown and\nHTML content.\n\n\n    \n    \n    article: str | None\n\ndefault `= None`\n\nan expanded article explaining the interface; if provided, appears below the\ninput and output components in regular font. Accepts Markdown and HTML\ncontent. If it is an HTTP(S) link to a downloadable remote file, the content\nof this file is displayed.\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\na Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    flagging_mode: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\none of \"never\", \"auto\", or \"manual\". If \"never\" or \"auto\", users will not see\na button to flag an input and output. If \"manual\", users will see a button to\nflag. If \"auto\", every input the user submits will be automatically flagged,\nalong with the generated output. If \"manual\", both the input and outputs are\nflagged when the user clicks flag button. This parameter can be set with\nenvironmental variable GRADIO_FLAGGING_MODE; otherwise defaults to \"manual\".\n\n\n    \n    \n    flagging_options: list[str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nif provided, allows user to select from the list of options when flagging.\nOnly applies if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "es if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored in the flagging CSV; or\nit can be a list of strings [\"X\", \"Y\"], in which case the values will be the\nlist of strings and the labels will [\"Flag as X\", \"Flag as Y\"], etc.\n\n\n    \n    \n    flagging_dir: str\n\ndefault `= \".gradio/flagged\"`\n\npath to the the directory where flagged data is stored. If the directory does\nnot exist, it will be created.\n\n\n    \n    \n    flagging_callback: FlaggingCallback | None\n\ndefault `= None`\n\neither None or an instance of a subclass of FlaggingCallback which will be\ncalled when a sample is flagged. If set to None, an instance of\ngradio.flagging.CSVLogger will be created and logs will be saved to a local\nCSV file in flagging_dir. Default to None.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nif True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nthe maximum number of inputs to batch together if this is called from the\nqueue (only relevant if batch=True)\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show the prediction endpoint in the \"view API\" page of the Gradio\napp, or in the \".view_api()\" method of the Gradio clients. Unlike setting\napi_name to False, setting show_api to False will still allow downstream apps\nas well as the Clients to use this event.\n\n\n    \n    \n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " in the \".view_api()\" method of the Gradio clients. Unlike setting\napi_name to False, setting show_api to False will still allow downstream apps\nas well as the Clients to use this event.\n\n\n    \n    \n    api_name: str | Literal[False] | None\n\ndefault `= \"predict\"`\n\ndefines how the prediction endpoint appears in the API docs. Can be a string,\nNone, or False. If set to a string, the endpoint will be exposed in the API\ndocs with the given name. If None, the name of the prediction function will be\nused as the API endpoint. If False, the endpoint will not be exposed in the\nAPI docs and downstream apps (including those that `gr.load` this app) will\nnot be able to use this prediction endpoint.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    allow_duplication: bool\n\ndefault `= False`\n\nif True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nif set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`.queue()`, which itself is 1 by default).\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n    \n    \n    css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo web", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a code string. The custom js should be in the form of a single js\nfunction. This function will automatically be executed when the page loads.\nFor more flexibility, use the head parameter to insert js inside <script>\ntags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html code to insert into the head of the demo webpage. This can be used\nto add custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n\n    \n    \n    head_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` parameter is also set, the html from `head` will\nbe included first.\n\n\n    \n    \n    additional_inputs: str | Component | list[str | Component] | None\n\ndefault `= None`\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThese components will be rendered in an accordion below the main input\ncomponents. By default, no additional input components will be displayed.\n\n\n    \n    \n    additional_inputs_accordion: str | Accordion | None\n\ndefault `= None`\n\nif a string is provided, this is the label of the `gr.Accordion` to use to\ncontain additional inputs. A `gr.Accordion` object can be provided as well to\nconfigure other properties of the container holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    submit_btn: ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ntainer holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    submit_btn: str | Button\n\ndefault `= \"Submit\"`\n\nthe button to use for submitting inputs. Defaults to a `gr.Button(\"Submit\",\nvariant=\"primary\")`. This parameter does not apply if the Interface is output-\nonly, in which case the submit button always displays \"Generate\". Can be set\nto a string (which becomes the button label) or a `gr.Button` object (which\nallows for more customization).\n\n\n    \n    \n    stop_btn: str | Button\n\ndefault `= \"Stop\"`\n\nthe button to use for stopping the interface. Defaults to a `gr.Button(\"Stop\",\nvariant=\"stop\", visible=False)`. Can be set to a string (which becomes the\nbutton label) or a `gr.Button` object (which allows for more customization).\n\n\n    \n    \n    clear_btn: str | Button | None\n\ndefault `= \"Clear\"`\n\nthe button to use for clearing the inputs. Defaults to a `gr.Button(\"Clear\",\nvariant=\"secondary\")`. Can be set to a string (which becomes the button label)\nor a `gr.Button` object (which allows for more customization). Can be set to\nNone, which hides the button.\n\n\n    \n    \n    delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\na tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "gress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    fill_width: bool\n\ndefault `= False`\n\nwhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width.\n\n\n    \n    \n    allow_flagging: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= 30`\n\nThe time limit for the stream to run. Default is 30 seconds. Parameter only\nused for streaming images or audio if the interface is live and the input\ncomponents are set to \"streaming=True\".\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for streaming images or audio if\nthe interface is live and the input components are set to \"streaming=True\".\n\n\n    \n    \n    deep_link: str | DeepLinkButton | bool | None\n\ndefault `= None`\n\na string or `gr.DeepLinkButton` object that creates a unique URL you can use\nto share your app and all components **as they currently are** with others.\nAutomatically enabled on Hugging Face Spaces unless explicitly set to False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "hello_worldhello_world_2hello_world_3\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name): return \"Hello \" + name + \"!\"\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\") if __name__\n== \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    \n    def greet(name):\n        return \"Hello \" + name + \"!\"\n    \n    \n    demo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, intensity): return \"Hello, \" +\nname + \"!\" * intensity demo = gr.Interface( fn=greet, inputs=[\"text\",\ngr.Slider(value=2, minimum=1, maximum=10, step=1)],\noutputs=[gr.Textbox(label=\"greeting\", lines=3)], ) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, intensity):\n        return \"Hello, \" + name + \"!\" * intensity\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", gr.Slider(value=2, minimum=1, maximum=10, step=1)],\n        outputs=[gr.Textbox(label=\"greeting\", lines=3)],\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, is_morning, temperature):\nsalutation = \"Good morning\" if is_morning else \"Good evening\" greeting =\nf\"{salutation} {name}. It is {temperature} degrees today\" celsius =\n(temperature - 32) * 5 / 9 return greeting, round(celsius, 2) demo =\ngr.Interface( fn=greet, inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\noutputs=[\"text\", \"number\"], ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, is_morning, temperature):\n        salutation = \"Good morning\" if is_morning else \"Good evening\"\n        greeting = f\"{salutation} {name}. It is {temperature} degrees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 10", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "grees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n        outputs=[\"text\", \"number\"],\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.launch(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nLaunches a simple web server that serves the demo. Can also be used to create\na public link used by anyone to access the demo from their browser by setting\nshare=True.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    def reverse(text):\n        return text[::-1]\n    demo = gr.Interface(reverse, \"text\", \"text\")\n    demo.launch(share=True, auth=(\"username\", \"password\"))\n\nParameters \u25bc\n\n\n    \n    \n    inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the de", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "pp inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the default\nbrowser.\n\n\n    \n    \n    share: bool | None\n\ndefault `= None`\n\nwhether to create a publicly shareable link for the gradio app. Creates an SSH\ntunnel to make your UI accessible from anywhere. If not provided, it is set to\nFalse by default every time, except when running in Google Colab. When\nlocalhost is not accessible (e.g. Google Colab), setting share=False is not\nsupported. Can be set by environment variable GRADIO_SHARE=True.\n\n\n    \n    \n    debug: bool\n\ndefault `= False`\n\nif True, blocks the main thread from running. If running in Google Colab, this\nis needed to print the errors in the cell output.\n\n\n    \n    \n    max_threads: int\n\ndefault `= 40`\n\nthe maximum number of total threads that the Gradio app can generate in\nparallel. The default is inherited from the starlette library (currently 40).\n\n\n    \n    \n    auth: Callable[[str, str], bool] | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access app. Can also provide function that takes username and\npassword and returns True if valid login.\n\n\n    \n    \n    auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page.\n\n\n    \n    \n    prevent_thread_lock: bool\n\ndefault `= False`\n\nBy default, the gradio app blocks the main thread while the server is running.\nIf set to True, the gradio app will not block and the gradio server will\nterminate as soon as the script finishes.\n\n\n    \n    \n    show_error: bool\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= No", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= None`\n\nto make app accessible on local network, set this to \"0.0.0.0\". Can be set by\nenvironment variable GRADIO_SERVER_NAME. If None, will use \"127.0.0.1\".\n\n\n    \n    \n    server_port: int | None\n\ndefault `= None`\n\nwill start gradio app on this port (if available). Can be set by environment\nvariable GRADIO_SERVER_PORT. If None, will search for an available port\nstarting at 7860.\n\n\n    \n    \n    height: int\n\ndefault `= 500`\n\nThe height in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    width: int | str\n\ndefault `= \"100%\"`\n\nThe width in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    favicon_path: str | Path | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for the web page.\n\n\n    \n    \n    ssl_keyfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the private key file to\ncreate a local server running on https.\n\n\n    \n    \n    ssl_certfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the signed certificate for\nhttps. Needs to be provided if ssl_keyfile is provided.\n\n\n    \n    \n    ssl_keyfile_password: str | None\n\ndefault `= None`\n\nIf a password is provided, will use this with the ssl certificate for https.\n\n\n    \n    \n    ssl_verify: bool\n\ndefault `= True`\n\nIf False, skips certificate validation which allows self-signed certificates\nto be used.\n\n\n    \n    \n    quiet: bool\n\ndefault `= False`\n\nIf True, suppresses most print statements.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent di", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": ": bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is allowed to\nserve. Must be absolute paths. Warning: if you provide directories, any files\nin these directories or their subdirectories are accessible to all users of\nyour app. Can be set by comma separated environment variable\nGRADIO_ALLOWED_PATHS. These files are generally assumed to be secure and will\nbe displayed in the browser when possible.\n\n\n    \n    \n    blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is not allowed to\nserve (i.e. users of your app are not allowed to access). Must be absolute\npaths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default. Can be set by comma separated\nenvironment variable GRADIO_BLOCKED_PATHS.\n\n\n    \n    \n    root_path: str | None\n\ndefault `= None`\n\nThe root path (or \"mount point\") of the application, if it's not served from\nthe root (\"/\") of the domain. Often used when the application is behind a\nreverse proxy that forwards requests to the application. For example, if the\napplication is served at \"https://example.com/myapp\", the `root_path` should\nbe set to \"/myapp\". A full URL beginning with http:// or https:// can be\nprovided, which will be used as the root path in its entirety. Can be set by\nenvironment variable GRADIO_ROOT_PATH. Defaults to \"\".\n\n\n    \n    \n    app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capac", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capacity to reduce memory usage when using gradio.State or returning\nupdated components from functions. Defaults to 10000.\n\n\n    \n    \n    share_server_address: str | None\n\ndefault `= None`\n\nUse this to specify a custom FRP server and port for sharing Gradio apps (only\napplies if share=True). If not provided, will use the default FRP server at\nhttps://gradio.live. See https://github.com/huggingface/frp for more\ninformation.\n\n\n    \n    \n    share_server_protocol: Literal['http', 'https'] | None\n\ndefault `= None`\n\nUse this to specify the protocol to use for the share links. Defaults to\n\"https\", unless a custom share_server_address is provided, in which case it\ndefaults to \"http\". If you are using a custom share_server_address and want to\nuse https, you must set this to \"https\".\n\n\n    \n    \n    share_server_tls_certificate: str | None\n\ndefault `= None`\n\nThe path to a TLS certificate file to use when connecting to a custom share\nserver. This parameter is not used with the default FRP server at\nhttps://gradio.live. Otherwise, you must provide a valid TLS certificate file\n(e.g. a \"cert.pem\") relative to the current working directory, or the\nconnection will not use TLS encryption, which is insecure.\n\n\n    \n    \n    auth_dependency: Callable[[fastapi.Request], str | None] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the app (they will see a 401 Unauthorized response). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any ", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ed with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    enable_monitoring: bool | None\n\ndefault `= None`\n\nEnables traffic monitoring of the app through the /monitoring endpoint. By\ndefault is None, which enables this endpoint. If explicitly True, will also\nprint the monitoring URL to the console. If False, will disable monitoring\naltogether.\n\n\n    \n    \n    strict_cors: bool\n\ndefault `= True`\n\nIf True, prevents external domains from making requests to a Gradio server\nrunning on localhost. If False, allows requests to localhost that originate\nfrom localhost but also, crucially, from \"null\". This parameter should\nnormally be True to prevent CSRF attacks but may need to be False when\nembedding a *locally-running Gradio app* using web components.\n\n\n    \n    \n    node_server_name: str | None\n\ndefault `= None`\n\n\n    \n    \n    node_port: int | None\n\ndefault `= None`\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 20+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n    \n    \n    pwa: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an installable PWA (Progressive Web\nApp). If set to None (default behavior), then the PWA feature will be enabled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_M", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "   mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_MCP_SERVER environment variable will be used to determine if the MCP\nserver should be enabled (which is \"True\" on Hugging Face Spaces).\n\n\n    \n    \n    i18n: I18n | None\n\ndefault `= None`\n\nAn I18n instance containing custom translations, which are used to translate\nstrings in our components (e.g. the labels of components or Markdown strings).\nThis feature can only be used to translate static text in the frontend, not\nvalues in the backend.\n\n", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.load(block, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%2", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Interface initially loads in the browser.\n\nParameters \u25bc\n\n\n    \n    \n    block: Block | None\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple correspondin", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime ", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "w the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click met", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "f other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api w", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ew_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.from_pipeline(pipeline, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nClass method that constructs an Interface from a Hugging Face\ntransformers.Pipeline or diffusers.DiffusionPipeline object. The input and\noutput components are automatically determined from the pipeline.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Co", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    from transformers import pipeline\n    pipe = pipeline(\"image-classification\")\n    gr.Interface.from_pipeline(pipe).launch()\n\nParameters \u25bc\n\n\n    \n    \n    pipeline: Pipeline | DiffusionPipeline\n\nthe pipeline object to use.\n\n", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ne object to use.\n\n", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.integrate(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20I", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nA catch-all method for integrating with other libraries. This method should be\nrun after launch()\n\nParameters \u25bc\n\n\n    \n    \n    comet_ml: <class 'inspect._empty'>\n\ndefault `= None`\n\nIf a comet_ml Experiment object is provided, will integrate with the\nexperiment and appear on Comet dashboard\n\n\n    \n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "\n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n\nIf the mlflow module is provided, will integrate with the experiment and\nappear on ML Flow dashboard\n\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.queue(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nBy enabling the queue you can control when users know their position in the\nqueue, and set a limit on maximum number of events allowed.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())\n    demo.queue(max_size=20)\n    demo.launch()\n\nParameters \u25bc\n\n\n    \n    \n    status_update_rate: float | Literal['auto']\n\ndefault `= \"auto\"`\n\nIf \"auto\", Queue will send status estimations to all clients whenever a job is\nfinished. Otherwise Queue will send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowin", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowing requests made\ndirectly to those endpoints to skip the queue.\n\n\n    \n    \n    max_size: int | None\n\ndefault `= None`\n\nThe maximum number of events the queue will store at any given moment. If the\nqueue is full, new events will not be added and a user will receive a message\nsaying that the queue is full. If None, the queue size will be unlimited.\n\n\n    \n    \n    default_concurrency_limit: int | None | Literal['not_set']\n\ndefault `= \"not_set\"`\n\nThe default value of `concurrency_limit` to use for event listeners that don't\nspecify a value. Can be set by environment variable\nGRADIO_DEFAULT_CONCURRENCY_LIMIT. Defaults to 1 if not set otherwise.\n\n", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "Creates a Dialogue component for displaying or collecting multi-speaker\nconversations. This component can be used as input to allow users to enter\ndialogue involving multiple speakers, or as output to display diarized speech,\nsuch as the result of a transcription or speaker identification model. Each\nmessage can be associated with a specific speaker, making it suitable for use\ncases like conversations, interviews, or meetings.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "**As input component** : Returns the dialogue as a string.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: tuple[str, list[tuple[str, str]]] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a string or a list of dictionaries of\ndialogue lines, where each dictionary contains 'speaker' and 'text' keys, or a\nstring.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> tuple[np.ndarray | PIL.Image.Image | str, list[tuple[np.ndarray | tuple[int, int, int, int], str]]] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: list[dict[str, str]] | Callable | None\n\ndefault `= None`\n\nValue of the dialogue. It is a list of dictionaries, each containing a\n'speaker' key and a 'text' key. If a function is provided, the function will\nbe called each time the app loads to set the initial value of this component.\n\n\n    \n    \n    speakers: list[str] | None\n\ndefault `= None`\n\nThe different speakers allowed in the dialogue. If `None` or an empty list, no\nspeakers will be displayed. Instead, the component will be a standard textarea\nthat optionally supports `tags` autocompletion.\n\n\n    \n    \n    formatter: Callable | None\n\ndefault `= None`\n\nA function that formats the dialogue line dictionary, e.g. {\"speaker\":\n\"Speaker 1\", \"text\": \"Hello, how are you?\"} into a string, e.g. \"Speaker 1:\nHello, how are you?\". This function is run on user input and the resulting\nstring is passed into the prediction function.\n\n\n    \n    \n    unformatter: Callable | None\n\ndefault `= None`\n\nA function that parses a formatted dialogue string back into a dialogue line\ndictionary. Should take a single string line and return a dictionary with\n'speaker' and 'text' keys. If not provided, the default unformatter will\nattempt to parse the default formatter pattern.\n\n\n    \n    \n    tags: list[str] | None\n\ndefault `= None`\n\nThe different tags allowed in the dialogue. Tags are displayed in an\nautocomplete menu below the input textbox when the user starts typing `:`. Use\nthe exact tag name expected by the AI model or inference function.\n\n\n    \n    \n    separator: str\n\ndefault `= \" \"`\n\nThe separator between the different dialogue lines used to join the formatted\ndialogue lines into a single string. For example, a newline character or empty\nstring.\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nA dictionary mapping speaker names to colors. The colors may be specified as\nhex codes or by their names. For example: {\"Speaker 1\": \"red\", \"Speaker 2\":\n\"FFEE22\"}. If not provided, default ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "`= None`\n\nA dictionary mapping speaker names to colors. The colors may be specified as\nhex codes or by their names. For example: {\"Speaker 1\": \"red\", \"Speaker 2\":\n\"FFEE22\"}. If not provided, default colors will be assigned to speakers. This\nis only used if `interactive` is False.\n\n\n    \n    \n    label: str | None\n\ndefault `= \"Dialogue\"`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | None\n\ndefault `= \"Type colon (:) in the dialogue line to see the available tags\"`\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\nplaceholder hint to provide behind textarea.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display the label. If False, the copy button is hidden as well\nas well as the label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible:", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "e, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= False`\n\nIf True, will focus on the textbox when the page loads. Use this carefully, as\nit can cause usability issues for sighted and non-sighted users.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | None\n\ndefault `= None`\n\nif assigned, will be used to assume identity across a re-render. Components\nthat have the same key across a re-render will have their value preserved.\n\n\n    \n    \n    max_lines: int | None\n\ndefault `= None`\n\nmaximum number of lines allowed in the dialogue.\n\n\n    \n    \n    show_submit_button: bool\n\ndefault `= True`\n\nIf True, includes a submit button to submit the dialogue.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= True`\n\nIf True, includes a copy button to copy the text in the textbox. Only applies\nif show_label is True.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "ncludes a copy button to copy the text in the textbox. Only applies\nif show_label is True.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Dialogue` | \"dialogue\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "dia_dialogue_demo\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import httpx tags = [ \"(laughs)\", \"(clears throat)\", \"(sighs)\", \"(gasps)\", \"(coughs)\", \"(singing)\", \"(sings)\", \"(mumbles)\", \"(beep)\", \"(groans)\", \"(sniffs)\", \"(claps)\", \"(screams)\", \"(inhales)\", \"(exhales)\", \"(applause)\", \"(burps)\", \"(humming)\", \"(sneezes)\", \"(chuckle)\", \"(whistles)\", ] speakers = [\"Speaker 1\", \"Speaker 2\"] client = httpx.AsyncClient(timeout=180) API_URL = \"https://router.huggingface.co/fal-ai/fal-ai/dia-tts\" async def query(dialogue: str, token: gr.OAuthToken | None): if token is None: raise gr.Error( \"No token provided. Use Sign in with Hugging Face to get a token.\" ) headers = { \"Authorization\": f\"Bearer {token.token}\", } response = await client.post(API_URL, headers=headers, json={\"text\": dialogue}) url = response.json()[\"audio\"][\"url\"] print(\"URL: \", url) return url def formatter(speaker, text): speaker = speaker.split(\" \")[1] return f\"[S{speaker}] {text}\" with gr.Blocks() as demo: with gr.Sidebar(): login_button = gr.LoginButton() gr.HTML( \"\"\" <h1 style='text-align: center; display: flex; align-items: center; justify-content: center;'> <img src=\"https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/dancing_huggy.gif\" alt=\"Dancing Huggy\" style=\"height: 100px; margin-right: 10px\"> Dia Dialogue Generation Model </h1> <h2 style='text-align: center; display: flex; align-items: center; justify-content: center;'>Model by &nbsp;<a href=\"https://huggingface.co/nari-labs/Dia-1.6B\"> Nari Labs</a>. Powered by HF and &nbsp; <a href=\"https://fal.ai/\">Fal AI</a>&nbsp; API.</h2> <h4>Dia is a dialogue generation model that can generate realistic dialogue between two speakers. Use the dialogue component to create a conversation and then hit the submit button in the bottom right corner to see it come to life .</h4> \"\"\" ) with gr.Row(): with gr.Column(): dialogue = gr.Dialogue( speakers=speakers, tags=tags, formatter=formatter ) with gr.Column(): with gr.Row(): audio = gr.Audio(label=\"Audio\") with gr", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": " life .</h4> \"\"\" ) with gr.Row(): with gr.Column(): dialogue = gr.Dialogue( speakers=speakers, tags=tags, formatter=formatter ) with gr.Column(): with gr.Row(): audio = gr.Audio(label=\"Audio\") with gr.Row(): gr.DeepLinkButton(value=\"Share Audio via Link\") with gr.Row(): gr.Examples( examples=[ [ [ { \"speaker\": \"Speaker 1\", \"text\": \"Why did the chicken cross the road?\", }, {\"speaker\": \"Speaker 2\", \"text\": \"I don't know!\"}, { \"speaker\": \"Speaker 1\", \"text\": \"to get to the other side! (laughs)\", }, ] ], [ [ { \"speaker\": \"Speaker 1\", \"text\": \"I am a little tired today (sighs).\", }, {\"speaker\": \"Speaker 2\", \"text\": \"Hang in there!\"}, ] ], ], inputs=[dialogue], cache_examples=False, ) dialogue.submit(query, [dialogue], audio) if __name__ == \"__main__\": demo.launch() \n    \n    \n    import gradio as gr\n    import httpx\n    \n    \n    tags = [\n        \"(laughs)\",\n        \"(clears throat)\",\n        \"(sighs)\",\n        \"(gasps)\",\n        \"(coughs)\",\n        \"(singing)\",\n        \"(sings)\",\n        \"(mumbles)\",\n        \"(beep)\",\n        \"(groans)\",\n        \"(sniffs)\",\n        \"(claps)\",\n        \"(screams)\",\n        \"(inhales)\",\n        \"(exhales)\",\n        \"(applause)\",\n        \"(burps)\",\n        \"(humming)\",\n        \"(sneezes)\",\n        \"(chuckle)\",\n        \"(whistles)\",\n    ]\n    speakers = [\"Speaker 1\", \"Speaker 2\"]\n    \n    client = httpx.AsyncClient(timeout=180)\n    API_URL = \"https://router.huggingface.co/fal-ai/fal-ai/dia-tts\"\n    \n    \n    async def query(dialogue: str, token: gr.OAuthToken | None):\n        if token is None:\n            raise gr.Error(\n                \"No token provided. Use Sign in with Hugging Face to get a token.\"\n            )\n        headers = {\n            \"Authorization\": f\"Bearer {token.token}\",\n        }\n        response = await client.post(API_URL, headers=headers, json={\"text\": dialogue})\n        url = response.json()[\"audio\"][\"url\"]\n        print(\"URL: \", url)\n        return url\n    \n    \n    def formatter(speaker, text):\n        speaker = spea", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "eaders=headers, json={\"text\": dialogue})\n        url = response.json()[\"audio\"][\"url\"]\n        print(\"URL: \", url)\n        return url\n    \n    \n    def formatter(speaker, text):\n        speaker = speaker.split(\" \")[1]\n        return f\"[S{speaker}] {text}\"\n    \n    \n    with gr.Blocks() as demo:\n        with gr.Sidebar():\n            login_button = gr.LoginButton()\n        gr.HTML(\n            \"\"\"\n            \n    \n    \n            ![Dancing Huggy](https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/dancing_huggy.gif) Dia Dialogue Generation Model\n            \n    \n    \n            \n    \n    Model by \u00a0[ Nari Labs](https://huggingface.co/nari-labs/Dia-1.6B). Powered by HF and \u00a0 [Fal AI](https://fal.ai/)\u00a0  API.\n    \n    \n            \n    \n    Dia is a dialogue generation model that can generate realistic dialogue between two speakers. Use the dialogue component to create a conversation and then hit the submit button in the bottom right corner to see it come to life .\n    \n    \n            \"\"\"\n        )\n        with gr.Row():\n            with gr.Column():\n                dialogue = gr.Dialogue(\n                    speakers=speakers, tags=tags, formatter=formatter\n                )\n            with gr.Column():\n                with gr.Row():\n                    audio = gr.Audio(label=\"Audio\")\n                with gr.Row():\n                    gr.DeepLinkButton(value=\"Share Audio via Link\")\n        with gr.Row():\n            gr.Examples(\n                examples=[\n                    [\n                        [\n                            {\n                                \"speaker\": \"Speaker 1\",\n                                \"text\": \"Why did the chicken cross the road?\",\n                            },\n                            {\"speaker\": \"Speaker 2\", \"text\": \"I don't know!\"},\n                            {\n                                \"speaker\": \"Speaker 1\",\n                                \"text\": \"to get to the other side! (laughs)\",\n     ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": " 2\", \"text\": \"I don't know!\"},\n                            {\n                                \"speaker\": \"Speaker 1\",\n                                \"text\": \"to get to the other side! (laughs)\",\n                            },\n                        ]\n                    ],\n                    [\n                        [\n                            {\n                                \"speaker\": \"Speaker 1\",\n                                \"text\": \"I am a little tired today (sighs).\",\n                            },\n                            {\"speaker\": \"Speaker 2\", \"text\": \"Hang in there!\"},\n                        ]\n                    ],\n                ],\n                inputs=[dialogue],\n                cache_examples=False,\n            )\n    \n        dialogue.submit(query, [dialogue], audio)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Dialogue component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Dialogue.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Dialogue changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Dialogue.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Dialogue.  \n`Dialogue.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the Dialogue is focused.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    b", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "n the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending even", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "ending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "value identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "The gr.DeletedFileData class is a subclass of gr.EventData that\nspecifically carries information about the `.delete()` event. When\ngr.DeletedFileData is added as a type hint to an argument of an event listener\nmethod, a gr.DeletedFileData object will automatically be passed as the value\nof that argument. The attributes of this object contains information about the\nevent that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "import gradio as gr\n    \n    def test(delete_data: gr.DeletedFileData):\n        return delete_data.file.path\n    \n    with gr.Blocks() as demo:\n        files = gr.File(file_count=\"multiple\")\n        deleted_file = gr.File()\n        files.delete(test, None, deleted_file)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    file: FileData\n\nThe file that was deleted, as a FileData object. The str path to the file can\nbe retrieved with the .path attribute.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "file_component_events\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def delete_file(n: int, file:\ngr.DeletedFileData): return [file.file.path, n + 1] with gr.Blocks() as demo:\nwith gr.Row(): with gr.Column(): file_component = gr.File(label=\"Upload Single\nFile\", file_count=\"single\") with gr.Column(): output_file_1 = gr.File(\nlabel=\"Upload Single File Output\", file_count=\"single\" ) num_load_btn_1 =\ngr.Number(label=\"Load Upload Single File\", value=0) file_component.upload(\nlambda s, n: (s, n + 1), [file_component, num_load_btn_1], [output_file_1,\nnum_load_btn_1], ) with gr.Row(): with gr.Column(): file_component_multiple =\ngr.File( label=\"Upload Multiple Files\", file_count=\"multiple\" ) with\ngr.Column(): output_file_2 = gr.File( label=\"Upload Multiple Files Output\",\nfile_count=\"multiple\" ) num_load_btn_2 = gr.Number(label=\"Load Upload\nMultiple Files\", value=0) file_component_multiple.upload( lambda s, n: (s, n +\n1), [file_component_multiple, num_load_btn_2], [output_file_2,\nnum_load_btn_2], ) with gr.Row(): with gr.Column(): file_component_specific =\ngr.File( label=\"Upload Multiple Files Image/Video\", file_count=\"multiple\",\nfile_types=[\"image\", \"video\"], ) with gr.Column(): output_file_3 = gr.File(\nlabel=\"Upload Multiple Files Output Image/Video\", file_count=\"multiple\" )\nnum_load_btn_3 = gr.Number( label=\"Load Upload Multiple Files Image/Video\",\nvalue=0 ) file_component_specific.upload( lambda s, n: (s, n + 1),\n[file_component_specific, num_load_btn_3], [output_file_3, num_load_btn_3], )\nwith gr.Row(): with gr.Column(): file_component_pdf = gr.File(label=\"Upload\nPDF File\", file_types=[\".pdf\"]) with gr.Column(): output_file_4 =\ngr.File(label=\"Upload PDF File Output\") num_load_btn_4 = gr.Number(label=\"\nLoad Upload PDF File\", value=0) file_component_pdf.upload( lambda s, n: (s, n\n+ 1), [file_component_pdf, num_load_btn_4], [output_file_4, num_load_btn_4], )\nwith gr.Row(): with gr.Column(): file_component_invalid = gr.File(\nlabel=\"Upload File with Invalid file_types\", file_types=", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "1), [file_component_pdf, num_load_btn_4], [output_file_4, num_load_btn_4], )\nwith gr.Row(): with gr.Column(): file_component_invalid = gr.File(\nlabel=\"Upload File with Invalid file_types\", file_types=[\"invalid file_type\"],\n) with gr.Column(): output_file_5 = gr.File(label=\"Upload File with Invalid\nfile_types Output\") num_load_btn_5 = gr.Number( label=\"Load Upload File with\nInvalid file_types\", value=0 ) file_component_invalid.upload( lambda s, n: (s,\nn + 1), [file_component_invalid, num_load_btn_5], [output_file_5,\nnum_load_btn_5], ) with gr.Row(): with gr.Column(): del_file_input =\ngr.File(label=\"Delete File\", file_count=\"multiple\") with gr.Column():\ndel_file_data = gr.Textbox(label=\"Delete file data\") num_load_btn_6 =\ngr.Number(label=\"Deleted File\", value=0) del_file_input.delete( delete_file,\n[num_load_btn_6], [del_file_data, num_load_btn_6], ) f =\ngr.File(label=\"Upload many File\", file_count=\"multiple\") \nf.delete(delete_file) f.delete(delete_file, inputs=None, outputs=None) if\n__name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def delete_file(n: int, file: gr.DeletedFileData):\n        return [file.file.path, n + 1]\n    \n    with gr.Blocks() as demo:\n    \n        with gr.Row():\n            with gr.Column():\n                file_component = gr.File(label=\"Upload Single File\", file_count=\"single\")\n            with gr.Column():\n                output_file_1 = gr.File(\n                    label=\"Upload Single File Output\", file_count=\"single\"\n                )\n                num_load_btn_1 = gr.Number(label=\"Load Upload Single File\", value=0)\n                file_component.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component, num_load_btn_1],\n                    [output_file_1, num_load_btn_1],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_multiple = gr.File(\n                    label=\"Upload Multiple Files\", file_count=\"multiple\"\n ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "1],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_multiple = gr.File(\n                    label=\"Upload Multiple Files\", file_count=\"multiple\"\n                )\n            with gr.Column():\n                output_file_2 = gr.File(\n                    label=\"Upload Multiple Files Output\", file_count=\"multiple\"\n                )\n                num_load_btn_2 = gr.Number(label=\"Load Upload Multiple Files\", value=0)\n                file_component_multiple.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_multiple, num_load_btn_2],\n                    [output_file_2, num_load_btn_2],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_specific = gr.File(\n                    label=\"Upload Multiple Files Image/Video\",\n                    file_count=\"multiple\",\n                    file_types=[\"image\", \"video\"],\n                )\n            with gr.Column():\n                output_file_3 = gr.File(\n                    label=\"Upload Multiple Files Output Image/Video\", file_count=\"multiple\"\n                )\n                num_load_btn_3 = gr.Number(\n                    label=\"Load Upload Multiple Files Image/Video\", value=0\n                )\n                file_component_specific.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_specific, num_load_btn_3],\n                    [output_file_3, num_load_btn_3],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_pdf = gr.File(label=\"Upload PDF File\", file_types=[\".pdf\"])\n            with gr.Column():\n                output_file_4 = gr.File(label=\"Upload PDF File Output\")\n                num_load_btn_4 = gr.Number(label=\"Load Upload PDF File\", value=0)\n                file_component_pdf.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_pdf, num_load_bt", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "_btn_4 = gr.Number(label=\"Load Upload PDF File\", value=0)\n                file_component_pdf.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_pdf, num_load_btn_4],\n                    [output_file_4, num_load_btn_4],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_invalid = gr.File(\n                    label=\"Upload File with Invalid file_types\",\n                    file_types=[\"invalid file_type\"],\n                )\n            with gr.Column():\n                output_file_5 = gr.File(label=\"Upload File with Invalid file_types Output\")\n                num_load_btn_5 = gr.Number(\n                    label=\"Load Upload File with Invalid file_types\", value=0\n                )\n                file_component_invalid.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_invalid, num_load_btn_5],\n                    [output_file_5, num_load_btn_5],\n                )\n        with gr.Row():\n            with gr.Column():\n                del_file_input = gr.File(label=\"Delete File\", file_count=\"multiple\")\n            with gr.Column():\n                del_file_data = gr.Textbox(label=\"Delete file data\")\n                num_load_btn_6 = gr.Number(label=\"Deleted File\", value=0)\n                del_file_input.delete(\n                    delete_file,\n                    [num_load_btn_6],\n                    [del_file_data, num_load_btn_6],\n                )\n        f = gr.File(label=\"Upload many File\", file_count=\"multiple\")\n        f.delete(delete_file)\n        f.delete(delete_file, inputs=None, outputs=None)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "Creates a textarea for users to enter string input or display string output\nand also allows for the uploading of multimedia files.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "**As input component** : Passes text value and list of file(s) as a `dict`\ninto the function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: MultimodalValue | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `dict` with \"text\" and \"files\", both\noptional. The files array is a list of file paths or URLs.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> MultimodalValue | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | dict[str, str | list] | Callable | None\n\ndefault `= None`\n\nDefault value to show in MultimodalTextbox. A string value, or a dictionary of\nthe form {\"text\": \"sample text\", \"files\": [{path: \"files/file.jpg\", orig_name:\n\"file.jpg\", url: \"http://image_url.jpg\", size: 100}]}. If a function is\nprovided, the function will be called each time the app loads to set the\ninitial value of this component.\n\n\n    \n    \n    sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None\n\ndefault `= None`\n\nA list of sources permitted. \"upload\" creates a button where users can click\nto upload or drop files, \"microphone\" creates a microphone input. If None,\ndefaults to [\"upload\"].\n\n\n    \n    \n    file_types: list[str] | None\n\ndefault `= None`\n\nList of file extensions or types of files to be uploaded (e.g. ['image',\n'.json', '.mp4']). \"file\" allows any file to be uploaded, \"image\" allows only\nimage files to be uploaded, \"audio\" allows only audio files to be uploaded,\n\"video\" allows only video files to be uploaded, \"text\" allows only text files\nto be uploaded.\n\n\n    \n    \n    file_count: Literal['single', 'multiple', 'directory']\n\ndefault `= \"single\"`\n\nif single, allows user to upload one file. If \"multiple\", user uploads\nmultiple files. If \"directory\", user uploads all files in selected directory.\nReturn type will be list for each file in case of \"multiple\" or \"directory\".\n\n\n    \n    \n    lines: int\n\ndefault `= 1`\n\nminimum number of line rows to provide in textarea.\n\n\n    \n    \n    max_lines: int\n\ndefault `= 20`\n\nmaximum number of line rows to provide in textarea.\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\nplaceholder hint to provide behind textarea.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "e`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "cted first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= False`\n\nIf True, will focus on the textbox when the page loads. Use this carefully, as\nit can cause usability issues for sighted and non-sighted users.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "er()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    text_align: Literal['left', 'right'] | None\n\ndefault `= None`\n\nHow to align the text in the textbox, can be: \"left\", \"right\", or None\n(default). If None, the alignment is left if `rtl` is False, or right if `rtl`\nis True. Can only be changed if `type` is \"text\".\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True and `type` is \"text\", sets the direction of the text to right-to-left\n(cursor appears on the left of the text). Default is False, which renders\ncursor on the right.\n\n\n    \n    \n    submit_btn: str | bool | None\n\ndefault `= True`\n\nIf False, will not show a submit button. If a string, will use that string as\nthe submit button text.\n\n\n    \n    \n    stop_btn: str | bool | None\n\ndefault `= False`\n\nIf True, will show a stop button (useful for streaming demos). If a string,\nwill use that string as the stop button text.\n\n\n    \n    \n    max_plain_text_length: int\n\ndefault `= 1000`\n\nMaximum length of plain text in the textbox. If the text exceeds this length,\nthe text will be pasted as a file. Default is 1000.\n\n\n    \n    \n    html_attributes: InputHTMLAttributes | None\n\ndefault `= None`\n\nAn instance of gr.InputHTMLAttributes, which can be used to set HTML\nattributes for the input/textarea elements. Example:\nInputHTMLAttributes(autocorrect=\"off\", spellcheck=False) to disable\nautocorrect and spellcheck.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.MultimodalTextbox` | \"multimodaltextbox\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "chatbot_multimodal\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import time Chatbot demo with multimodal\ninput (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows\nsupport for streaming text. def print_like_dislike(x: gr.LikeData):\nprint(x.index, x.value, x.liked) def add_message(history, message): for x in\nmessage[\"files\"]: history.append({\"role\": \"user\", \"content\": {\"path\": x}}) if\nmessage[\"text\"] is not None: history.append({\"role\": \"user\", \"content\":\nmessage[\"text\"]}) return history, gr.MultimodalTextbox(value=None,\ninteractive=False) def bot(history: list): response = \"**That's cool!**\"\nhistory.append({\"role\": \"assistant\", \"content\": \"\"}) for character in\nresponse: history[-1][\"content\"] += character time.sleep(0.05) yield history\nwith gr.Blocks() as demo: chatbot = gr.Chatbot(elem_id=\"chatbot\",\nbubble_full_width=False, type=\"messages\") chat_input = gr.MultimodalTextbox(\ninteractive=True, file_count=\"multiple\", placeholder=\"Enter message or upload\nfile...\", show_label=False, sources=[\"microphone\", \"upload\"], ) chat_msg =\nchat_input.submit( add_message, [chatbot, chat_input], [chatbot, chat_input] )\nbot_msg = chat_msg.then(bot, chatbot, chatbot, api_name=\"bot_response\")\nbot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None,\n[chat_input]) chatbot.like(print_like_dislike, None, None,\nlike_user_message=True) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import time\n    \n    Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n    \n    \n    def print_like_dislike(x: gr.LikeData):\n        print(x.index, x.value, x.liked)\n    \n    \n    def add_message(history, message):\n        for x in message[\"files\"]:\n            history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n        if message[\"text\"] is not None:\n            history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n        return history, gr.MultimodalTextbox(valu", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "le\": \"user\", \"content\": {\"path\": x}})\n        if message[\"text\"] is not None:\n            history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n        return history, gr.MultimodalTextbox(value=None, interactive=False)\n    \n    \n    def bot(history: list):\n        response = \"**That's cool!**\"\n        history.append({\"role\": \"assistant\", \"content\": \"\"})\n        for character in response:\n            history[-1][\"content\"] += character\n            time.sleep(0.05)\n            yield history\n    \n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(elem_id=\"chatbot\", bubble_full_width=False, type=\"messages\")\n    \n        chat_input = gr.MultimodalTextbox(\n            interactive=True,\n            file_count=\"multiple\",\n            placeholder=\"Enter message or upload file...\",\n            show_label=False,\n            sources=[\"microphone\", \"upload\"],\n        )\n    \n        chat_msg = chat_input.submit(\n            add_message, [chatbot, chat_input], [chatbot, chat_input]\n        )\n        bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name=\"bot_response\")\n        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n    \n        chatbot.like(print_like_dislike, None, None, like_user_message=True)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe MultimodalTextbox component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`MultimodalTextbox.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the MultimodalTextbox changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`MultimodalTextbox.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the MultimodalTextbox.  \n`MultimodalTextbox.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the MultimodalTextbox. Uses event data gradio.SelectData to carry `value` referring to the label of the MultimodalTextbox, and `selected` to refer to state of the MultimodalTextbox. See EventData documentation on how to use this event data  \n`MultimodalTextbox.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the MultimodalTextbox is focused.  \n`MultimodalTextbox.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the MultimodalTextbox is focused.  \n`MultimodalTextbox.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the MultimodalTextbox is unfocused/blurred.  \n`MultimodalTextbox.stop(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the MultimodalTextbox.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's pred", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "t Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "roll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of oth", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_ap", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "rency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "Column is a layout element within Blocks that renders all children\nvertically. The widths of columns can be set through the `scale` and\n`min_width` parameters. If a certain scale results in a column narrower than\nmin_width, the min_width parameter will win.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/column", "source_page_title": "Gradio - Column Docs"}, {"text": "with gr.Blocks() as demo:\n        with gr.Row():\n            with gr.Column(scale=1):\n                text1 = gr.Textbox()\n                text2 = gr.Textbox()\n            with gr.Column(scale=4):\n                btn1 = gr.Button(\"Button 1\")\n                btn2 = gr.Button(\"Button 2\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/column", "source_page_title": "Gradio - Column Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    scale: int\n\ndefault `= 1`\n\nrelative width compared to adjacent Columns. For example, if Column A has\nscale=2, and Column B has scale=1, A will be twice as wide as B.\n\n\n    \n    \n    min_width: int\n\ndefault `= 320`\n\nminimum pixel width of Column, will wrap if not sufficient screen space to\nsatisfy this value. If a certain scale value results in a column narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    variant: Literal['default', 'panel', 'compact']\n\ndefault `= \"default\"`\n\ncolumn type, 'default' (no background), 'panel' (gray background color and\nrounded corners), or 'compact' (rounded corners and no internal gap).\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, column will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    show_progress: bool\n\ndefault `= False`\n\nIf True, shows progress animation when being updated.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) paramet", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/column", "source_page_title": "Gradio - Column Docs"}, {"text": "] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/column", "source_page_title": "Gradio - Column Docs"}, {"text": "Used to render arbitrary Markdown output. Can also render latex enclosed by\ndollar signs. As this component does not accept user input, it is rarely used\nas an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "**As input component** : Passes the `str` of Markdown corresponding to the\ndisplayed value.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a valid `str` that can be rendered as\nMarkdown.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | I18nData | Callable | None\n\ndefault `= None`\n\nValue to show in Markdown component. If a function is provided, the function\nwill be called each time the app loads to set the initial value of this\ncomponent.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThis parameter has no effect\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nThis parameter has no effect.\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True, sets the direction of the rendered text to right-to-left. Default is\nFalse, which renders text left-to-right.\n\n\n    \n    \n    latex_delimiters: list[dict[str, str | bool]] | None\n\ndefault `= None`\n\nA list of dicts of the form {\"left\": open delimiter (str), \"right\": close\ndelimiter (str), \"display\": whether to display in newline (bool)} that will be\nused to render LaTeX expressions. If not provided, `latex_delimiters` is set\nto `[{ \"left\": \"$$\", \"right\": \"$$\", \"display\": True }]`, so only expressions\nenclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass\nin an empty list to disable LaTeX rendering. For more information, see the\n[KaTeX documentation](https://katex.org/docs/autorender.html).\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "t will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    sanitize_html: bool\n\ndefault `= True`\n\nIf False, will disable HTML sanitization when converted from markdown. This is\nnot recommended, as it can lead to security vulnerabilities.\n\n\n    \n    \n    line_breaks: bool\n\ndefault `= False`\n\nIf True, will enable Github-flavored Markdown line breaks in chatbot messages.\nIf False (default), single new lines will be ignored.\n\n\n    \n    \n    header_links: bool\n\ndefault `= False`\n\nIf True, will automatically create anchors for headings, displaying a link\nicon on hover.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. If mark", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "a link\nicon on hover.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. If markdown content exceeds the height, the\ncomponent will scroll.\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= None`\n\nThe maximum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If markdown content exceeds the\nheight, the component will scroll. If markdown content is shorter than the\nheight, the component will shrink to fit the content. Will not have any effect\nif `height` is set and is smaller than `max_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If markdown content exceeds the\nheight, the component will expand to fit the content. Will not have any effect\nif `height` is set and is larger than `min_height`.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, includes a copy button to copy the text in the Markdown component.\nDefault is False.\n\n\n    \n    \n    container: bool\n\ndefault `= False`\n\nIf True, the Markdown component will be displayed in a container. Default is\nFalse.\n\n\n    \n    \n    padding: bool\n\ndefault `= False`\n\nIf True, the Markdown component will have a certain padding (set by the\n`--block-padding` CSS variable) in all directions. Default is False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Markdown` | \"markdown\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "blocks_helloblocks_kinematics\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def welcome(name): return f\"Welcome to Gradio,\n{name}!\" with gr.Blocks() as demo: gr.Markdown( \"\"\" Hello World! Start\ntyping below to see the output. \"\"\") inp = gr.Textbox(placeholder=\"What is\nyour name?\") out = gr.Textbox() inp.change(welcome, inp, out) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def welcome(name):\n        return f\"Welcome to Gradio, {name}!\"\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\n        \"\"\"\n        Hello World!\n        Start typing below to see the output.\n        \"\"\")\n        inp = gr.Textbox(placeholder=\"What is your name?\")\n        out = gr.Textbox()\n        inp.change(welcome, inp, out)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd import numpy as np import gradio as gr def\nplot(v, a): g = 9.81 theta = a / 180 * 3.14 tmax = ((2 * v) * np.sin(theta)) /\ng timemat = tmax * np.linspace(0, 1, 40) x = (v * timemat) * np.cos(theta) y =\n((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2)) df =\npd.DataFrame({\"x\": x, \"y\": y}) return df demo = gr.Blocks() with demo:\ngr.Markdown( r\"Let's do some kinematics! Choose the speed and angle to see the\ntrajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n) with gr.Row(): speed = gr.Slider(1, 30, 25, label=\"Speed\") angle =\ngr.Slider(0, 90, 45, label=\"Angle\") output = gr.LinePlot( x=\"x\", y=\"y\",\noverlay_point=True, tooltip=[\"x\", \"y\"], x_lim=[0, 100], y_lim=[0, 60],\nwidth=350, height=300, ) btn = gr.Button(value=\"Run\") btn.click(plot, [speed,\nangle], output) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import pandas as pd\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot(v, a):\n        g = 9.81\n        theta = a / 180 * 3.14\n        tmax = ((2 * v) * np.sin(theta)) / g\n        timemat = tmax * np.linspace(0, 1, 40)\n    \n        x = (v * timemat) * np.cos(theta)\n        y = ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "\n        g = 9.81\n        theta = a / 180 * 3.14\n        tmax = ((2 * v) * np.sin(theta)) / g\n        timemat = tmax * np.linspace(0, 1, 40)\n    \n        x = (v * timemat) * np.cos(theta)\n        y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))\n        df = pd.DataFrame({\"x\": x, \"y\": y})\n        return df\n    \n    demo = gr.Blocks()\n    \n    with demo:\n        gr.Markdown(\n            r\"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n        )\n    \n        with gr.Row():\n            speed = gr.Slider(1, 30, 25, label=\"Speed\")\n            angle = gr.Slider(0, 90, 45, label=\"Angle\")\n        output = gr.LinePlot(\n            x=\"x\",\n            y=\"y\",\n            overlay_point=True,\n            tooltip=[\"x\", \"y\"],\n            x_lim=[0, 100],\n            y_lim=[0, 60],\n            width=350,\n            height=300,\n        )\n        btn = gr.Button(value=\"Run\")\n        btn.click(plot, [speed, angle], output)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Markdown component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Markdown.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Markdown changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Markdown.copy(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user copies content from the Markdown. Uses event data gradio.CopyData to carry information about the copied content. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: s", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "xt] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": " request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npe", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "The gr.EditData class is a subclass of gr.Event data that specifically\ncarries information about the `.edit()` event. When gr.EditData is added as a\ntype hint to an argument of an event listener method, a gr.EditData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/editdata", "source_page_title": "Gradio - Editdata Docs"}, {"text": "import gradio as gr\n    \n    def edit(edit_data: gr.EditData, history: list[gr.MessageDict]):\n        history_up_to_edit = history[:edit_data.index]\n        history_up_to_edit[-1] = edit_data.value\n        return history_up_to_edit\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot()\n        chatbot.undo(edit, chatbot, chatbot)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/editdata", "source_page_title": "Gradio - Editdata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the message that was edited.\n\n\n    \n    \n    previous_value: Any\n\nThe previous content of the message that was edited.\n\n\n    \n    \n    value: Any\n\nThe new content of the message that was edited.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/editdata", "source_page_title": "Gradio - Editdata Docs"}, {"text": "A base class for defining methods that all input/output components should\nhave.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "**As input component** : Passes a value of arbitrary type through.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: Any\n    )\n    \t...\n\n  \n\n**As output component** : Expects a value of arbitrary type, as long as it\ncan be deepcopied.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> Any\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: Any\n\ndefault `= None`\n\nthe initial value (of arbitrary type) of the state. The provided argument is\ndeepcopied. If a callable is provided, the function will be called whenever\nthe app loads to set the initial value of the state.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nshould always be True, is included for consistency with other components.\n\n\n    \n    \n    time_to_live: int | float | None\n\ndefault `= None`\n\nthe number of seconds the state should be stored for after it is created or\nupdated. If None, the state will be stored indefinitely. Gradio automatically\ndeletes state variables after a user closes the browser tab or refreshes the\npage, so this is useful for clearing state for potentially long running\nsessions.\n\n\n    \n    \n    delete_callback: Callable[[Any], None] | None\n\ndefault `= None`\n\na function that is called when the state is deleted. The function should take\nthe state value as an argument.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe State component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`State.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the State changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "nes how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`).", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nf", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": " Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "Button that triggers a Spaces Duplication, when the demo is on Hugging Face\nSpaces. Does nothing locally.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "**As input component** : (Rarely used) the `str` corresponding to the\nbutton label when the button is clicked\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : string corresponding to the button label\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str\n\ndefault `= \"Duplicate Space\"`\n\ndefault text for the button to display. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop', 'huggingface']\n\ndefault `= \"huggingface\"`\n\nsets the background and text color of the button. Use 'primary' for main call-\nto-action buttons, 'secondary' for a more subdued style, 'stop' for a stop\nbutton, 'huggingface' for a black background with white text, consistent with\nHugging Face's button styles.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"sm\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | Path | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    link: str | None\n\ndefault `= None`\n\nURL to open when the button is clicked. If None, no link will be used.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nif False, the Button will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are ass", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "gned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    scale: int | None\n\ndefault `= 0`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.DuplicateButton` | \"duplicatebutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe DuplicateButton component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`DuplicateButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Button is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_d", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": " as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to m", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "The render decorator allows Gradio Blocks apps to have dynamic layouts, so\nthat the components and event listeners in your app can change depending on\ncustom logic. Attaching a @gr.render decorator to a function will cause the\nfunction to be re-run whenever the inputs are changed (or specified triggers\nare activated). The function contains the components and event listeners that\nwill update based on the inputs.  \nThe basic usage of @gr.render is as follows:  \n1\\. Create a function and attach the @gr.render decorator to it.  \n2\\. Add the input components to the `inputs=` argument of @gr.render, and\ncreate a corresponding argument in your function for each component.  \n3\\. Add all components inside the function that you want to update based on\nthe inputs. Any event listeners that use these components should also be\ninside this function.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/render", "source_page_title": "Gradio - Render Docs"}, {"text": "import gradio as gr\n    \n    with gr.Blocks() as demo:\n        input_text = gr.Textbox()\n    \n        @gr.render(inputs=input_text)\n        def show_split(text):\n            if len(text) == 0:\n                gr.Markdown(\"No Input Provided\")\n            else:\n                for letter in text:\n                    with gr.Row():\n                        text = gr.Textbox(letter)\n                        btn = gr.Button(\"Clear\")\n                        btn.click(lambda: gr.Textbox(value=\"\"), None, text)\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/render", "source_page_title": "Gradio - Render Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    inputs: list[Component] | Component | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    triggers: list[EventListenerCallable] | EventListenerCallable | None\n\ndefault `= None`\n\nList of triggers to listen to, e.g. [btn.click, number.change]. If None, will\nlisten to changes to any inputs.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= \"always_last\"`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= None`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/render", "source_page_title": "Gradio - Render Docs"}, {"text": "This function allows you to pass custom info messages to the user. You can\ndo so simply by writing `gr.Info('message here')` in your function, and when\nthat line is executed the custom message will appear in a modal on the demo.\nThe modal is gray by default and has the heading: \"Info.\" Queue must be\nenabled for this behavior; otherwise, the message will be printed to the\nconsole.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "import gradio as gr\n    def hello_world():\n        gr.Info('This is some info.')\n        return \"hello world\"\n    with gr.Blocks() as demo:\n        md = gr.Markdown()\n        demo.load(hello_world, inputs=None, outputs=[md])\n    demo.queue().launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    message: str\n\ndefault `= \"Info issued.\"`\n\nThe info message to be displayed to the user. Can be HTML, which will be\nrendered in the modal.\n\n\n    \n    \n    duration: float | None\n\ndefault `= 10`\n\nThe duration in seconds that the info message should be displayed for. If None\nor 0, the message will be displayed indefinitely until the user closes it.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the error message should be displayed in the UI.\n\n\n    \n    \n    title: str\n\ndefault `= \"Info\"`\n\nThe title to be displayed to the user at the top of the modal.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "blocks_chained_events\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def failure(): raise gr.Error(\"This should\nfail!\") def exception(): raise ValueError(\"Something went wrong\") def\nsuccess(): return True def warning_fn(): gr.Warning(\"This is a warning!\") def\ninfo_fn(): gr.Info(\"This is some info\") with gr.Blocks() as demo:\ngr.Markdown(\"Used in E2E tests of success event trigger. The then event\ncovered in chatbot E2E tests.\" \" Also testing that the status modals show\nup.\") with gr.Row(): result = gr.Textbox(label=\"Result\") result_2 =\ngr.Textbox(label=\"Consecutive Event\") with gr.Row(): success_btn =\ngr.Button(value=\"Trigger Success\") success_btn_2 = gr.Button(value=\"Trigger\nConsecutive Success\") failure_btn = gr.Button(value=\"Trigger Failure\")\nfailure_exception = gr.Button(value=\"Trigger Failure With ValueError\") with\ngr.Row(): trigger_warning = gr.Button(value=\"Trigger Warning\") trigger_info =\ngr.Button(value=\"Trigger Info\") success_btn_2.click(success, None,\nNone).success(lambda: \"First Event Trigered\", None, result).success(lambda:\n\"Consecutive Event Triggered\", None, result_2) success_btn.click(success,\nNone, None).success(lambda: \"Success event triggered\", inputs=None,\noutputs=result) failure_btn.click(failure, None, None).success(lambda: \"Should\nnot be triggered\", inputs=None, outputs=result)\nfailure_exception.click(exception, None, None)\ntrigger_warning.click(warning_fn, None, None) trigger_info.click(info_fn,\nNone, None) if __name__ == \"__main__\": demo.launch(show_error=True)\n\n    \n    \n    import gradio as gr\n    \n    def failure():\n        raise gr.Error(\"This should fail!\")\n    \n    def exception():\n        raise ValueError(\"Something went wrong\")\n    \n    def success():\n        return True\n    \n    def warning_fn():\n        gr.Warning(\"This is a warning!\")\n    \n    def info_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n        ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "fo_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n                    \" Also testing that the status modals show up.\")\n        with gr.Row():\n            result = gr.Textbox(label=\"Result\")\n            result_2 = gr.Textbox(label=\"Consecutive Event\")\n        with gr.Row():\n            success_btn = gr.Button(value=\"Trigger Success\")\n            success_btn_2 = gr.Button(value=\"Trigger Consecutive Success\")\n            failure_btn = gr.Button(value=\"Trigger Failure\")\n            failure_exception = gr.Button(value=\"Trigger Failure With ValueError\")\n        with gr.Row():\n            trigger_warning = gr.Button(value=\"Trigger Warning\")\n            trigger_info = gr.Button(value=\"Trigger Info\")\n    \n            success_btn_2.click(success, None, None).success(lambda: \"First Event Trigered\", None, result).success(lambda: \"Consecutive Event Triggered\", None, result_2)\n            success_btn.click(success, None, None).success(lambda: \"Success event triggered\", inputs=None, outputs=result)\n            failure_btn.click(failure, None, None).success(lambda: \"Should not be triggered\", inputs=None, outputs=result)\n            failure_exception.click(exception, None, None)\n            trigger_warning.click(warning_fn, None, None)\n            trigger_info.click(info_fn, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch(show_error=True)\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "Mount a gradio.Blocks to an existing FastAPI application.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": "from fastapi import FastAPI\n    import gradio as gr\n    app = FastAPI()\n    @app.get(\"/\")\n    def read_main():\n        return {\"message\": \"This is your main app\"}\n    io = gr.Interface(lambda x: \"Hello, \" + x + \"!\", \"textbox\", \"textbox\")\n    app = gr.mount_gradio_app(app, io, path=\"/gradio\")\n\nThen run `uvicorn run:app` from the terminal and navigate to\nhttp://localhost:8000/gradio.\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    app: fastapi.FastAPI\n\nThe parent FastAPI application.\n\n\n    \n    \n    blocks: gradio.Blocks\n\nThe blocks object we want to mount to the parent app.\n\n\n    \n    \n    path: str\n\nThe path at which the gradio application will be mounted, e.g. \"/gradio\".\n\n\n    \n    \n    server_name: str\n\ndefault `= \"0.0.0.0\"`\n\nThe server name on which the Gradio app will be run.\n\n\n    \n    \n    server_port: int\n\ndefault `= 7860`\n\nThe port on which the Gradio app will be run.\n\n\n    \n    \n    show_api: bool | None\n\ndefault `= None`\n\nIf False, hides the \"Use via API\" button on the Gradio interface.\n\n\n    \n    \n    app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    auth: Callable | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access the gradio app. Can also provide function that takes\nusername and password and returns True if valid login.\n\n\n    \n    \n    auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page for this gradio app.\n\n\n    \n    \n    auth_dependency: Callable[[fastapi.Request], str | None] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the gradio app (they will see a 401 Unauthorized\nresponse). To be used with external authentication systems like OAuth. Cannot\nbe used with `auth`.\n\n\n    \n    \n    root_path: str | None\n\ndefault `= None`\n\nThe subpath corresponding to the public deployment of this FastAPI\napplication. For example, if the application is served at\n\"https://example.com/myapp\", the `root_path` should be set to \"/myapp\". A full\nURL beginning with http:// or https:// can be provided, wh", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": "tAPI\napplication. For example, if the application is served at\n\"https://example.com/myapp\", the `root_path` should be set to \"/myapp\". A full\nURL beginning with http:// or https:// can be provided, which will be used in\nits entirety. Normally, this does not need to provided (even if you are using\na custom `path`). However, if you are serving the FastAPI app behind a proxy,\nthe proxy may not provide the full path to the Gradio app in the request\nheaders. In which case, you can provide the root path here.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that this gradio app is\nallowed to serve. Must be absolute paths. Warning: if you provide directories,\nany files in these directories or their subdirectories are accessible to all\nusers of your app.\n\n\n    \n    \n    blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that this gradio app is not\nallowed to serve (i.e. users of your app are not allowed to access). Must be\nabsolute paths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default.\n\n\n    \n    \n    favicon_path: str | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for this gradio app's page.\n\n\n    \n    \n    show_error: bool\n\ndefault `= True`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. Otherwise, errors will only be visible in\nthe terminal session running the Gradio app.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwh", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": " is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 20+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n    \n    \n    node_server_name: str | None\n\ndefault `= None`\n\nThe name of the Node server to use for SSR. If None, will use\nGRADIO_NODE_SERVER_NAME environment variable or search for a node binary in\nthe system.\n\n\n    \n    \n    node_port: int | None\n\ndefault `= None`\n\nThe port on which the Node server should run. If None, will use\nGRADIO_NODE_SERVER_PORT environment variable or find a free port.\n\n\n    \n    \n    enable_monitoring: bool | None\n\ndefault `= None`\n\n\n    \n    \n    pwa: bool | None\n\ndefault `= None`\n\n\n    \n    \n    i18n: I18n | None\n\ndefault `= None`\n\nIf provided, the i18n instance to use for this gradio app.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the MCP server will be launched on the gradio app. If None, will use\nGRADIO_MCP_SERVER environment variable or default to False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": "Creates a component allows users to upload or view 3D Model files (.obj,\n.glb, .stl, .gltf, .splat, or .ply).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "**As input component** : Passes the uploaded file as a `str` filepath to\nthe function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects function to return a `str` or\n`pathlib.Path` filepath of type (.obj, .glb, .stl, or .gltf)\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Callable | None\n\ndefault `= None`\n\npath to (.obj, .glb, .stl, .gltf, .splat, or .ply) file to show in model3D\nviewer. If a function is provided, the function will be called each time the\napp loads to set the initial value of this component.\n\n\n    \n    \n    display_mode: Literal['solid', 'point_cloud', 'wireframe'] | None\n\ndefault `= None`\n\nthe display mode of the 3D model in the scene. Can be \"solid\" (which renders\nthe model as a solid object), \"point_cloud\", or \"wireframe\". For .splat, or\n.ply files, this parameter is ignored, as those files can only be rendered as\nsolid objects.\n\n\n    \n    \n    clear_color: tuple[float, float, float, float] | None\n\ndefault `= None`\n\nbackground color of scene, should be a tuple of 4 floats between 0 and 1\nrepresenting RGBA values.\n\n\n    \n    \n    camera_position: tuple[int | float | None, int | float | None, int | float | None]\n\ndefault `= (None, None, None)`\n\ninitial camera position of scene, provided as a tuple of `(alpha, beta,\nradius)`. Each value is optional. If provided, `alpha` and `beta` should be in\ndegrees reflecting the angular position along the longitudinal and latitudinal\naxes, respectively. Radius corresponds to the distance from the center of the\nobject to the camera.\n\n\n    \n    \n    zoom_speed: float\n\ndefault `= 1`\n\nthe speed of zooming in and out of the scene when the cursor wheel is rotated\nor when screen is pinched on a mobile device. Should be a positive float,\nincrease this value to make zooming faster, decrease to make it slower.\nAffects the wheelPrecision property of the camera.\n\n\n    \n    \n    pan_speed: float\n\ndefault `= 1`\n\nthe speed of panning the scene when the cursor is dragged or when the screen\nis dragged on a mobile device. Should be a positive float, increase this value\nto make panning faster, decrease to make it slower. Affects the panSensibility\nproperty of the camera.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the model3D c", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "his value\nto make panning faster, decrease to make it slower. Affects the panSensibility\nproperty of the camera.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the model3D component, specified in pixels if a number is\npassed, or in CSS units if a string is passed.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool |", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "reen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload a file; if False, can only be used to\ndisplay files. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Model3D` | \"model3d\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Model3D component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Model3D.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Model3D changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Model3D.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Model3D.  \n`Model3D.edit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user edits the Model3D (e.g. image) using the built-in editor.  \n`Model3D.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Model3D using the clear button for the component.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.compo", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": " inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "ress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pe", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "one\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "| tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "Displays a classification label, along with confidence scores of top\ncategories, if provided. As this component does not accept user input, it is\nrarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "**As input component** : Depending on the value, passes the label as a `str | int | float`, or the labels and confidences as a `dict[str, float]`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: dict[str, float] | str | int | float | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `dict[str, float]` of classes and confidences, or `str` with just the class or an `int | float` for regression outputs, or a `str` path to a .json file containing a json dictionary in one of the preceding formats.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> dict[str, float] | str | int | float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: dict[str, float] | str | float | Callable | None\n\ndefault `= None`\n\nDefault value to show in the component. If a str or number is provided, simply\ndisplays the string or number. If a {Dict[str, float]} of classes and\nconfidences is provided, displays the top class on top and the\n`num_top_classes` below, along with their confidence bars. If a function is\nprovided, the function will be called each time the app loads to set the\ninitial value of this component.\n\n\n    \n    \n    num_top_classes: int | None\n\ndefault `= None`\n\nnumber of most confident classes to show.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_he", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nThe background color of the label (either a valid css color name or\nhexadecimal string).\n\n\n    \n    \n    show", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "e values\nprovided during constructor.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nThe background color of the label (either a valid css color name or\nhexadecimal string).\n\n\n    \n    \n    show_heading: bool\n\ndefault `= True`\n\nIf False, the heading will not be displayed if a dictionary of labels and\nconfidences is provided. The heading will still be visible if the value is a\nstring or number.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Label` | \"label\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Label component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Label.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Label changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Label.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Label. Uses event data gradio.SelectData to carry `value` referring to the label of the Label, and `selected` to refer to state of the Label. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an emp", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "ontext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queu", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "lt `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would al", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": " not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "ener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "Creates a set of (string or numeric type) radio buttons of which only one\ncan be selected.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "**As input component** : Passes the value of the selected radio button as a `str | int | float`, or its index as an `int` into the function, depending on `type`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | int | float | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str | int | float` corresponding to the value of the radio button to be selected\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | int | float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    choices: list[str | int | float | tuple[str, str | int | float]] | None\n\ndefault `= None`\n\nA list of string or numeric options to select from. An option can also be a\ntuple of the form (name, value), where name is the displayed name of the radio\nbutton and value is the value to be passed to the function, or returned by the\nfunction.\n\n\n    \n    \n    value: str | int | float | Callable | None\n\ndefault `= None`\n\nThe option selected by default. If None, no option is selected by default. If\na function is provided, the function will be called each time the app loads to\nset the initial value of this component.\n\n\n    \n    \n    type: Literal['value', 'index']\n\ndefault `= \"value\"`\n\nType of value to be returned by component. \"value\" returns the string of the\nchoice selected, \"index\" returns the index of the choice selected.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nRelative width compared to adjacent Components in a Row. For example, if\nComponent A has scale=2, and Component B has scale=1, A will be twice as wide\nas B. Should be an integer.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nMinimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, choices in this radio group will be selectable; if False, selection\nwill be disabled. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_k", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True, the radio buttons will be displayed in right-to-left order. Default\nis False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Radio` | \"radio\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "sentence_builderblocks_essay\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": ",\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr countries_cities_dict = { \"USA\": [\"New York\",\n\"Los Angeles\", \"Chicago\"], \"Canada\": [\"Toronto\", \"Montreal\", \"Vancouver\"],\n\"Pakistan\": [\"Karachi\", \"Lahore\", \"Islamabad\"], } def change_textbox(choice):\nif choice == \"short\": return gr.Textbox(lines=2, visible=True),\ngr.Button(interactive=True) elif choice == \"long\": return gr.Textbox(lines=8,\nvisible=True, value=\"Lorem ipsum dolor sit amet\"), gr.Button(interactive=True)\nelse: return gr.Textbox(visible=False), gr.Button(interactive=False) with\ngr.Blocks() as demo: radio = gr.Radio( [\"short\", \"long\", \"none\"], label=\"What\nkind of essay would you like to write?\" ) text = gr.Textbox(lines=2,\ninteractive=True, show_copy_button=True) with gr.Row(): num =\ngr.Number(minimum=0, maximum=100, label=\"input\") out =\ngr.Number(label=\"output\") minimum_slider = gr.Slider(0, 100, 0, label=\"min\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "ines=2,\ninteractive=True, show_copy_button=True) with gr.Row(): num =\ngr.Number(minimum=0, maximum=100, label=\"input\") out =\ngr.Number(label=\"output\") minimum_slider = gr.Slider(0, 100, 0, label=\"min\")\nmaximum_slider = gr.Slider(0, 100, 100, label=\"max\") submit_btn =\ngr.Button(\"Submit\", variant=\"primary\") with gr.Row(): country =\ngr.Dropdown(list(countries_cities_dict.keys()), label=\"Country\") cities =\ngr.Dropdown([], label=\"Cities\") @country.change(inputs=country,\noutputs=cities) def update_cities(country): cities =\nlist(countries_cities_dict[country]) return gr.Dropdown(choices=cities,\nvalue=cities[0], interactive=True) def reset_bounds(minimum, maximum): return\ngr.Number(minimum=minimum, maximum=maximum) radio.change(fn=change_textbox,\ninputs=radio, outputs=[text, submit_btn]) gr.on( [minimum_slider.change,\nmaximum_slider.change], reset_bounds, [minimum_slider, maximum_slider],\noutputs=num, ) num.submit(lambda x: x, num, out) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    countries_cities_dict = {\n        \"USA\": [\"New York\", \"Los Angeles\", \"Chicago\"],\n        \"Canada\": [\"Toronto\", \"Montreal\", \"Vancouver\"],\n        \"Pakistan\": [\"Karachi\", \"Lahore\", \"Islamabad\"],\n    }\n    \n    def change_textbox(choice):\n        if choice == \"short\":\n            return gr.Textbox(lines=2, visible=True), gr.Button(interactive=True)\n        elif choice == \"long\":\n            return gr.Textbox(lines=8, visible=True, value=\"Lorem ipsum dolor sit amet\"), gr.Button(interactive=True)\n        else:\n            return gr.Textbox(visible=False), gr.Button(interactive=False)\n    \n    with gr.Blocks() as demo:\n        radio = gr.Radio(\n            [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n        )\n        text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)\n    \n        with gr.Row():\n            num = gr.Number(minimum=0, maximum=100, label=\"input\")\n            out = gr.Number(label=\"output\")\n        m", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "x(lines=2, interactive=True, show_copy_button=True)\n    \n        with gr.Row():\n            num = gr.Number(minimum=0, maximum=100, label=\"input\")\n            out = gr.Number(label=\"output\")\n        minimum_slider = gr.Slider(0, 100, 0, label=\"min\")\n        maximum_slider = gr.Slider(0, 100, 100, label=\"max\")\n        submit_btn = gr.Button(\"Submit\", variant=\"primary\")\n    \n        with gr.Row():\n            country = gr.Dropdown(list(countries_cities_dict.keys()), label=\"Country\")\n            cities = gr.Dropdown([], label=\"Cities\")\n        @country.change(inputs=country, outputs=cities)\n        def update_cities(country):\n            cities = list(countries_cities_dict[country])\n            return gr.Dropdown(choices=cities, value=cities[0], interactive=True)\n    \n        def reset_bounds(minimum, maximum):\n            return gr.Number(minimum=minimum, maximum=maximum)\n    \n        radio.change(fn=change_textbox, inputs=radio, outputs=[text, submit_btn])\n        gr.on(\n            [minimum_slider.change, maximum_slider.change],\n            reset_bounds,\n            [minimum_slider, maximum_slider],\n            outputs=num,\n        )\n        num.submit(lambda x: x, num, out)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Radio component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Radio.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Radio. Uses event data gradio.SelectData to carry `value` referring to the label of the Radio, and `selected` to refer to state of the Radio. See EventData documentation on how to use this event data  \n`Radio.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Radio changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Radio.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Radio.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nLi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": " function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "will show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions ar", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-rende", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "   key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "The gr.SelectData class is a subclass of gr.EventData that specifically\ncarries information about the `.select()` event. When gr.SelectData is added\nas a type hint to an argument of an event listener method, a gr.SelectData\nobject will automatically be passed as the value of that argument. The\nattributes of this object contains information about the event that triggered\nthe listener.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "import gradio as gr\n    \n    with gr.Blocks() as demo:\n        table = gr.Dataframe([[1, 2, 3], [4, 5, 6]])\n        gallery = gr.Gallery([(\"cat.jpg\", \"Cat\"), (\"dog.jpg\", \"Dog\")])\n        textbox = gr.Textbox(\"Hello World!\")\n        statement = gr.Textbox()\n    \n        def on_select(evt: gr.SelectData):\n            return f\"You selected {evt.value} at {evt.index} from {evt.target}\"\n    \n        table.select(on_select, None, statement)\n        gallery.select(on_select, None, statement)\n        textbox.select(on_select, None, statement)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the selected item. Is a tuple if the component is two dimensional\nor selection is a range.\n\n\n    \n    \n    value: Any\n\nThe value of the selected item.\n\n\n    \n    \n    row_value: list[float | str]\n\nThe value of the entire row that the selected item belongs to, as a 1-D list.\nOnly implemented for the `Dataframe` component, returns None for other\ncomponents.\n\n\n    \n    \n    col_value: list[float | str]\n\nThe value of the entire column that the selected item belongs to, as a 1-D\nlist. Only implemented for the `Dataframe` component, returns None for other\ncomponents.\n\n\n    \n    \n    selected: bool\n\nTrue if the item was selected, False if deselected.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "gallery_selectionstictactoe\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np with gr.Blocks() as demo:\nimgs = gr.State() gallery = gr.Gallery(allow_preview=False) def\ndeselect_images(): return gr.Gallery(selected_index=None) def\ngenerate_images(): images = [] for _ in range(9): image = np.ones((100, 100,\n3), dtype=np.uint8) * np.random.randint( 0, 255, 3 ) image is a solid single\ncolor images.append(image) return images, images demo.load(generate_images,\nNone, [gallery, imgs]) with gr.Row(): selected = gr.Number(show_label=False)\ndarken_btn = gr.Button(\"Darken selected\") deselect_button =\ngr.Button(\"Deselect\") deselect_button.click(deselect_images, None, gallery)\ndef get_select_index(evt: gr.SelectData): return evt.index\ngallery.select(get_select_index, None, selected) def darken_img(imgs, index):\nindex = int(index) imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\nreturn imgs, imgs darken_btn.click(darken_img, [imgs, selected], [imgs,\ngallery]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    \n    with gr.Blocks() as demo:\n        imgs = gr.State()\n        gallery = gr.Gallery(allow_preview=False)\n    \n        def deselect_images():\n            return gr.Gallery(selected_index=None)\n    \n        def generate_images():\n            images = []\n            for _ in range(9):\n                image = np.ones((100, 100, 3), dtype=np.uint8) * np.random.randint(\n                    0, 255, 3\n                )  image is a solid single color\n                images.append(image)\n            return images, images\n    \n        demo.load(generate_images, None, [gallery, imgs])\n    \n        with gr.Row():\n            selected = gr.Number(show_label=False)\n            darken_btn = gr.Button(\"Darken selected\")\n        deselect_button = gr.Button(\"Deselect\")\n    \n        deselect_button.click(deselect_images, None, gallery)\n    \n        def get_select_index(evt: gr.SelectData):\n            return evt.index\n    \n  ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "  deselect_button = gr.Button(\"Deselect\")\n    \n        deselect_button.click(deselect_images, None, gallery)\n    \n        def get_select_index(evt: gr.SelectData):\n            return evt.index\n    \n        gallery.select(get_select_index, None, selected)\n    \n        def darken_img(imgs, index):\n            index = int(index)\n            imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\n            return imgs, imgs\n    \n        darken_btn.click(darken_img, [imgs, selected], [imgs, gallery])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr with gr.Blocks() as demo: turn =\ngr.Textbox(\"X\", interactive=False, label=\"Turn\") board =\ngr.Dataframe(value=[[\"\", \"\", \"\"]] * 3, interactive=False, type=\"array\") def\nplace(board: list[list[int]], turn, evt: gr.SelectData): if evt.value: return\nboard, turn board[evt.index[0]][evt.index[1]] = turn turn = \"O\" if turn == \"X\"\nelse \"X\" return board, turn board.select(place, [board, turn], [board, turn],\nshow_progress=\"hidden\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    with gr.Blocks() as demo:\n        turn = gr.Textbox(\"X\", interactive=False, label=\"Turn\")\n        board = gr.Dataframe(value=[[\"\", \"\", \"\"]] * 3, interactive=False, type=\"array\")\n    \n        def place(board: list[list[int]], turn, evt: gr.SelectData):  \n            if evt.value:\n                return board, turn\n            board[evt.index[0]][evt.index[1]] = turn\n            turn = \"O\" if turn == \"X\" else \"X\"\n            return board, turn\n    \n        board.select(place, [board, turn], [board, turn], show_progress=\"hidden\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "Creates a component to displays a base image and colored annotations on top\nof that image. Annotations can take the from of rectangles (e.g. object\ndetection) or masks (e.g. image segmentation). As this component does not\naccept user input, it is rarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "**As input component** : Passes its value as a `tuple` consisting of a\n`str` filepath to a base image and `list` of annotations. Each annotation\nitself is `tuple` of a mask (as a `str` filepath to image) and a `str` label.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: tuple[str, list[tuple[str, str]]] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a a tuple of a base image and list of\nannotations: a `tuple[Image, list[Annotation]]`. The `Image` itself can be\n`str` filepath, `numpy.ndarray`, or `PIL.Image`. Each `Annotation` is a\n`tuple[Mask, str]`. The `Mask` can be either a `tuple` of 4 `int`'s\nrepresenting the bounding box coordinates (x1, y1, x2, y2), or 0-1 confidence\nmask in the form of a `numpy.ndarray` of the same shape as the image, while\nthe second element of the `Annotation` tuple is a `str` label.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> tuple[np.ndarray | PIL.Image.Image | str, list[tuple[np.ndarray | tuple[int, int, int, int], str]]] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: tuple[np.ndarray | PIL.Image.Image | str, list[tuple[np.ndarray | tuple[int, int, int, int], str]]] | None\n\ndefault `= None`\n\nTuple of base image and list of (annotation, label) pairs.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFormat used to save images before it is returned to the front end, such as\n'jpeg' or 'png'. This parameter only takes effect when the base image is\nreturned from the prediction function as a numpy array or a PIL Image. The\nformat should be supported by the PIL library.\n\n\n    \n    \n    show_legend: bool\n\ndefault `= True`\n\nIf True, will show a legend of the annotations.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfile or numpy array, but will affect the displayed image.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfile or numpy array, but will affect the displayed image.\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nA dictionary mapping labels to colors. The colors must be specified as hex\ncodes.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "ct otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nRelative width compared to adjacent Components in a Row. For example, if\nComponent A has scale=2, and Component B has scale=1, A will be twice as wide\nas B. Should be an integer.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nMinimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a button to allow the image to be viewed in fullscreen\nmode.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.AnnotatedImage` | \"annotatedimage\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "image_segmentation\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np import random with\ngr.Blocks() as demo: section_labels = [ \"apple\", \"banana\", \"carrot\", \"donut\",\n\"eggplant\", \"fish\", \"grapes\", \"hamburger\", \"ice cream\", \"juice\", ] with\ngr.Row(): num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\nnum_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\") with\ngr.Row(): img_input = gr.Image() img_output = gr.AnnotatedImage(\ncolor_map={\"banana\": \"a89a00\", \"carrot\": \"ffae00\"} ) section_btn =\ngr.Button(\"Identify Sections\") selected_section = gr.Textbox(label=\"Selected\nSection\") def section(img, num_boxes, num_segments): sections = [] for a in\nrange(num_boxes): x = random.randint(0, img.shape[1]) y = random.randint(0,\nimg.shape[0]) w = random.randint(0, img.shape[1] - x) h = random.randint(0,\nimg.shape[0] - y) sections.append(((x, y, x + w, y + h), section_labels[a]))\nfor b in range(num_segments): x = random.randint(0, img.shape[1]) y =\nrandom.randint(0, img.shape[0]) r = random.randint(0, min(x, y, img.shape[1] -\nx, img.shape[0] - y)) mask = np.zeros(img.shape[:2]) for i in\nrange(img.shape[0]): for j in range(img.shape[1]): dist_square = (i - y) ** 2\n+ (j - x) ** 2 if dist_square < r**2: mask[i, j] = round((r**2 - dist_square)\n/ r**2 * 4) / 4 sections.append((mask, section_labels[b + num_boxes])) return\n(img, sections) section_btn.click(section, [img_input, num_boxes,\nnum_segments], img_output) def select_section(evt: gr.SelectData): return\nsection_labels[evt.index] img_output.select(select_section, None,\nselected_section) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    import random\n    \n    with gr.Blocks() as demo:\n        section_labels = [\n            \"apple\",\n            \"banana\",\n            \"carrot\",\n            \"donut\",\n            \"eggplant\",\n            \"fish\",\n            \"grapes\",\n            \"hamburger\",\n            \"ice cream\",\n            \"juice\",\n        ]\n    \n        w", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "        \"carrot\",\n            \"donut\",\n            \"eggplant\",\n            \"fish\",\n            \"grapes\",\n            \"hamburger\",\n            \"ice cream\",\n            \"juice\",\n        ]\n    \n        with gr.Row():\n            num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\n            num_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\")\n    \n        with gr.Row():\n            img_input = gr.Image()\n            img_output = gr.AnnotatedImage(\n                color_map={\"banana\": \"a89a00\", \"carrot\": \"ffae00\"}\n            )\n    \n        section_btn = gr.Button(\"Identify Sections\")\n        selected_section = gr.Textbox(label=\"Selected Section\")\n    \n        def section(img, num_boxes, num_segments):\n            sections = []\n            for a in range(num_boxes):\n                x = random.randint(0, img.shape[1])\n                y = random.randint(0, img.shape[0])\n                w = random.randint(0, img.shape[1] - x)\n                h = random.randint(0, img.shape[0] - y)\n                sections.append(((x, y, x + w, y + h), section_labels[a]))\n            for b in range(num_segments):\n                x = random.randint(0, img.shape[1])\n                y = random.randint(0, img.shape[0])\n                r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))\n                mask = np.zeros(img.shape[:2])\n                for i in range(img.shape[0]):\n                    for j in range(img.shape[1]):\n                        dist_square = (i - y) ** 2 + (j - x) ** 2\n                        if dist_square < r**2:\n                            mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4\n                sections.append((mask, section_labels[b + num_boxes]))\n            return (img, sections)\n    \n        section_btn.click(section, [img_input, num_boxes, num_segments], img_output)\n    \n        def select_section(evt: gr.SelectData):\n            return section_labels[evt.index]\n    \n        img_output.select(select_s", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "ick(section, [img_input, num_boxes, num_segments], img_output)\n    \n        def select_section(evt: gr.SelectData):\n            return section_labels[evt.index]\n    \n        img_output.select(select_section, None, selected_section)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe AnnotatedImage component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`AnnotatedImage.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the AnnotatedImage. Uses event data gradio.SelectData to carry `value` referring to the label of the AnnotatedImage, and `selected` to refer to state of the AnnotatedImage. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (d", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "ault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return sho", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "Interface is Gradio's main high-level class, and allows you to create a\nweb-based GUI / demo around a machine learning model (or any Python function)\nin a few lines of code. You must specify three parameters: (1) the function to\ncreate a GUI for (2) the desired input components and (3) the desired output\ncomponents. Additional parameters can be used to control the appearance and\nbehavior of the demo.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "import gradio as gr\n    \n    def image_classifier(inp):\n        return {'cat': 0.3, 'dog': 0.7}\n    \n    demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\")\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    fn: Callable\n\nthe function to wrap an interface around. Often a machine learning model's\nprediction function. Each parameter of the function corresponds to one input\ncomponent, and the function should return a single value or a tuple of values,\nwith each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of input components should match the number of parameters in fn. If\nset to None, then only the output components will be displayed.\n\n\n    \n    \n    outputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of output components should match the number of values returned by\nfn. If set to None, then only the input components will be displayed.\n\n\n    \n    \n    examples: list[Any] | list[list[Any]] | str | None\n\ndefault `= None`\n\nsample inputs for the function; if provided, appear below the UI components\nand can be clicked to populate the interface. Should be nested list, in which\nthe outer list consists of samples and each inner list consists of an input\ncorresponding to each input component. A string path to a directory of\nexamples can also be provided, but it should be within the directory with the\npython file running the gradio app. If there are multiple input components and\na directory is provided, a log.csv file must be present in the directory to\nlink corresponding inputs.\n\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES\nenvironment variable, which should be either \"true\" or \"false\". In HuggingFace\nSpaces, this parameter defaults to True (as long as `fn` and `outputs` are\nalso provided). Note that examples are cached separately from Gradio's queue()\nso certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will\nnot be displayed in Gradio's UI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare cached at app launch. If None, will use the GRADIO_CACHE_MODE environment\nvariable if defined, or default to \"eager\". In HuggingFace Spaces, this\nparameter defaults to \"eager\" except for ZeroGPU Spaces, in which case it\ndefaults to \"lazy\".\n\n\n    \n    \n    examples_per_page: int\n\ndefault `= 10`\n\nif examples are provided, how many to display per page.\n\n\n    \n    \n    example_labels: list[str] | None\n\ndefault `= None`\n\na list of labels for each example. If provided, the length of this list should\nbe the same as the number of examples, and these labels will be used in the UI\ninstead of rendering the example values.\n\n\n    \n    \n    preload_example: int | Literal[False]\n\ndefault `= False`\n\nIf an integer is provided (and examples are being cached), the example at that\nindex in the examples list will be preloaded when the Gradio app is first\nloaded. If False, no example will be preloaded.\n\n\n    \n    \n    live: bool\n\ndefault `= False`\n\nwhether the interface should automatically rerun if any of the inputs change.\n\n\n    \n    \n    title: str | I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": " I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n    \n    \n    description: str | None\n\ndefault `= None`\n\na description for the interface; if provided, appears above the input and\noutput components and beneath the title in regular font. Accepts Markdown and\nHTML content.\n\n\n    \n    \n    article: str | None\n\ndefault `= None`\n\nan expanded article explaining the interface; if provided, appears below the\ninput and output components in regular font. Accepts Markdown and HTML\ncontent. If it is an HTTP(S) link to a downloadable remote file, the content\nof this file is displayed.\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\na Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    flagging_mode: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\none of \"never\", \"auto\", or \"manual\". If \"never\" or \"auto\", users will not see\na button to flag an input and output. If \"manual\", users will see a button to\nflag. If \"auto\", every input the user submits will be automatically flagged,\nalong with the generated output. If \"manual\", both the input and outputs are\nflagged when the user clicks flag button. This parameter can be set with\nenvironmental variable GRADIO_FLAGGING_MODE; otherwise defaults to \"manual\".\n\n\n    \n    \n    flagging_options: list[str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nif provided, allows user to select from the list of options when flagging.\nOnly applies if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "es if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored in the flagging CSV; or\nit can be a list of strings [\"X\", \"Y\"], in which case the values will be the\nlist of strings and the labels will [\"Flag as X\", \"Flag as Y\"], etc.\n\n\n    \n    \n    flagging_dir: str\n\ndefault `= \".gradio/flagged\"`\n\npath to the the directory where flagged data is stored. If the directory does\nnot exist, it will be created.\n\n\n    \n    \n    flagging_callback: FlaggingCallback | None\n\ndefault `= None`\n\neither None or an instance of a subclass of FlaggingCallback which will be\ncalled when a sample is flagged. If set to None, an instance of\ngradio.flagging.CSVLogger will be created and logs will be saved to a local\nCSV file in flagging_dir. Default to None.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nif True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nthe maximum number of inputs to batch together if this is called from the\nqueue (only relevant if batch=True)\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show the prediction endpoint in the \"view API\" page of the Gradio\napp, or in the \".view_api()\" method of the Gradio clients. Unlike setting\napi_name to False, setting show_api to False will still allow downstream apps\nas well as the Clients to use this event.\n\n\n    \n    \n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": " in the \".view_api()\" method of the Gradio clients. Unlike setting\napi_name to False, setting show_api to False will still allow downstream apps\nas well as the Clients to use this event.\n\n\n    \n    \n    api_name: str | Literal[False] | None\n\ndefault `= \"predict\"`\n\ndefines how the prediction endpoint appears in the API docs. Can be a string,\nNone, or False. If set to a string, the endpoint will be exposed in the API\ndocs with the given name. If None, the name of the prediction function will be\nused as the API endpoint. If False, the endpoint will not be exposed in the\nAPI docs and downstream apps (including those that `gr.load` this app) will\nnot be able to use this prediction endpoint.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    allow_duplication: bool\n\ndefault `= False`\n\nif True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nif set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`.queue()`, which itself is 1 by default).\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n    \n    \n    css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo web", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a code string. The custom js should be in the form of a single js\nfunction. This function will automatically be executed when the page loads.\nFor more flexibility, use the head parameter to insert js inside <script>\ntags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html code to insert into the head of the demo webpage. This can be used\nto add custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n\n    \n    \n    head_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` parameter is also set, the html from `head` will\nbe included first.\n\n\n    \n    \n    additional_inputs: str | Component | list[str | Component] | None\n\ndefault `= None`\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThese components will be rendered in an accordion below the main input\ncomponents. By default, no additional input components will be displayed.\n\n\n    \n    \n    additional_inputs_accordion: str | Accordion | None\n\ndefault `= None`\n\nif a string is provided, this is the label of the `gr.Accordion` to use to\ncontain additional inputs. A `gr.Accordion` object can be provided as well to\nconfigure other properties of the container holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    submit_btn: ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ntainer holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    submit_btn: str | Button\n\ndefault `= \"Submit\"`\n\nthe button to use for submitting inputs. Defaults to a `gr.Button(\"Submit\",\nvariant=\"primary\")`. This parameter does not apply if the Interface is output-\nonly, in which case the submit button always displays \"Generate\". Can be set\nto a string (which becomes the button label) or a `gr.Button` object (which\nallows for more customization).\n\n\n    \n    \n    stop_btn: str | Button\n\ndefault `= \"Stop\"`\n\nthe button to use for stopping the interface. Defaults to a `gr.Button(\"Stop\",\nvariant=\"stop\", visible=False)`. Can be set to a string (which becomes the\nbutton label) or a `gr.Button` object (which allows for more customization).\n\n\n    \n    \n    clear_btn: str | Button | None\n\ndefault `= \"Clear\"`\n\nthe button to use for clearing the inputs. Defaults to a `gr.Button(\"Clear\",\nvariant=\"secondary\")`. Can be set to a string (which becomes the button label)\nor a `gr.Button` object (which allows for more customization). Can be set to\nNone, which hides the button.\n\n\n    \n    \n    delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\na tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "gress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    fill_width: bool\n\ndefault `= False`\n\nwhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width.\n\n\n    \n    \n    allow_flagging: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= 30`\n\nThe time limit for the stream to run. Default is 30 seconds. Parameter only\nused for streaming images or audio if the interface is live and the input\ncomponents are set to \"streaming=True\".\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for streaming images or audio if\nthe interface is live and the input components are set to \"streaming=True\".\n\n\n    \n    \n    deep_link: str | DeepLinkButton | bool | None\n\ndefault `= None`\n\na string or `gr.DeepLinkButton` object that creates a unique URL you can use\nto share your app and all components **as they currently are** with others.\nAutomatically enabled on Hugging Face Spaces unless explicitly set to False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "hello_worldhello_world_2hello_world_3\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name): return \"Hello \" + name + \"!\"\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\") if __name__\n== \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    \n    def greet(name):\n        return \"Hello \" + name + \"!\"\n    \n    \n    demo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, intensity): return \"Hello, \" +\nname + \"!\" * intensity demo = gr.Interface( fn=greet, inputs=[\"text\",\ngr.Slider(value=2, minimum=1, maximum=10, step=1)],\noutputs=[gr.Textbox(label=\"greeting\", lines=3)], ) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, intensity):\n        return \"Hello, \" + name + \"!\" * intensity\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", gr.Slider(value=2, minimum=1, maximum=10, step=1)],\n        outputs=[gr.Textbox(label=\"greeting\", lines=3)],\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, is_morning, temperature):\nsalutation = \"Good morning\" if is_morning else \"Good evening\" greeting =\nf\"{salutation} {name}. It is {temperature} degrees today\" celsius =\n(temperature - 32) * 5 / 9 return greeting, round(celsius, 2) demo =\ngr.Interface( fn=greet, inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\noutputs=[\"text\", \"number\"], ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, is_morning, temperature):\n        salutation = \"Good morning\" if is_morning else \"Good evening\"\n        greeting = f\"{salutation} {name}. It is {temperature} degrees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 10", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "grees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n        outputs=[\"text\", \"number\"],\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.launch(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nLaunches a simple web server that serves the demo. Can also be used to create\na public link used by anyone to access the demo from their browser by setting\nshare=True.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    def reverse(text):\n        return text[::-1]\n    demo = gr.Interface(reverse, \"text\", \"text\")\n    demo.launch(share=True, auth=(\"username\", \"password\"))\n\nParameters \u25bc\n\n\n    \n    \n    inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the de", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "pp inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the default\nbrowser.\n\n\n    \n    \n    share: bool | None\n\ndefault `= None`\n\nwhether to create a publicly shareable link for the gradio app. Creates an SSH\ntunnel to make your UI accessible from anywhere. If not provided, it is set to\nFalse by default every time, except when running in Google Colab. When\nlocalhost is not accessible (e.g. Google Colab), setting share=False is not\nsupported. Can be set by environment variable GRADIO_SHARE=True.\n\n\n    \n    \n    debug: bool\n\ndefault `= False`\n\nif True, blocks the main thread from running. If running in Google Colab, this\nis needed to print the errors in the cell output.\n\n\n    \n    \n    max_threads: int\n\ndefault `= 40`\n\nthe maximum number of total threads that the Gradio app can generate in\nparallel. The default is inherited from the starlette library (currently 40).\n\n\n    \n    \n    auth: Callable[[str, str], bool] | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access app. Can also provide function that takes username and\npassword and returns True if valid login.\n\n\n    \n    \n    auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page.\n\n\n    \n    \n    prevent_thread_lock: bool\n\ndefault `= False`\n\nBy default, the gradio app blocks the main thread while the server is running.\nIf set to True, the gradio app will not block and the gradio server will\nterminate as soon as the script finishes.\n\n\n    \n    \n    show_error: bool\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= No", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= None`\n\nto make app accessible on local network, set this to \"0.0.0.0\". Can be set by\nenvironment variable GRADIO_SERVER_NAME. If None, will use \"127.0.0.1\".\n\n\n    \n    \n    server_port: int | None\n\ndefault `= None`\n\nwill start gradio app on this port (if available). Can be set by environment\nvariable GRADIO_SERVER_PORT. If None, will search for an available port\nstarting at 7860.\n\n\n    \n    \n    height: int\n\ndefault `= 500`\n\nThe height in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    width: int | str\n\ndefault `= \"100%\"`\n\nThe width in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    favicon_path: str | Path | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for the web page.\n\n\n    \n    \n    ssl_keyfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the private key file to\ncreate a local server running on https.\n\n\n    \n    \n    ssl_certfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the signed certificate for\nhttps. Needs to be provided if ssl_keyfile is provided.\n\n\n    \n    \n    ssl_keyfile_password: str | None\n\ndefault `= None`\n\nIf a password is provided, will use this with the ssl certificate for https.\n\n\n    \n    \n    ssl_verify: bool\n\ndefault `= True`\n\nIf False, skips certificate validation which allows self-signed certificates\nto be used.\n\n\n    \n    \n    quiet: bool\n\ndefault `= False`\n\nIf True, suppresses most print statements.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent di", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": ": bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is allowed to\nserve. Must be absolute paths. Warning: if you provide directories, any files\nin these directories or their subdirectories are accessible to all users of\nyour app. Can be set by comma separated environment variable\nGRADIO_ALLOWED_PATHS. These files are generally assumed to be secure and will\nbe displayed in the browser when possible.\n\n\n    \n    \n    blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is not allowed to\nserve (i.e. users of your app are not allowed to access). Must be absolute\npaths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default. Can be set by comma separated\nenvironment variable GRADIO_BLOCKED_PATHS.\n\n\n    \n    \n    root_path: str | None\n\ndefault `= None`\n\nThe root path (or \"mount point\") of the application, if it's not served from\nthe root (\"/\") of the domain. Often used when the application is behind a\nreverse proxy that forwards requests to the application. For example, if the\napplication is served at \"https://example.com/myapp\", the `root_path` should\nbe set to \"/myapp\". A full URL beginning with http:// or https:// can be\nprovided, which will be used as the root path in its entirety. Can be set by\nenvironment variable GRADIO_ROOT_PATH. Defaults to \"\".\n\n\n    \n    \n    app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capac", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capacity to reduce memory usage when using gradio.State or returning\nupdated components from functions. Defaults to 10000.\n\n\n    \n    \n    share_server_address: str | None\n\ndefault `= None`\n\nUse this to specify a custom FRP server and port for sharing Gradio apps (only\napplies if share=True). If not provided, will use the default FRP server at\nhttps://gradio.live. See https://github.com/huggingface/frp for more\ninformation.\n\n\n    \n    \n    share_server_protocol: Literal['http', 'https'] | None\n\ndefault `= None`\n\nUse this to specify the protocol to use for the share links. Defaults to\n\"https\", unless a custom share_server_address is provided, in which case it\ndefaults to \"http\". If you are using a custom share_server_address and want to\nuse https, you must set this to \"https\".\n\n\n    \n    \n    share_server_tls_certificate: str | None\n\ndefault `= None`\n\nThe path to a TLS certificate file to use when connecting to a custom share\nserver. This parameter is not used with the default FRP server at\nhttps://gradio.live. Otherwise, you must provide a valid TLS certificate file\n(e.g. a \"cert.pem\") relative to the current working directory, or the\nconnection will not use TLS encryption, which is insecure.\n\n\n    \n    \n    auth_dependency: Callable[[fastapi.Request], str | None] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the app (they will see a 401 Unauthorized response). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any ", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ed with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    enable_monitoring: bool | None\n\ndefault `= None`\n\nEnables traffic monitoring of the app through the /monitoring endpoint. By\ndefault is None, which enables this endpoint. If explicitly True, will also\nprint the monitoring URL to the console. If False, will disable monitoring\naltogether.\n\n\n    \n    \n    strict_cors: bool\n\ndefault `= True`\n\nIf True, prevents external domains from making requests to a Gradio server\nrunning on localhost. If False, allows requests to localhost that originate\nfrom localhost but also, crucially, from \"null\". This parameter should\nnormally be True to prevent CSRF attacks but may need to be False when\nembedding a *locally-running Gradio app* using web components.\n\n\n    \n    \n    node_server_name: str | None\n\ndefault `= None`\n\n\n    \n    \n    node_port: int | None\n\ndefault `= None`\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 20+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n    \n    \n    pwa: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an installable PWA (Progressive Web\nApp). If set to None (default behavior), then the PWA feature will be enabled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_M", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "   mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_MCP_SERVER environment variable will be used to determine if the MCP\nserver should be enabled (which is \"True\" on Hugging Face Spaces).\n\n\n    \n    \n    i18n: I18n | None\n\ndefault `= None`\n\nAn I18n instance containing custom translations, which are used to translate\nstrings in our components (e.g. the labels of components or Markdown strings).\nThis feature can only be used to translate static text in the frontend, not\nvalues in the backend.\n\n", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.load(block, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%2", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Interface initially loads in the browser.\n\nParameters \u25bc\n\n\n    \n    \n    block: Block | None\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple correspondin", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": " prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime ", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "w the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click met", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "f other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api w", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ew_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.from_pipeline(pipeline, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nClass method that constructs an Interface from a Hugging Face\ntransformers.Pipeline or diffusers.DiffusionPipeline object. The input and\noutput components are automatically determined from the pipeline.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Co", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    from transformers import pipeline\n    pipe = pipeline(\"image-classification\")\n    gr.Interface.from_pipeline(pipe).launch()\n\nParameters \u25bc\n\n\n    \n    \n    pipeline: Pipeline | DiffusionPipeline\n\nthe pipeline object to use.\n\n", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ne object to use.\n\n", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.integrate(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20I", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nA catch-all method for integrating with other libraries. This method should be\nrun after launch()\n\nParameters \u25bc\n\n\n    \n    \n    comet_ml: <class 'inspect._empty'>\n\ndefault `= None`\n\nIf a comet_ml Experiment object is provided, will integrate with the\nexperiment and appear on Comet dashboard\n\n\n    \n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "\n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n\nIf the mlflow module is provided, will integrate with the experiment and\nappear on ML Flow dashboard\n\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.queue(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nBy enabling the queue you can control when users know their position in the\nqueue, and set a limit on maximum number of events allowed.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())\n    demo.queue(max_size=20)\n    demo.launch()\n\nParameters \u25bc\n\n\n    \n    \n    status_update_rate: float | Literal['auto']\n\ndefault `= \"auto\"`\n\nIf \"auto\", Queue will send status estimations to all clients whenever a job is\nfinished. Otherwise Queue will send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowin", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": " send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowing requests made\ndirectly to those endpoints to skip the queue.\n\n\n    \n    \n    max_size: int | None\n\ndefault `= None`\n\nThe maximum number of events the queue will store at any given moment. If the\nqueue is full, new events will not be added and a user will receive a message\nsaying that the queue is full. If None, the queue size will be unlimited.\n\n\n    \n    \n    default_concurrency_limit: int | None | Literal['not_set']\n\ndefault `= \"not_set\"`\n\nThe default value of `concurrency_limit` to use for event listeners that don't\nspecify a value. Can be set by environment variable\nGRADIO_DEFAULT_CONCURRENCY_LIMIT. Defaults to 1 if not set otherwise.\n\n", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "Displays an interactive table of parameters and their descriptions and\ndefault values with syntax highlighting. For each parameter, the user should\nprovide a type (e.g. a `str`), a human-readable description, and a default\nvalue. As this component does not accept user input, it is rarely used as an\ninput component. Internally, this component is used to display the parameters\nof components in the Custom Component Gallery (https://www.gradio.app/custom-\ncomponents/gallery).\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "**As input component** : (Rarely used) passes value as a `dict[str, dict]`.\nThe key in the outer dictionary is the parameter name, while the inner\ndictionary has keys \"type\", \"description\", and \"default\" for each parameter.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: dict[str, Parameter]\n    )\n    \t...\n\n  \n\n**As output component** : Expects value as a `dict[str, dict]`. The key in\nthe outer dictionary is the parameter name, while the inner dictionary has\nkeys \"type\", \"description\", and \"default\" for each parameter.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> dict[str, Parameter]\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: dict[str, Parameter] | None\n\ndefault `= None`\n\nA dictionary of dictionaries. The key in the outer dictionary is the parameter\nname, while the inner dictionary has keys \"type\", \"description\", and \"default\"\nfor each parameter. Markdown links are supported in \"description\".\n\n\n    \n    \n    language: Literal['python', 'typescript']\n\ndefault `= \"python\"`\n\nThe language to display the code in. One of \"python\" or \"typescript\".\n\n\n    \n    \n    linkify: list[str] | None\n\ndefault `= None`\n\nA list of strings to linkify. If any of these strings is found in the\ndescription, it will be rendered as a link.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "er()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    header: str | None\n\ndefault `= \"Parameters\"`\n\nThe header to display above the table of parameters, also includes a toggle\nbutton that closes/opens all details at once. If None, no header will be\ndisplayed.\n\n\n    \n    \n    anchor_links: bool | str\n\ndefault `= False`\n\nIf True, creates anchor links for each parameter that can be used to link\ndirectly to that parameter. If a string, creates anchor links with the given\nstring as the prefix to prevent conflicts with other ParamViewer components.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ParamViewer` | \"paramviewer\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ParamViewer component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ParamViewer.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the ParamViewer changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`ParamViewer.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the ParamViewer.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, o", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "on returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nsh", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method t", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "Creates a plot component to display various kinds of plots (matplotlib,\nplotly, altair, or bokeh plots are supported). As this component does not\naccept user input, it is rarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "**As input component** : (Rarely used) passes the data displayed in the\nplot as an PlotData dataclass, which includes the plot information as a JSON\nstring, as well as the type of chart and the plotting library.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: PlotData | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects plot data in one of these formats: a\nmatplotlib.Figure, bokeh.Model, plotly.Figure, or altair.Chart object.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> Any\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: Any | None\n\ndefault `= None`\n\nOptionally, supply a default plot object to display, must be a matplotlib,\nplotly, altair, or bokeh figure, or a callable. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFile format in which to send matplotlib plots to the front end, such as 'jpg'\nor 'png'.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a cert", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "o top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Plot` | \"plot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "blocks_kinematicsstock_forecast\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd import numpy as np import gradio as gr def\nplot(v, a): g = 9.81 theta = a / 180 * 3.14 tmax = ((2 * v) * np.sin(theta)) /\ng timemat = tmax * np.linspace(0, 1, 40) x = (v * timemat) * np.cos(theta) y =\n((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2)) df =\npd.DataFrame({\"x\": x, \"y\": y}) return df demo = gr.Blocks() with demo:\ngr.Markdown( r\"Let's do some kinematics! Choose the speed and angle to see the\ntrajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n) with gr.Row(): speed = gr.Slider(1, 30, 25, label=\"Speed\") angle =\ngr.Slider(0, 90, 45, label=\"Angle\") output = gr.LinePlot( x=\"x\", y=\"y\",\noverlay_point=True, tooltip=[\"x\", \"y\"], x_lim=[0, 100], y_lim=[0, 60],\nwidth=350, height=300, ) btn = gr.Button(value=\"Run\") btn.click(plot, [speed,\nangle], output) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import pandas as pd\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot(v, a):\n        g = 9.81\n        theta = a / 180 * 3.14\n        tmax = ((2 * v) * np.sin(theta)) / g\n        timemat = tmax * np.linspace(0, 1, 40)\n    \n        x = (v * timemat) * np.cos(theta)\n        y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))\n        df = pd.DataFrame({\"x\": x, \"y\": y})\n        return df\n    \n    demo = gr.Blocks()\n    \n    with demo:\n        gr.Markdown(\n            r\"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n        )\n    \n        with gr.Row():\n            speed = gr.Slider(1, 30, 25, label=\"Speed\")\n            angle = gr.Slider(0, 90, 45, label=\"Angle\")\n        output = gr.LinePlot(\n            x=\"x\",\n            y=\"y\",\n            overlay_point=True,\n            tooltip=[\"x\", \"y\"],\n            x_lim=[0, 100],\n            y_lim=[0, 60],\n            width=350,\n            height=300,\n        )\n        btn = gr.Button(value=\"Run\")\n ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "erlay_point=True,\n            tooltip=[\"x\", \"y\"],\n            x_lim=[0, 100],\n            y_lim=[0, 60],\n            width=350,\n            height=300,\n        )\n        btn = gr.Button(value=\"Run\")\n        btn.click(plot, [speed, angle], output)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import matplotlib.pyplot as plt import numpy as np import gradio\nas gr def plot_forecast(final_year, companies, noise, show_legend,\npoint_style): start_year = 2020 x = np.arange(start_year, final_year + 1)\nyear_count = x.shape[0] plt_format = ({\"cross\": \"X\", \"line\": \"-\", \"circle\":\n\"o--\"})[point_style] fig = plt.figure() ax = fig.add_subplot(111) for i,\ncompany in enumerate(companies): series = np.arange(0, year_count,\ndtype=float) series = series**2 * (i + 1) series += np.random.rand(year_count)\n* noise ax.plot(x, series, plt_format) if show_legend: plt.legend(companies)\nreturn fig demo = gr.Interface( plot_forecast, [ gr.Radio([2025, 2030, 2035,\n2040], label=\"Project to:\"), gr.CheckboxGroup([\"Google\", \"Microsoft\",\n\"Gradio\"], label=\"Company Selection\"), gr.Slider(1, 100, label=\"Noise Level\"),\ngr.Checkbox(label=\"Show Legend\"), gr.Dropdown([\"cross\", \"line\", \"circle\"],\nlabel=\"Style\"), ], gr.Plot(label=\"forecast\", format=\"png\"), ) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    import matplotlib.pyplot as plt\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot_forecast(final_year, companies, noise, show_legend, point_style):\n        start_year = 2020\n        x = np.arange(start_year, final_year + 1)\n        year_count = x.shape[0]\n        plt_format = ({\"cross\": \"X\", \"line\": \"-\", \"circle\": \"o--\"})[point_style]\n        fig = plt.figure()\n        ax = fig.add_subplot(111)\n        for i, company in enumerate(companies):\n            series = np.arange(0, year_count, dtype=float)\n            series = series**2 * (i + 1)\n            series += np.random.rand(year_count) * noise\n            ax.plot(x, series, plt_format)\n       ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "     series = np.arange(0, year_count, dtype=float)\n            series = series**2 * (i + 1)\n            series += np.random.rand(year_count) * noise\n            ax.plot(x, series, plt_format)\n        if show_legend:\n            plt.legend(companies)\n        return fig\n    \n    demo = gr.Interface(\n        plot_forecast,\n        [\n            gr.Radio([2025, 2030, 2035, 2040], label=\"Project to:\"),\n            gr.CheckboxGroup([\"Google\", \"Microsoft\", \"Gradio\"], label=\"Company Selection\"),\n            gr.Slider(1, 100, label=\"Noise Level\"),\n            gr.Checkbox(label=\"Show Legend\"),\n            gr.Dropdown([\"cross\", \"line\", \"circle\"], label=\"Style\"),\n        ],\n        gr.Plot(label=\"forecast\", format=\"png\"),\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Plot component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`Plot.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Plot changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the fun", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": " how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). Th", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "n the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "teral[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "Constructs a Gradio app automatically from a Hugging Face model/Space repo\nname or a 3rd-party API provider. Note that if a Space repo is loaded, certain\nhigh-level attributes of the Blocks (e.g. custom `css`, `js`, and `head`\nattributes) will not be loaded.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/load", "source_page_title": "Gradio - Load Docs"}, {"text": "import gradio as gr\n    demo = gr.load(\"gradio/question-answering\", src=\"spaces\")\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/load", "source_page_title": "Gradio - Load Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    name: str\n\nthe name of the model (e.g. \"google/vit-base-patch16-224\") or Space (e.g.\n\"flax-community/spanish-gpt2\"). This is the first parameter passed into the\n`src` function. Can also be formatted as {src}/{repo name} (e.g.\n\"models/google/vit-base-patch16-224\") if `src` is not provided.\n\n\n    \n    \n    src: Callable[[str, str | None], Blocks] | Literal['models', 'spaces'] | None\n\ndefault `= None`\n\nfunction that accepts a string model `name` and a string or None `token` and\nreturns a Gradio app. Alternatively, this parameter takes one of two strings\nfor convenience: \"models\" (for loading a Hugging Face model through the\nInference API) or \"spaces\" (for loading a Hugging Face Space). If None, uses\nthe prefix of the `name` parameter to determine `src`.\n\n\n    \n    \n    token: str | None\n\ndefault `= None`\n\noptional token that is passed as the second parameter to the `src` function.\nIf not explicitly provided, will use the HF_TOKEN environment variable or\nfallback to the locally-saved HF token when loading models but not Spaces\n(when loading Spaces, only provide a token if you are loading a trusted\nprivate Space as the token can be read by the Space you are loading). Find\nyour HF tokens here: https://huggingface.co/settings/tokens.\n\n\n    \n    \n    hf_token: str | None\n\ndefault `= None`\n\n\n    \n    \n    accept_token: bool | LoginButton\n\ndefault `= False`\n\nif True, a Textbox component is first rendered to allow the user to provide a\ntoken, which will be used instead of the `token` parameter when calling the\nloaded model or Space. Can also provide an instance of a gr.LoginButton in the\nsame Blocks scope, which allows the user to login with a Hugging Face account\nwhose token will be used instead of the `token` parameter when calling the\nloaded model or Space.\n\n\n    \n    \n    provider: PROVIDER_T | None\n\ndefault `= None`\n\nthe name of the third-party (non-Hugging Face) providers to use for model\ninference (e.g. \"replicate\", \"sambanova\", \"fal-ai\", et", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/load", "source_page_title": "Gradio - Load Docs"}, {"text": "r Space.\n\n\n    \n    \n    provider: PROVIDER_T | None\n\ndefault `= None`\n\nthe name of the third-party (non-Hugging Face) providers to use for model\ninference (e.g. \"replicate\", \"sambanova\", \"fal-ai\", etc). Should be one of the\nproviders supported by `huggingface_hub.InferenceClient`. This parameter is\nonly used when `src` is \"models\"\n\n\n    \n    \n    kwargs: <class 'inspect._empty'>\n\nadditional keyword parameters to pass into the `src` function. If `src` is\n\"models\" or \"Spaces\", these parameters are passed into the `gr.Interface` or\n`gr.ChatInterface` constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/load", "source_page_title": "Gradio - Load Docs"}, {"text": "dict() -> new empty dictionary dict(mapping) -> new dictionary initialized\nfrom a mapping object's (key, value) pairs dict(iterable) -> new dictionary\ninitialized as if via: d = `` for k, v in iterable: d[k] = v dict(__kwargs) -\n> new dictionary initialized with the name=value pairs in the keyword argument\nlist. For example: dict(one=1, two=2)\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dependency", "source_page_title": "Gradio - Dependency Docs"}, {"text": "Set the static paths to be served by the gradio app.  \nStatic files are are served directly from the file system instead of being\ncopied. They are served to users with The Content-Disposition HTTP header set\nto \"inline\" when sending these files to users. This indicates that the file\nshould be displayed directly in the browser window if possible. This function\nis useful when you want to serve files that you know will not be modified\nduring the lifetime of the gradio app (like files used in gr.Examples). By\nsetting static paths, your app will launch faster and it will consume less\ndisk space. Calling this function will set the static paths for all gradio\napplications defined in the same interpreter session until it is called again\nor the session ends.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/set_static_paths", "source_page_title": "Gradio - Set_Static_Paths Docs"}, {"text": "import gradio as gr\n    \n    Paths can be a list of strings or pathlib.Path objects\n    corresponding to filenames or directories.\n    gr.set_static_paths(paths=[\"test/test_files/\"])\n    \n    The example files and the default value of the input\n    will not be copied to the gradio cache and will be served directly.\n    demo = gr.Interface(\n        lambda s: s.rotate(45),\n        gr.Image(value=\"test/test_files/cheetah1.jpg\", type=\"pil\"),\n        gr.Image(),\n        examples=[\"test/test_files/bus.png\"],\n    )\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/set_static_paths", "source_page_title": "Gradio - Set_Static_Paths Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    paths: str | Path | list[str | Path]\n\nfilepath or list of filepaths or directory names to be served by the gradio\napp. If it is a directory name, ALL files located within that directory will\nbe considered static and not moved to the gradio cache. This also means that\nALL files in that directory will be accessible over the network.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/set_static_paths", "source_page_title": "Gradio - Set_Static_Paths Docs"}, {"text": "This function allows you to pass custom warning messages to the user. You\ncan do so simply by writing `gr.Warning('message here')` in your function, and\nwhen that line is executed the custom message will appear in a modal on the\ndemo. The modal is yellow by default and has the heading: \"Warning.\" Queue\nmust be enabled for this behavior; otherwise, the warning will be printed to\nthe console using the `warnings` library.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "import gradio as gr\n    def hello_world():\n        gr.Warning('This is a warning message.')\n        return \"hello world\"\n    with gr.Blocks() as demo:\n        md = gr.Markdown()\n        demo.load(hello_world, inputs=None, outputs=[md])\n    demo.queue().launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    message: str\n\ndefault `= \"Warning issued.\"`\n\nThe warning message to be displayed to the user. Can be HTML, which will be\nrendered in the modal.\n\n\n    \n    \n    duration: float | None\n\ndefault `= 10`\n\nThe duration in seconds that the warning message should be displayed for. If\nNone or 0, the message will be displayed indefinitely until the user closes\nit.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the error message should be displayed in the UI.\n\n\n    \n    \n    title: str\n\ndefault `= \"Warning\"`\n\nThe title to be displayed to the user at the top of the modal.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "blocks_chained_events\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def failure(): raise gr.Error(\"This should\nfail!\") def exception(): raise ValueError(\"Something went wrong\") def\nsuccess(): return True def warning_fn(): gr.Warning(\"This is a warning!\") def\ninfo_fn(): gr.Info(\"This is some info\") with gr.Blocks() as demo:\ngr.Markdown(\"Used in E2E tests of success event trigger. The then event\ncovered in chatbot E2E tests.\" \" Also testing that the status modals show\nup.\") with gr.Row(): result = gr.Textbox(label=\"Result\") result_2 =\ngr.Textbox(label=\"Consecutive Event\") with gr.Row(): success_btn =\ngr.Button(value=\"Trigger Success\") success_btn_2 = gr.Button(value=\"Trigger\nConsecutive Success\") failure_btn = gr.Button(value=\"Trigger Failure\")\nfailure_exception = gr.Button(value=\"Trigger Failure With ValueError\") with\ngr.Row(): trigger_warning = gr.Button(value=\"Trigger Warning\") trigger_info =\ngr.Button(value=\"Trigger Info\") success_btn_2.click(success, None,\nNone).success(lambda: \"First Event Trigered\", None, result).success(lambda:\n\"Consecutive Event Triggered\", None, result_2) success_btn.click(success,\nNone, None).success(lambda: \"Success event triggered\", inputs=None,\noutputs=result) failure_btn.click(failure, None, None).success(lambda: \"Should\nnot be triggered\", inputs=None, outputs=result)\nfailure_exception.click(exception, None, None)\ntrigger_warning.click(warning_fn, None, None) trigger_info.click(info_fn,\nNone, None) if __name__ == \"__main__\": demo.launch(show_error=True)\n\n    \n    \n    import gradio as gr\n    \n    def failure():\n        raise gr.Error(\"This should fail!\")\n    \n    def exception():\n        raise ValueError(\"Something went wrong\")\n    \n    def success():\n        return True\n    \n    def warning_fn():\n        gr.Warning(\"This is a warning!\")\n    \n    def info_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n        ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "fo_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n                    \" Also testing that the status modals show up.\")\n        with gr.Row():\n            result = gr.Textbox(label=\"Result\")\n            result_2 = gr.Textbox(label=\"Consecutive Event\")\n        with gr.Row():\n            success_btn = gr.Button(value=\"Trigger Success\")\n            success_btn_2 = gr.Button(value=\"Trigger Consecutive Success\")\n            failure_btn = gr.Button(value=\"Trigger Failure\")\n            failure_exception = gr.Button(value=\"Trigger Failure With ValueError\")\n        with gr.Row():\n            trigger_warning = gr.Button(value=\"Trigger Warning\")\n            trigger_info = gr.Button(value=\"Trigger Info\")\n    \n            success_btn_2.click(success, None, None).success(lambda: \"First Event Trigered\", None, result).success(lambda: \"Consecutive Event Triggered\", None, result_2)\n            success_btn.click(success, None, None).success(lambda: \"Success event triggered\", inputs=None, outputs=result)\n            failure_btn.click(failure, None, None).success(lambda: \"Should not be triggered\", inputs=None, outputs=result)\n            failure_exception.click(exception, None, None)\n            trigger_warning.click(warning_fn, None, None)\n            trigger_info.click(info_fn, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch(show_error=True)\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "Creates an image component that, as an input, can be used to upload and\nedit images using simple editing tools such as brushes, strokes, cropping, and\nlayers. Or, as an output, this component can be used to display images.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "**As input component** : Passes the uploaded images as an instance of\nEditorValue, which is just a `dict` with keys: 'background', 'layers', and\n'composite'. The values corresponding to 'background' and 'composite' are\nimages, while 'layers' is a `list` of images. The images are of type\n`PIL.Image`, `np.array`, or `str` filepath, depending on the `type` parameter.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: EditorValue | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a EditorValue, which is just a dictionary\nwith keys: 'background', 'layers', and 'composite'. The values corresponding\nto 'background' and 'composite' should be images or None, while `layers`\nshould be a list of images. Images can be of type `PIL.Image`, `np.array`, or\n`str` filepath/URL. Or, the value can be simply a single image (`ImageType`),\nin which case it will be used as the background.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> EditorValue | ImageType | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: EditorValue | ImageType | None\n\ndefault `= None`\n\nOptional initial image(s) to populate the image editor. Should be a dictionary\nwith keys: `background`, `layers`, and `composite`. The values corresponding\nto `background` and `composite` should be images or None, while `layers`\nshould be a list of images. Images can be of type PIL.Image, np.array, or str\nfilepath/URL. Or, the value can be a callable, in which case the function will\nbe called whenever the app loads to set the initial value of the component.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfiles or numpy arrays, but will affect the displayed images. Beware of\nconflicting values with the canvas_size paramter. If the canvas_size is larger\nthan the height, the editing canvas will not fit in the component.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfiles or numpy arrays, but will affect the displayed images. Beware of\nconflicting values with the canvas_size paramter. If the canvas_size is larger\nthan the height, the editing canvas will not fit in the component.\n\n\n    \n    \n    image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F']\n\ndefault `= \"RGBA\"`\n\n\"RGB\" if color, or \"L\" if black and white. See\nhttps://pillow.readthedocs.io/en/stable/handbook/concepts.html for other\nsupported image modes and their meaning.\n\n\n    \n    \n    sources: Iterable[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None\n\ndefault `= ('upload', 'webcam', 'clipboard')`\n\nList of sources that can be used to set the background image. \"upload\" creates\na box where user can drop an image file, \"webc", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "bcam', 'clipboard'] | None\n\ndefault `= ('upload', 'webcam', 'clipboard')`\n\nList of sources that can be used to set the background image. \"upload\" creates\na box where user can drop an image file, \"webcam\" allows user to take snapshot\nfrom their webcam, \"clipboard\" allows users to paste an image from the\nclipboard.\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe format the images are converted to before being passed into the prediction\nfunction. \"numpy\" converts the images to numpy arrays with shape (height,\nwidth, 3) and values from 0 to 255, \"pil\" converts the images to PIL image\nobjects, \"filepath\" passes images as str filepaths to temporary copies of the\nimages.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_download_button: bool\n\ndefault `= True`\n\nIf True, will display button to download image.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Componen", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "ue`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload and edit an image; if False, can only be\nused to display images. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "mponent, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\nCustom text for the upload area. Overrides default upload messages when\nprovided. Accepts new lines and `` to designate a heading.\n\n\n    \n    \n    mirror_webcam: bool | None\n\ndefault `= None`\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    crop_size: tuple[int | float, int | float] | str | None\n\ndefault `= None`\n\nDeprecated. Used to set the `canvas_size` parameter.\n\n\n    \n    \n    transforms: Iterable[Literal['crop', 'resize']] | None\n\ndefault `= ('crop', 'resize')`\n\nThe transforms tools to make available to users. \"crop\" allows the user to\ncrop the image.\n\n\n    \n    \n    eraser: Eraser | None | Literal[False]\n\ndefault `= None`\n\nThe options for the eraser tool in the image editor. Should be an instance of\nthe `gr.Eraser` class, or None to use the default settings. Can also be False\nto hide the eraser tool. See `gr.Eraser` docs.\n\n\n    \n    \n    brush: Brush | None | Literal[False]\n\ndefault `= None`\n\nThe options for the brush tool in the image editor. Should be an instance of\nthe `gr.Brush` class, or None to use the default settings. Can also be False\nto hide the brush tool, which will also hide t", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "The options for the brush tool in the image editor. Should be an instance of\nthe `gr.Brush` class, or None to use the default settings. Can also be False\nto hide the brush tool, which will also hide the eraser tool. See `gr.Brush`\ndocs.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFormat to save image if it does not already have a valid format (e.g. if the\nimage is being returned to the frontend as a numpy array or PIL Image). The\nformat should be supported by the PIL library. This parameter has no effect on\nSVG files.\n\n\n    \n    \n    layers: bool | LayerOptions\n\ndefault `= True`\n\nThe options for the layer tool in the image editor. Can be a boolean or an\ninstance of the `gr.LayerOptions` class. If True, will allow users to add\nlayers to the image. If False, the layers option will be hidden. If an\ninstance of `gr.LayerOptions`, it will be used to configure the layer tool.\nSee `gr.LayerOptions` docs.\n\n\n    \n    \n    canvas_size: tuple[int, int]\n\ndefault `= (800, 800)`\n\nThe initial size of the canvas in pixels. The first value is the width and the\nsecond value is the height. If `fixed_canvas` is `True`, uploaded images will\nbe rescaled to fit the canvas size while preserving the aspect ratio.\nOtherwise, the canvas size will change to match the size of an uploaded image.\n\n\n    \n    \n    fixed_canvas: bool\n\ndefault `= False`\n\nIf True, the canvas size will not change based on the size of the background\nimage and the image will be rescaled to fit (while preserving the aspect\nratio) and placed in the center of the canvas.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will display button to view image in fullscreen mode.\n\n\n    \n    \n    webcam_options: WebcamOptions | None\n\ndefault `= None`\n\nThe options for the webcam tool in the image editor. Can be an instance of the\n`gr.WebcamOptions` class, or None to use the default settings. See\n`gr.WebcamOptions` docs.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "be an instance of the\n`gr.WebcamOptions` class, or None to use the default settings. See\n`gr.WebcamOptions` docs.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ImageEditor` | \"imageeditor\" | Uses default values  \n`gradio.Sketchpad` | \"sketchpad\" | Uses sources=(), brush=Brush(colors=[\"000000\"], color_mode=\"fixed\")  \n`gradio.Paint` | \"paint\" | Uses sources=()  \n`gradio.ImageMask` | \"imagemask\" | Uses brush=Brush(colors=[\"000000\"], color_mode=\"fixed\")  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "image_editor\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import time def sleep(im): time.sleep(5)\nreturn [im[\"background\"], im[\"layers\"][0], im[\"layers\"][1], im[\"composite\"]]\ndef predict(im): return im[\"composite\"] with gr.Blocks() as demo: with\ngr.Row(): im = gr.ImageEditor( type=\"numpy\", crop_size=\"1:1\", ) im_preview =\ngr.Image() n_upload = gr.Number(0, label=\"Number of upload events\", step=1)\nn_change = gr.Number(0, label=\"Number of change events\", step=1) n_input =\ngr.Number(0, label=\"Number of input events\", step=1) im.upload(lambda x: x +\n1, outputs=n_upload, inputs=n_upload) im.change(lambda x: x + 1,\noutputs=n_change, inputs=n_change) im.input(lambda x: x + 1, outputs=n_input,\ninputs=n_input) im.change(predict, outputs=im_preview, inputs=im,\nshow_progress=\"hidden\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import time\n    \n    \n    def sleep(im):\n        time.sleep(5)\n        return [im[\"background\"], im[\"layers\"][0], im[\"layers\"][1], im[\"composite\"]]\n    \n    \n    def predict(im):\n        return im[\"composite\"]\n    \n    \n    with gr.Blocks() as demo:\n        with gr.Row():\n            im = gr.ImageEditor(\n                type=\"numpy\",\n                crop_size=\"1:1\",\n            )\n            im_preview = gr.Image()\n        n_upload = gr.Number(0, label=\"Number of upload events\", step=1)\n        n_change = gr.Number(0, label=\"Number of change events\", step=1)\n        n_input = gr.Number(0, label=\"Number of input events\", step=1)\n    \n        im.upload(lambda x: x + 1, outputs=n_upload, inputs=n_upload)\n        im.change(lambda x: x + 1, outputs=n_change, inputs=n_change)\n        im.input(lambda x: x + 1, outputs=n_input, inputs=n_input)\n        im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ImageEditor component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ImageEditor.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the ImageEditor using the clear button for the component.  \n`ImageEditor.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the ImageEditor changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`ImageEditor.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the ImageEditor.  \n`ImageEditor.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the ImageEditor. Uses event data gradio.SelectData to carry `value` referring to the label of the ImageEditor, and `selected` to refer to state of the ImageEditor. See EventData documentation on how to use this event data  \n`ImageEditor.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the ImageEditor.  \n`ImageEditor.apply(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user applies changes to the ImageEditor through an integrated UI action.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each ele", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "ten a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"mi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "default `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "ne\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this ev", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "gradio.Brush(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the brush tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `brush` parameter of\n`gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    default_size: int | Literal['auto']\n\ndefault `= \"auto\"`\n\nThe default radius, in pixels, of the brush tool. Defaults to \"auto\" in which\ncase the radius is automatically determined based on the size of the image\n(generally 1/50th of smaller dimension).\n\n\n    \n    \n    colors: list[str | tuple[str, float]] | str | tuple[str, float] | None\n\ndefault `= None`\n\nA list of colors to make available to the user when using the brush. Defaults\nto a list of 5 colors.\n\n\n    \n    \n    default_color: str | tuple[str, float] | None\n\ndefault `= None`\n\nThe default color of the brush. Defaults to the first color in the `colors`\nlist.\n\n\n    \n    \n    color_mode: Literal['fixed', 'defaults']\n\ndefault `= \"defaults\"`\n\nIf set to \"fixed\", user can only select from among the colors in `colors`. If\n\"defaults\", the colors in `colors` are provided as a default palette, but the\nuser can also select any color using a color picker.\n\n", "heading1": "Brush", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "gradio.Eraser(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the eraser tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `eraser` parameter\nof `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    default_size: int | Literal['auto']\n\ndefault `= \"auto\"`\n\nThe default radius, in pixels, of the eraser tool. Defaults to \"auto\" in which\ncase the radius is automatically determined based on the size of the image\n(generally 1/50th of smaller dimension).\n\n", "heading1": "Eraser", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "gradio.LayerOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the layer tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `layers` parameter\nof `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    allow_additional_layers: bool\n\ndefault `= True`\n\nIf True, users can add additional layers to the image. If False, the add layer\nbutton will not be shown.\n\n\n    \n    \n    layers: list[str] | None\n\ndefault `= None`\n\nA list of layers to make available to the user when using the layer tool. One\nlayer must be provided, if the length of the list is 0 then a layer will be\ngenerated automatically.\n\n\n    \n    \n    disabled: bool\n\ndefault `= False`\n\n", "heading1": "Layer Options", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "gradio.WebcamOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the webcam tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `webcam_options`\nparameter of `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    mirror: bool\n\ndefault `= True`\n\nIf True, the webcam will be mirrored.\n\n\n    \n    \n    constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary of constraints for the webcam.\n\n", "heading1": "Webcam Options", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "Blocks is Gradio's low-level API that allows you to create more custom web\napplications and demos than Interfaces (yet still entirely in Python).  \n  \nCompared to the Interface class, Blocks offers more flexibility and control\nover: (1) the layout of components (2) the events that trigger the execution\nof functions (3) data flows (e.g. inputs can trigger outputs, which can\ntrigger the next level of outputs). Blocks also offers ways to group together\nrelated demos such as with tabs.  \n  \nThe basic usage of Blocks is as follows: create a Blocks object, then use it\nas a context (with the \"with\" statement), and then define layouts, components,\nor events within the Blocks context. Finally, call the launch() method to\nlaunch the demo.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "import gradio as gr\n    def update(name):\n        return f\"Welcome to Gradio, {name}!\"\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Start typing below and then click **Run** to see the output.\")\n        with gr.Row():\n            inp = gr.Textbox(placeholder=\"What is your name?\")\n            out = gr.Textbox()\n        btn = gr.Button(\"Run\")\n        btn.click(fn=update, inputs=inp, outputs=out)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\nA Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nWhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable or default to True.\n\n\n    \n    \n    mode: str\n\ndefault `= \"blocks\"`\n\nA human-friendly name for the kind of Blocks or Interface being created. Used\ninternally for analytics.\n\n\n    \n    \n    title: str | I18nData\n\ndefault `= \"Gradio\"`\n\nThe tab title to display when this is opened in a browser window.\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n    \n    \n    css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a code string. The custom js should be in the form of a single js\nfunction. This function will automatically be executed when the page loads.\nFor more flexibility, use the head parameter to insert js inside <script>\ntags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html code to insert into the head of the demo webpage. This can be used\nto add custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n\n    \n    \n    head_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "e\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` parameter is also set, the html from `head` will\nbe included first.\n\n\n    \n    \n    fill_height: bool\n\ndefault `= False`\n\nWhether to vertically expand top-level child components to the height of the\nwindow. If True, expansion occurs when the scale value of the child components\n>= 1.\n\n\n    \n    \n    fill_width: bool\n\ndefault `= False`\n\nWhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width. Only applies if this is the outermost\n`Blocks` in your Gradio app.\n\n\n    \n    \n    delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\nA tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "blocks_helloblocks_flipperblocks_kinematics\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def welcome(name): return f\"Welcome to Gradio,\n{name}!\" with gr.Blocks() as demo: gr.Markdown( \"\"\" Hello World! Start\ntyping below to see the output. \"\"\") inp = gr.Textbox(placeholder=\"What is\nyour name?\") out = gr.Textbox() inp.change(welcome, inp, out) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def welcome(name):\n        return f\"Welcome to Gradio, {name}!\"\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\n        \"\"\"\n        Hello World!\n        Start typing below to see the output.\n        \"\"\")\n        inp = gr.Textbox(placeholder=\"What is your name?\")\n        out = gr.Textbox()\n        inp.change(welcome, inp, out)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr def flip_text(x): return\nx[::-1] def flip_image(x): return np.fliplr(x) with gr.Blocks() as demo:\ngr.Markdown(\"Flip text or image files using this demo.\") with gr.Tab(\"Flip\nText\"): text_input = gr.Textbox() text_output = gr.Textbox() text_button =\ngr.Button(\"Flip\") with gr.Tab(\"Flip Image\"): with gr.Row(): image_input =\ngr.Image() image_output = gr.Image() image_button = gr.Button(\"Flip\") with\ngr.Accordion(\"Open for More!\", open=False): gr.Markdown(\"Look at me...\")\ntemp_slider = gr.Slider( 0, 1, value=0.1, step=0.1, interactive=True,\nlabel=\"Slide me\", ) text_button.click(flip_text, inputs=text_input,\noutputs=text_output) image_button.click(flip_image, inputs=image_input,\noutputs=image_output) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    import gradio as gr\n    \n    def flip_text(x):\n        return x[::-1]\n    \n    def flip_image(x):\n        return np.fliplr(x)\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Flip text or image files using this demo.\")\n        with gr.Tab(\"Flip Text\"):\n            text_input = gr.Textbox()\n            text_output = gr.Textbox()\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "gr.Blocks() as demo:\n        gr.Markdown(\"Flip text or image files using this demo.\")\n        with gr.Tab(\"Flip Text\"):\n            text_input = gr.Textbox()\n            text_output = gr.Textbox()\n            text_button = gr.Button(\"Flip\")\n        with gr.Tab(\"Flip Image\"):\n            with gr.Row():\n                image_input = gr.Image()\n                image_output = gr.Image()\n            image_button = gr.Button(\"Flip\")\n    \n        with gr.Accordion(\"Open for More!\", open=False):\n            gr.Markdown(\"Look at me...\")\n            temp_slider = gr.Slider(\n                0, 1,\n                value=0.1,\n                step=0.1,\n                interactive=True,\n                label=\"Slide me\",\n            )\n    \n        text_button.click(flip_text, inputs=text_input, outputs=text_output)\n        image_button.click(flip_image, inputs=image_input, outputs=image_output)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd import numpy as np import gradio as gr def\nplot(v, a): g = 9.81 theta = a / 180 * 3.14 tmax = ((2 * v) * np.sin(theta)) /\ng timemat = tmax * np.linspace(0, 1, 40) x = (v * timemat) * np.cos(theta) y =\n((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2)) df =\npd.DataFrame({\"x\": x, \"y\": y}) return df demo = gr.Blocks() with demo:\ngr.Markdown( r\"Let's do some kinematics! Choose the speed and angle to see the\ntrajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n) with gr.Row(): speed = gr.Slider(1, 30, 25, label=\"Speed\") angle =\ngr.Slider(0, 90, 45, label=\"Angle\") output = gr.LinePlot( x=\"x\", y=\"y\",\noverlay_point=True, tooltip=[\"x\", \"y\"], x_lim=[0, 100], y_lim=[0, 60],\nwidth=350, height=300, ) btn = gr.Button(value=\"Run\") btn.click(plot, [speed,\nangle], output) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import pandas as pd\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot(v, a):\n        g = 9.81\n        theta = a / 180 * 3.14\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "_name__ == \"__main__\": demo.launch()\n\n    \n    \n    import pandas as pd\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot(v, a):\n        g = 9.81\n        theta = a / 180 * 3.14\n        tmax = ((2 * v) * np.sin(theta)) / g\n        timemat = tmax * np.linspace(0, 1, 40)\n    \n        x = (v * timemat) * np.cos(theta)\n        y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))\n        df = pd.DataFrame({\"x\": x, \"y\": y})\n        return df\n    \n    demo = gr.Blocks()\n    \n    with demo:\n        gr.Markdown(\n            r\"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n        )\n    \n        with gr.Row():\n            speed = gr.Slider(1, 30, 25, label=\"Speed\")\n            angle = gr.Slider(0, 90, 45, label=\"Angle\")\n        output = gr.LinePlot(\n            x=\"x\",\n            y=\"y\",\n            overlay_point=True,\n            tooltip=[\"x\", \"y\"],\n            x_lim=[0, 100],\n            y_lim=[0, 60],\n            width=350,\n            height=300,\n        )\n        btn = gr.Button(value=\"Run\")\n        btn.click(plot, [speed, angle], output)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.launch(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "-!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nLaunches a simple web server that serves the demo. Can also be used to create\na public link used by anyone to access the demo from their browser by setting\nshare=True.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    def reverse(text):\n        return text[::-1]\n    with gr.Blocks() as demo:\n        button = gr.Button(value=\"Reverse\")\n        button.click(reverse, gr.Textbox(), gr.Textbox())\n    demo.launch(share=True, auth=(\"username\", \"password\"))\n\nParameters \u25bc\n\n\n    \n    \n    inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault ", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "  \n    inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the default\nbrowser.\n\n\n    \n    \n    share: bool | None\n\ndefault `= None`\n\nwhether to create a publicly shareable link for the gradio app. Creates an SSH\ntunnel to make your UI accessible from anywhere. If not provided, it is set to\nFalse by default every time, except when running in Google Colab. When\nlocalhost is not accessible (e.g. Google Colab), setting share=False is not\nsupported. Can be set by environment variable GRADIO_SHARE=True.\n\n\n    \n    \n    debug: bool\n\ndefault `= False`\n\nif True, blocks the main thread from running. If running in Google Colab, this\nis needed to print the errors in the cell output.\n\n\n    \n    \n    max_threads: int\n\ndefault `= 40`\n\nthe maximum number of total threads that the Gradio app can generate in\nparallel. The default is inherited from the starlette library (currently 40).\n\n\n    \n    \n    auth: Callable[[str, str], bool] | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access app. Can also provide function that takes username and\npassword and returns True if valid login.\n\n\n    \n    \n    auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page.\n\n\n    \n    \n    prevent_thread_lock: bool\n\ndefault `= False`\n\nBy default, the gradio app blocks the main thread while the server is running.\nIf set to True, the gradio app will not block and the gradio server will\nterminate as soon as the script finishes.\n\n\n    \n    \n    show_error: bool\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= None`\n\nto make app accessible on local network, set this to \"0.0.0.0\". Can be set by\nenvironment variable GRADIO_SERVER_NAME. If None, will use \"127.0.0.1\".\n\n\n    \n    \n    server_port: int | None\n\ndefault `= None`\n\nwill start gradio app on this port (if available). Can be set by environment\nvariable GRADIO_SERVER_PORT. If None, will search for an available port\nstarting at 7860.\n\n\n    \n    \n    height: int\n\ndefault `= 500`\n\nThe height in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    width: int | str\n\ndefault `= \"100%\"`\n\nThe width in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    favicon_path: str | Path | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for the web page.\n\n\n    \n    \n    ssl_keyfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the private key file to\ncreate a local server running on https.\n\n\n    \n    \n    ssl_certfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the signed certificate for\nhttps. Needs to be provided if ssl_keyfile is provided.\n\n\n    \n    \n    ssl_keyfile_password: str | None\n\ndefault `= None`\n\nIf a password is provided, will use this with the ssl certificate for https.\n\n\n    \n    \n    ssl_verify: bool\n\ndefault `= True`\n\nIf False, skips certificate validation which allows self-signed certificates\nto be used.\n\n\n    \n    \n    quiet: bool\n\ndefault `= False`\n\nIf True, suppresses most print statements.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_pa", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "t `= False`\n\nIf True, suppresses most print statements.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is allowed to\nserve. Must be absolute paths. Warning: if you provide directories, any files\nin these directories or their subdirectories are accessible to all users of\nyour app. Can be set by comma separated environment variable\nGRADIO_ALLOWED_PATHS. These files are generally assumed to be secure and will\nbe displayed in the browser when possible.\n\n\n    \n    \n    blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is not allowed to\nserve (i.e. users of your app are not allowed to access). Must be absolute\npaths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default. Can be set by comma separated\nenvironment variable GRADIO_BLOCKED_PATHS.\n\n\n    \n    \n    root_path: str | None\n\ndefault `= None`\n\nThe root path (or \"mount point\") of the application, if it's not served from\nthe root (\"/\") of the domain. Often used when the application is behind a\nreverse proxy that forwards requests to the application. For example, if the\napplication is served at \"https://example.com/myapp\", the `root_path` should\nbe set to \"/myapp\". A full URL beginning with http:// or https:// can be\nprovided, which will be used as the root path in its entirety. Can be set by\nenvironment variable GRADIO_ROOT_PATH. Defaults to \"\".\n\n\n    \n    \n    app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "ument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capacity to reduce memory usage when using gradio.State or returning\nupdated components from functions. Defaults to 10000.\n\n\n    \n    \n    share_server_address: str | None\n\ndefault `= None`\n\nUse this to specify a custom FRP server and port for sharing Gradio apps (only\napplies if share=True). If not provided, will use the default FRP server at\nhttps://gradio.live. See https://github.com/huggingface/frp for more\ninformation.\n\n\n    \n    \n    share_server_protocol: Literal['http', 'https'] | None\n\ndefault `= None`\n\nUse this to specify the protocol to use for the share links. Defaults to\n\"https\", unless a custom share_server_address is provided, in which case it\ndefaults to \"http\". If you are using a custom share_server_address and want to\nuse https, you must set this to \"https\".\n\n\n    \n    \n    share_server_tls_certificate: str | None\n\ndefault `= None`\n\nThe path to a TLS certificate file to use when connecting to a custom share\nserver. This parameter is not used with the default FRP server at\nhttps://gradio.live. Otherwise, you must provide a valid TLS certificate file\n(e.g. a \"cert.pem\") relative to the current working directory, or the\nconnection will not use TLS encryption, which is insecure.\n\n\n    \n    \n    auth_dependency: Callable[[fastapi.Request], str | None] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the app (they will see a 401 Unauthorized response). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that c", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "ponse). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    enable_monitoring: bool | None\n\ndefault `= None`\n\nEnables traffic monitoring of the app through the /monitoring endpoint. By\ndefault is None, which enables this endpoint. If explicitly True, will also\nprint the monitoring URL to the console. If False, will disable monitoring\naltogether.\n\n\n    \n    \n    strict_cors: bool\n\ndefault `= True`\n\nIf True, prevents external domains from making requests to a Gradio server\nrunning on localhost. If False, allows requests to localhost that originate\nfrom localhost but also, crucially, from \"null\". This parameter should\nnormally be True to prevent CSRF attacks but may need to be False when\nembedding a *locally-running Gradio app* using web components.\n\n\n    \n    \n    node_server_name: str | None\n\ndefault `= None`\n\n\n    \n    \n    node_port: int | None\n\ndefault `= None`\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 20+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n    \n    \n    pwa: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an installable PWA (Progressive Web\nApp). If set to None (default behavior), then the PWA feature will be enabled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfun", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "abled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_MCP_SERVER environment variable will be used to determine if the MCP\nserver should be enabled (which is \"True\" on Hugging Face Spaces).\n\n\n    \n    \n    i18n: I18n | None\n\ndefault `= None`\n\nAn I18n instance containing custom translations, which are used to translate\nstrings in our components (e.g. the labels of components or Markdown strings).\nThis feature can only be used to translate static text in the frontend, not\nvalues in the backend.\n\n", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.queue(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20-", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nBy enabling the queue you can control when users know their position in the\nqueue, and set a limit on maximum number of events allowed.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%2", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    with gr.Blocks() as demo:\n        button = gr.Button(label=\"Generate Image\")\n        button.click(fn=image_generator, inputs=gr.Textbox(), outputs=gr.Image())\n    demo.queue(max_size=10)\n    demo.launch()\n\nParameters \u25bc\n\n\n    \n    \n    status_update_rate: float | Literal['auto']\n\ndefault `= \"auto\"`\n\nIf \"auto\", Queue will send status estimations to all clients whenever a job is\nfinished. Otherwise Queue will send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open:", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": " will send status estimations to all clients whenever a job is\nfinished. Otherwise Queue will send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowing requests made\ndirectly to those endpoints to skip the queue.\n\n\n    \n    \n    max_size: int | None\n\ndefault `= None`\n\nThe maximum number of events the queue will store at any given moment. If the\nqueue is full, new events will not be added and a user will receive a message\nsaying that the queue is full. If None, the queue size will be unlimited.\n\n\n    \n    \n    default_concurrency_limit: int | None | Literal['not_set']\n\ndefault `= \"not_set\"`\n\nThe default value of `concurrency_limit` to use for event listeners that don't\nspecify a value. Can be set by environment variable\nGRADIO_DEFAULT_CONCURRENCY_LIMIT. Defaults to 1 if not set otherwise.\n\n", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.integrate(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nA catch-all method for integrating with other libraries. This method should be\nrun after launch()\n\nParameters \u25bc\n\n\n    \n    \n    comet_ml: <class 'inspect._empty'>\n\ndefault `= None`\n\nIf a comet_ml Experiment object is provided, will integrate with the\nexperiment and appear on Comet dashboard\n\n\n    \n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n\nIf", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "  \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n\nIf the mlflow module is provided, will integrate with the experiment and\nappear on ML Flow dashboard\n\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.load(block, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20In", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Blocks initially loads in the browser.\n\nParameters \u25bc\n\n\n    \n    \n    block: Block | None\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to o", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "ction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime displa", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. F", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "r events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will au", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.unload(fn, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc", "heading1": "unload", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the user closes or refreshes the tab, ending\nthe user session. It is useful for cleaning up resources when the app is\nclosed.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%2", "heading1": "unload", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    with gr.Blocks() as demo:\n        gr.Markdown(\"When you close the tab, hello will be printed to the console\")\n        demo.unload(lambda: print(\"hello\"))\n    demo.launch()\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable[..., Any]\n\nCallable function to run to clear resources. The function should not take any\narguments and the output is not used.\n\n", "heading1": "unload", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "ources. The function should not take any\narguments and the output is not used.\n\n", "heading1": "unload", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "Component to select a date and (optionally) a time.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "**As input component** : Passes text value as a `str` into the function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: float | datetime | str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a tuple pair of datetimes.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> float | datetime | str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: float | str | datetime | None\n\ndefault `= None`\n\ndefault value for datetime.\n\n\n    \n    \n    include_time: bool\n\ndefault `= True`\n\nIf True, the component will include time selection. If False, only date\nselection will be available.\n\n\n    \n    \n    type: Literal['timestamp', 'datetime', 'string']\n\ndefault `= \"timestamp\"`\n\nThe type of the value. Can be \"timestamp\", \"datetime\", or \"string\". If\n\"timestamp\", the value will be a number representing the start and end date in\nseconds since epoch. If \"datetime\", the value will be a datetime object. If\n\"string\", the value will be the date entered by the user.\n\n\n    \n    \n    timezone: str | None\n\ndefault `= None`\n\nThe timezone to use for timestamps, such as \"US/Pacific\" or \"Europe/Paris\". If\nNone, the timezone will be the local timezone.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: float | None\n\ndefault `= None`\n\nIf `value` is a callable, run the function 'every' number of seconds while the\nclient connection is open. Has no effect otherwise. The event can be accessed\n(e.g. to cancel it) via this component's .load_event attribute.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin B", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "nents. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.DateTime` | \"datetime\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe DateTime component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`DateTime.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the DateTime changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`DateTime.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the DateTime is focused.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, Non", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "nction returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that i", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "eue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js meth", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "When gr.EventData or one of its subclasses is added as a type hint to an\nargument of a prediction function, a gr.EventData object will automatically be\npassed as the value of that argument. The attributes of this object contains\ninformation about the event that triggered the listener. The gr.EventData\nobject itself contains a `.target` attribute that refers to the component that\ntriggered the event, while subclasses of gr.EventData contains additional\nattributes that are different for each class.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "import gradio as gr\n    \n    with gr.Blocks() as demo:\n        table = gr.Dataframe([[1, 2, 3], [4, 5, 6]])\n        gallery = gr.Gallery([(\"cat.jpg\", \"Cat\"), (\"dog.jpg\", \"Dog\")])\n        textbox = gr.Textbox(\"Hello World!\")\n        statement = gr.Textbox()\n    \n        def on_select(value, evt: gr.EventData):\n            return f\"The {evt.target} component was selected, and its value was {value}.\"\n    \n        table.select(on_select, table, statement)\n        gallery.select(on_select, gallery, statement)\n        textbox.select(on_select, textbox, statement)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    target: Block | None\n\nThe component object that triggered the event. Can be used to distinguish\nmultiple components bound to the same listener.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "gallery_selectionstictactoe\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np with gr.Blocks() as demo:\nimgs = gr.State() gallery = gr.Gallery(allow_preview=False) def\ndeselect_images(): return gr.Gallery(selected_index=None) def\ngenerate_images(): images = [] for _ in range(9): image = np.ones((100, 100,\n3), dtype=np.uint8) * np.random.randint( 0, 255, 3 ) image is a solid single\ncolor images.append(image) return images, images demo.load(generate_images,\nNone, [gallery, imgs]) with gr.Row(): selected = gr.Number(show_label=False)\ndarken_btn = gr.Button(\"Darken selected\") deselect_button =\ngr.Button(\"Deselect\") deselect_button.click(deselect_images, None, gallery)\ndef get_select_index(evt: gr.SelectData): return evt.index\ngallery.select(get_select_index, None, selected) def darken_img(imgs, index):\nindex = int(index) imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\nreturn imgs, imgs darken_btn.click(darken_img, [imgs, selected], [imgs,\ngallery]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    \n    with gr.Blocks() as demo:\n        imgs = gr.State()\n        gallery = gr.Gallery(allow_preview=False)\n    \n        def deselect_images():\n            return gr.Gallery(selected_index=None)\n    \n        def generate_images():\n            images = []\n            for _ in range(9):\n                image = np.ones((100, 100, 3), dtype=np.uint8) * np.random.randint(\n                    0, 255, 3\n                )  image is a solid single color\n                images.append(image)\n            return images, images\n    \n        demo.load(generate_images, None, [gallery, imgs])\n    \n        with gr.Row():\n            selected = gr.Number(show_label=False)\n            darken_btn = gr.Button(\"Darken selected\")\n        deselect_button = gr.Button(\"Deselect\")\n    \n        deselect_button.click(deselect_images, None, gallery)\n    \n        def get_select_index(evt: gr.SelectData):\n            return evt.index\n    \n  ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "  deselect_button = gr.Button(\"Deselect\")\n    \n        deselect_button.click(deselect_images, None, gallery)\n    \n        def get_select_index(evt: gr.SelectData):\n            return evt.index\n    \n        gallery.select(get_select_index, None, selected)\n    \n        def darken_img(imgs, index):\n            index = int(index)\n            imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\n            return imgs, imgs\n    \n        darken_btn.click(darken_img, [imgs, selected], [imgs, gallery])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr with gr.Blocks() as demo: turn =\ngr.Textbox(\"X\", interactive=False, label=\"Turn\") board =\ngr.Dataframe(value=[[\"\", \"\", \"\"]] * 3, interactive=False, type=\"array\") def\nplace(board: list[list[int]], turn, evt: gr.SelectData): if evt.value: return\nboard, turn board[evt.index[0]][evt.index[1]] = turn turn = \"O\" if turn == \"X\"\nelse \"X\" return board, turn board.select(place, [board, turn], [board, turn],\nshow_progress=\"hidden\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    with gr.Blocks() as demo:\n        turn = gr.Textbox(\"X\", interactive=False, label=\"Turn\")\n        board = gr.Dataframe(value=[[\"\", \"\", \"\"]] * 3, interactive=False, type=\"array\")\n    \n        def place(board: list[list[int]], turn, evt: gr.SelectData):  \n            if evt.value:\n                return board, turn\n            board[evt.index[0]][evt.index[1]] = turn\n            turn = \"O\" if turn == \"X\" else \"X\"\n            return board, turn\n    \n        board.select(place, [board, turn], [board, turn], show_progress=\"hidden\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "Creates a code editor for viewing code (as an output component), or for\nentering and editing code (as an input component).\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "**As input component** : Passes the code entered as a `str`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` of code.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> tuple[str] | str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Callable | None\n\ndefault `= None`\n\nDefault value to show in the code editor. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    language: Literal['python', 'c', 'cpp', 'markdown', 'latex', 'json', 'html', 'css', 'javascript', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-msSQL', 'sql-mySQL', 'sql-mariaDB', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgSQL', 'sql-gql', 'sql-gpSQL', 'sql-sparkSQL', 'sql-esper'] | None\n\ndefault `= None`\n\nThe language to display the code as. Supported languages listed in\n`gr.Code.languages`.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    lines: int\n\ndefault `= 5`\n\nMinimum number of visible lines to show in the code editor.\n\n\n    \n    \n    max_lines: int | None\n\ndefault `= None`\n\nMaximum number of visible lines to show in the code editor. Defaults to None\nand will fill the height of the container.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nWhether user should be able to enter code or only view it.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= No", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "\ncomponent is assigned to.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nWhether user should be able to enter code or only view it.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": " treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    wrap_lines: bool\n\ndefault `= False`\n\nIf True, will wrap lines to the width of the container when overflow occurs.\nDefaults to False.\n\n\n    \n    \n    show_line_numbers: bool\n\ndefault `= True`\n\nIf True, displays line numbers, and if False, hides line numbers.\n\n\n    \n    \n    autocomplete: bool\n\ndefault `= False`\n\nIf True, will show autocomplete suggestions for supported languages. Defaults\nto False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Code` | \"code\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Code component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`Code.languages(fn, \u00b7\u00b7\u00b7)` | ['python', 'c', 'cpp', 'markdown', 'latex', 'json', 'html', 'css', 'javascript', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-msSQL', 'sql-mySQL', 'sql-mariaDB', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgSQL', 'sql-gql', 'sql-gpSQL', 'sql-sparkSQL', 'sql-esper', None]  \n`Code.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Code changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Code.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Code.  \n`Code.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Code is focused.  \n`Code.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Code is unfocused/blurred.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "Creates an image component that can be used to upload images (as an input)\nor display images (as an output).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "**As input component** : Passes the uploaded image as a `numpy.array`,\n`PIL.Image` or `str` filepath depending on `type`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: np.ndarray | PIL.Image.Image | str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `numpy.array`, `PIL.Image`, or `str` or\n`pathlib.Path` filepath to an image which is displayed.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> np.ndarray | PIL.Image.Image | str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | PIL.Image.Image | np.ndarray | Callable | None\n\ndefault `= None`\n\nA PIL Image, numpy array, path or URL for the default value that Image\ncomponent is going to take. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFile format (e.g. \"png\" or \"gif\"). Used to save image if it does not already\nhave a valid format (e.g. if the image is being returned to the frontend as a\nnumpy array or PIL Image). The format should be supported by the PIL library.\nApplies both when this component is used as an input or output. This parameter\nhas no effect on SVG files.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfile or numpy array, but will affect the displayed image.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfile or numpy array, but will affect the displayed image.\n\n\n    \n    \n    image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None\n\ndefault `= \"RGB\"`\n\nThe pixel format and color depth that the image should be loaded and\npreprocessed as. \"RGB\" will load the image as a color image, or \"L\" as black-\nand-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html\nfor other supported image modes and their meaning. This parameter has no\neffect on SVG or GIF files. If set to None, the image_mode will be inferred\nfrom the image file type (e.g. \"RGBA\" for a .png image, \"RGB\" in most other\ncases).\n\n\n    \n    \n    sources: list[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None\n\ndefault `= None`\n\nLi", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": ".g. \"RGBA\" for a .png image, \"RGB\" in most other\ncases).\n\n\n    \n    \n    sources: list[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None\n\ndefault `= None`\n\nList of sources for the image. \"upload\" creates a box where user can drop an\nimage file, \"webcam\" allows user to take snapshot from their webcam,\n\"clipboard\" allows users to paste an image from the clipboard. If None,\ndefaults to [\"upload\", \"webcam\", \"clipboard\"] if streaming is False, otherwise\ndefaults to [\"webcam\"].\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe format the image is converted before being passed into the prediction\nfunction. \"numpy\" converts the image to a numpy array with shape (height,\nwidth, 3) and values from 0 to 255, \"pil\" converts the image to a PIL image\nobject, \"filepath\" passes a str path to a temporary file containing the image.\nTo support animated GIFs in input, the `type` should be set to \"filepath\" or\n\"pil\". To support SVGs, the `type` should be set to \"filepath\".\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_d", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_download_button: bool\n\ndefault `= True`\n\nIf True, will display button to download image. Only applies if interactive is\nFalse (e.g. if the component is used as an output).\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload and edit an image; if False, can only be\nused to display images. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    streaming: bool\n\ndefault `= False`\n\nIf True when used in a `live` interface, will automatically stream webcam\nfeed. Only valid is source is 'webcam'. If the component is an output\ncomponent, will automatically convert images to base64.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as th", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": " id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    mirror_webcam: bool | None\n\ndefault `= None`\n\nIf True webcam will be mirrored. Default is True.\n\n\n    \n    \n    webcam_options: WebcamOptions | None\n\ndefault `= None`\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\nCustom text for the upload area. Overrides default upload messages when\nprovided. Accepts new lines and `` to designate a heading.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a fullscreen i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ea. Overrides default upload messages when\nprovided. Accepts new lines and `` to designate a heading.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a fullscreen icon in the corner of the component that\nallows user to view the image in fullscreen mode. If False, icon does not\nappear.\n\n\n    \n    \n    webcam_constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary that allows developers to specify custom media constraints for\nthe webcam stream. This parameter provides flexibility to control the video\nstream's properties, such as resolution and front or rear camera on mobile\ndevices. See\n[demo/webcam_constraints](https://gradio.app/playground?demo=Blank&code=aW1wb3J0IGdyYWRpbyBhcyBncgppbXBvcnQgY3YyCgpkZWYgZ2V0X3ZpZGVvX3NoYXBlKHZpZGVvKToKICAgIGNhcCA9IGN2Mi5WaWRlb0NhcHR1cmUodmlkZW8pCiAgICB3aWR0aCA9IGludChjYXAuZ2V0KGN2Mi5DQVBfUFJPUF9GUkFNRV9XSURUSCkpCiAgICBoZWlnaHQgPSBpbnQoY2FwLmdldChjdjIuQ0FQX1BST1BfRlJBTUVfSEVJR0hUKSkKICAgIGNhcC5yZWxlYXNlKCkKICAgIHJldHVybiB7IndpZHRoIjogd2lkdGgsICJoZWlnaHQiOiBoZWlnaHR9CgpkZWYgaW1hZ2VfbW9kKGltYWdlKToKICAgIHdpZHRoLCBoZWlnaHQgPSBpbWFnZS5zaXplCiAgICByZXR1cm4geyJ3aWR0aCI6IHdpZHRoLCAiaGVpZ2h0IjogaGVpZ2h0fQoKCnZpZGVvID0gZ3IuSW50ZXJmYWNlKAogICAgZm49Z2V0X3ZpZGVvX3NoYXBlLAogICAgaW5wdXRzPWdyLlZpZGVvKHdlYmNhbV9jb25zdHJhaW50cz17InZpZGVvIjogeyJ3aWR0aCI6IDgwMCwgImhlaWdodCI6IDYwMH19LCBzb3VyY2VzPSJ3ZWJjYW0iKSwKICAgIG91dHB1dHM9Z3IuSlNPTigpCikKCmltYWdlID0gZ3IuSW50ZXJmYWNlKAogICAgICAgIGltYWdlX21vZCwKICAgICAgICBnci5JbWFnZSh0eXBlPSJwaWwiLCB3ZWJjYW1fY29uc3RyYWludHM9eyJ2aWRlbyI6IHsid2lkdGgiOiA4MDAsICJoZWlnaHQiOiA2MDB9fSwgc291cmNlcz0id2ViY2FtIiksCiAgICAgICAgZ3IuSnNvbigpKQoKd2l0aCBnci5CbG9ja3MoKSBhcyBkZW1vOgogICAgZ3IuTWFya2Rvd24oIiIiIyBXZWJjYW0gQ29uc3RyYWludHMKICAgICAgICAgICAgICAgIFRoZSB3ZWJjYW0gY29uc3RyYWludHMgYXJlIHNldCB0byA4MDB4NjAwIHdpdGggdGhlIGZvbGxvd2luZyBzeW50YXg6CiAgICAgICAgICAgICAgICBgYGBweXRob24KICAgICAgICAgICAgICAgIGdyLlZpZGVvKHdlYmNhbV9jb25zdHJhaW50cz17InZpZGVvIjogeyJ3aWR0aCI6IDgwMCwgImhlaWdodCI6IDYwMH19L", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "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&reqs=b3BlbmN2LXB5dGhvbg%3D%3D)\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Image` | \"image\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "The `gr.Image` component can process or display any image format that is\n[supported by the PIL\nlibrary](https://pillow.readthedocs.io/en/stable/handbook/image-file-\nformats.html), including animated GIFs. In addition, it also supports the SVG\nimage format.\n\nWhen the `gr.Image` component is used as an input component, the image is\nconverted into a `str` filepath, a `PIL.Image` object, or a `numpy.array`,\ndepending on the `type` parameter. However, animated GIF and SVG images are\ntreated differently:\n\n  * Animated `GIF` images can only be converted to `str` filepaths or `PIL.Image` objects. If they are converted to a `numpy.array` (which is the default behavior), only the first frame will be used. So if your demo expects an input `GIF` image, make sure to set the `type` parameter accordingly, e.g.\n\n    \n    \n    import gradio as gr\n    \n    demo = gr.Interface(\n        fn=lambda x:x, \n        inputs=gr.Image(type=\"filepath\"), \n        outputs=gr.Image()\n    )\n        \n    demo.launch()\n\n  * For `SVG` images, the `type` parameter is ignored altogether and the image is always returned as an image filepath. This is because `SVG` images cannot be processed as `PIL.Image` or `numpy.array` objects.\n\n", "heading1": "`GIF` and `SVG` Image Formats", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "sepia_filterfake_diffusion\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr def sepia(input_img):\nsepia_filter = np.array([ [0.393, 0.769, 0.189], [0.349, 0.686, 0.168],\n[0.272, 0.534, 0.131] ]) sepia_img = input_img.dot(sepia_filter.T) sepia_img\n/= sepia_img.max() return sepia_img demo = gr.Interface(sepia, gr.Image(),\n\"image\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    import gradio as gr\n    \n    def sepia(input_img):\n        sepia_filter = np.array([\n            [0.393, 0.769, 0.189],\n            [0.349, 0.686, 0.168],\n            [0.272, 0.534, 0.131]\n        ])\n        sepia_img = input_img.dot(sepia_filter.T)\n        sepia_img /= sepia_img.max()\n        return sepia_img\n    \n    demo = gr.Interface(sepia, gr.Image(), \"image\")\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np import time def\nfake_diffusion(steps): rng = np.random.default_rng() for i in range(steps):\ntime.sleep(1) image = rng.random(size=(600, 600, 3)) yield image image =\nnp.ones((1000,1000,3), np.uint8) image[:] = [255, 124, 0] yield image demo =\ngr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3, step=1),\noutputs=\"image\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    import time\n    \n    def fake_diffusion(steps):\n        rng = np.random.default_rng()\n        for i in range(steps):\n            time.sleep(1)\n            image = rng.random(size=(600, 600, 3))\n            yield image\n        image = np.ones((1000,1000,3), np.uint8)\n        image[:] = [255, 124, 0]\n        yield image\n    \n    demo = gr.Interface(fake_diffusion,\n                        inputs=gr.Slider(1, 10, 3, step=1),\n                        outputs=\"image\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Image component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Image.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Image using the clear button for the component.  \n`Image.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Image changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Image.stream(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user streams the Image.  \n`Image.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Image. Uses event data gradio.SelectData to carry `value` referring to the label of the Image, and `selected` to refer to state of the Image. See EventData documentation on how to use this event data  \n`Image.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Image.  \n`Image.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Image.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockCo", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ion should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | lis", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ent area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctio", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ill cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n   ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ll still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "gradio.WebcamOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the webcam tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `webcam_options`\nparameter of `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    mirror: bool\n\ndefault `= True`\n\nIf True, the webcam will be mirrored.\n\n\n    \n    \n    constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary of constraints for the webcam.\n\n", "heading1": "Webcam Options", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "Creates a checkbox that can be set to `True` or `False`. Can be used as an\ninput to pass a boolean value to a function or as an output to display a\nboolean value.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "**As input component** : Passes the status of the checkbox as a `bool`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: bool | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `bool` value that is set as the status\nof the checkbox\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> bool | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: bool | Callable\n\ndefault `= False`\n\nif True, checked by default. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, this checkbox can be checked; if False, checking will be disabled. If\nnot provided, this is inferred based on whether the component is used as an\ninput or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "n event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Checkbox` | \"checkbox\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "sentence_builderhello_world_3\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, is_morning, temperature):\nsalutation = \"Good morning\" if is_morning else \"Good evening\" greeting =\nf\"{salutation} {name}. It is {temperature} degrees today\" celsius =\n(temperature - 32) * 5 / 9 return greeting, round(celsius, 2) demo =\ngr.Interface( fn=greet, inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\noutputs=[\"text\", \"number\"], ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, is_morning, temperature):\n        salutation = \"Good morning\" if is_morning else \"Good evening\"\n        greeting = f\"{salutation} {name}. It is {temperature} degrees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n        outp", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "    celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n        outputs=[\"text\", \"number\"],\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Checkbox component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Checkbox.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Checkbox changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Checkbox.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Checkbox.  \n`Checkbox.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Checkbox. Uses event data gradio.SelectData to carry `value` referring to the label of the Checkbox, and `selected` to refer to state of the Checkbox. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] |", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "ts to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue h", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "ess animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "iteral['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event a", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "ult `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Creates a video component that can be used to upload/record videos (as an\ninput) or display videos (as an output). For the video to be playable in the\nbrowser it must have a compatible container and codec combination. Allowed\ncombinations are .mp4 with h264 codec, .ogg with theora codec, and .webm with\nvp9 codec. If the component detects that the output video would not be\nplayable in the browser it will attempt to convert it to a playable mp4 video.\nIf the conversion fails, the original video is returned.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "**As input component** : Passes the uploaded video as a `str` filepath or\nURL whose extension can be modified by `format`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` or `pathlib.Path` filepath to a video which is displayed, or a `Tuple[str | pathlib.Path, str | pathlib.Path | None]` where the first element is a filepath to a video and the second element is an optional filepath to a subtitle file.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | tuple[str | Path, str | Path | None] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Path | tuple[str | Path, str | Path | None] | Callable | None\n\ndefault `= None`\n\npath or URL for the default value that Video component is going to take. Can\nalso be a tuple consisting of (video filepath, subtitle filepath). If a\nsubtitle file is provided, it should be of type .srt or .vtt. Or can be\ncallable, in which case the function will be called whenever the app loads to\nset the initial value of the component.\n\n\n    \n    \n    format: str | None\n\ndefault `= None`\n\nthe file extension with which to save video, such as 'avi' or 'mp4'. This\nparameter applies both when this component is used as an input to determine\nwhich file format to convert user-provided video to, and when this component\nis used as an output to determine the format of video returned to the user. If\nNone, no file format conversion is done and the video is kept as is. Use 'mp4'\nto ensure browser playability.\n\n\n    \n    \n    sources: list[Literal['upload', 'webcam']] | Literal['upload', 'webcam'] | None\n\ndefault `= None`\n\nlist of sources permitted for video. \"upload\" creates a box where user can\ndrop a video file, \"webcam\" allows user to record a video from their webcam.\nIf None, defaults to both [\"upload, \"webcam\"].\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed video\nfile, but will affect the displayed video.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed video\nfile, but will affect the displayed video.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interf", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "| None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload a video; if False, can only be used to\ndisplay videos. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    elem_id: str | No", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "d, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    mirror_webcam: bool | None\n\ndefault `= None`\n\n\n    \n    \n    webcam_options: WebcamOptions | None\n\ndefault `= None`\n\nA `gr.WebcamOptions` instance that allows developers to specify custom media\nconstraints for the webcam stream. This parameter provides flexibility to\ncontrol the video stream's properties, such as resolution and front or rear\ncamera on mobile devices. See\n[demo/webcam_constraints](https://gradio.app/playground?demo=Blank&code=aW1wb3J0IGdyYWRpbyBhcyBncgppbXBvcnQgY3YyCgpkZWYgZ2V0X3ZpZGVvX3NoYXBlKHZpZGVvKToKICAgIGNhcCA9IG", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "ear\ncamera on mobile devices. See\n[demo/webcam_constraints](https://gradio.app/playground?demo=Blank&code=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&reqs=b3BlbmN2LXB5dGhvbg%3D%3D)\n\n\n    \n    \n    include_audio: bool | None\n\ndefault `= None`\n\nwhether the component should record/retain the audio track for a video. By\ndefault, audio is excluded for webcam videos and included for uploaded videos.\n\n\n    \n    \n    autoplay: bool\n\ndefaul", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": " None`\n\nwhether the component should record/retain the audio track for a video. By\ndefault, audio is excluded for webcam videos and included for uploaded videos.\n\n\n    \n    \n    autoplay: bool\n\ndefault `= False`\n\nwhether to automatically play the video when the component is used as an\noutput. Note: browsers will not autoplay video files if the user has not\ninteracted with the page yet.\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nif True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    show_download_button: bool | None\n\ndefault `= None`\n\nif True, will show a download icon in the corner of the component that allows\nuser to download the output. If False, icon does not appear. By default, it\nwill be True for output components and False for input components.\n\n\n    \n    \n    min_length: int | None\n\ndefault `= None`\n\nthe minimum length of video (in seconds) that the user can pass into the\nprediction function. If None, there is no minimum length.\n\n\n    \n    \n    max_length: int | None\n\ndefault `= None`\n\nthe maximum length of video (in seconds) that the user can pass into the\nprediction function. If None, there is no maximum length.\n\n\n    \n    \n    loop: bool\n\ndefault `= False`\n\nif True, the video will loop when it reaches the end and continue playing from\nthe beginning.\n\n\n    \n    \n    streaming: bool\n\ndefault `= False`\n\nwhen used set as an output, takes video chunks yielded from the backend and\ncombines them into one streaming video output. Each chunk should be a video\nfile with a .ts extension using an h.264 encoding. Mp4 files are also accepted\nbut they will be converted to h.264 encoding.\n\n\n    \n    \n    watermark: str | Path | None\n\ndefault `= None`\n\nan image file to be included as a watermark on the video. Th", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": ". Mp4 files are also accepted\nbut they will be converted to h.264 encoding.\n\n\n    \n    \n    watermark: str | Path | None\n\ndefault `= None`\n\nan image file to be included as a watermark on the video. The image is not\nscaled and is displayed on the bottom right of the video. Valid formats for\nthe image are: jpeg, png.\n\n\n    \n    \n    webcam_constraints: dict[str, Any] | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Video` | \"video\" | Uses default values  \n`gradio.PlayableVideo` | \"playablevideo\" | Uses format=\"mp4\"  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "video_identity_2\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def video_identity(video): return video demo =\ngr.Interface(video_identity, gr.Video(), \"playable_video\", ) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def video_identity(video):\n        return video\n    \n    demo = gr.Interface(video_identity,\n                        gr.Video(),\n                        \"playable_video\",\n                        )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Video component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Video.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Video changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Video.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Video using the clear button for the component.  \n`Video.start_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user starts recording with the Video.  \n`Video.stop_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user stops recording with the Video.  \n`Video.stop(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the Video.  \n`Video.play(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user plays the media in the Video.  \n`Video.pause(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the media in the Video stops for any reason.  \n`Video.end(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the Video.  \n`Video.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Video.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": " function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well a", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "w_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the cli", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "ancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow dow", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "ther to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "gradio.WebcamOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the webcam tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `webcam_options`\nparameter of `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    mirror: bool\n\ndefault `= True`\n\nIf True, the webcam will be mirrored.\n\n\n    \n    \n    constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary of constraints for the webcam.\n\n", "heading1": "Webcam Options", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "The FileData class is a subclass of the GradioModel class that represents a\nfile object within a Gradio interface. It is used to store file data and\nmetadata when a file is uploaded.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/filedata", "source_page_title": "Gradio - Filedata Docs"}, {"text": "from gradio_client import Client, FileData, handle_file\n    \n    def get_url_on_server(data: FileData):\n        print(data['url'])\n    \n    client = Client(\"gradio/gif_maker_main\", download_files=False)\n    job = client.submit([handle_file(\"./cheetah.jpg\")], api_name=\"/predict\")\n    data = job.result()\n    video: FileData = data['video']\n    \n    get_url_on_server(video)\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/filedata", "source_page_title": "Gradio - Filedata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    path: str\n\nThe server file path where the file is stored.\n\n\n    \n    \n    url: Optional[str]\n\nThe normalized server URL pointing to the file.\n\n\n    \n    \n    size: Optional[int]\n\nThe size of the file in bytes.\n\n\n    \n    \n    orig_name: Optional[str]\n\nThe original filename before upload.\n\n\n    \n    \n    mime_type: Optional[str]\n\nThe MIME type of the file.\n\n\n    \n    \n    is_stream: bool\n\nIndicates whether the file is a stream.\n\n\n    \n    \n    meta: dict\n\nAdditional metadata used internally (should not be changed).\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/filedata", "source_page_title": "Gradio - Filedata Docs"}, {"text": "Displays text that contains spans that are highlighted by category or\nnumerical value.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "**As input component** : Passes the value as a list of tuples as a `list[tuple]` into the function. Each `tuple` consists of a `str` substring of the text (so the entire text is included) and `str | float | None` label, which is the category or confidence of that substring.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: list[tuple[str, str | float | None]] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a list of (word, category) tuples, or a\ndictionary of two keys: \"text\", and \"entities\", which itself is a list of\ndictionaries, each of which have the keys: \"entity\" (or \"entity_group\"),\n\"start\", and \"end\"\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> list[tuple[str, str | float | None]] | dict | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: list[tuple[str, str | float | None]] | dict | Callable | None\n\ndefault `= None`\n\nDefault value to show. If a function is provided, the function will be called\neach time the app loads to set the initial value of this component.\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nA dictionary mapping labels to colors. The colors may be specified as hex\ncodes or by their names. For example: {\"person\": \"red\", \"location\": \"FFEE22\"}\n\n\n    \n    \n    show_legend: bool\n\ndefault `= False`\n\nwhether to show span categories in a separate legend or inline.\n\n\n    \n    \n    show_inline_category: bool\n\ndefault `= True`\n\nIf False, will not display span category label. Only applies if\nshow_legend=False and interactive=False.\n\n\n    \n    \n    combine_adjacent: bool\n\ndefault `= False`\n\nIf True, will merge the labels of adjacent tokens belonging to the same\ncategory.\n\n\n    \n    \n    adjacent_separator: str\n\ndefault `= \"\"`\n\nSpecifies the separator to be used between tokens if combine_adjacent is True.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    con", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constru", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, the component will be editable, and allow user to select spans of\ntext and label them.\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True, will display the text in right-to-left direction, and the labels in\nthe legend will also be aligned to the right.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.HighlightedText` | \"highlightedtext\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "diff_texts\n\nOpen in \ud83c\udfa2 \u2197 from difflib import Differ import gradio as gr def\ndiff_texts(text1, text2): d = Differ() return [ (token[2:], token[0] if\ntoken[0] != \" \" else None) for token in d.compare(text1, text2) ] demo =\ngr.Interface( diff_texts, [ gr.Textbox( label=\"Text 1\", info=\"Initial text\",\nlines=3, value=\"The quick brown fox jumped over the lazy dogs.\", ),\ngr.Textbox( label=\"Text 2\", info=\"Text to compare\", lines=3, value=\"The fast\nbrown fox jumps over lazy dogs.\", ), ], gr.HighlightedText( label=\"Diff\",\ncombine_adjacent=True, show_legend=True, color_map={\"+\": \"red\", \"-\":\n\"green\"}), theme=gr.themes.Base() ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    from difflib import Differ\n    \n    import gradio as gr\n    \n    def diff_texts(text1, text2):\n        d = Differ()\n        return [\n            (token[2:], token[0] if token[0] != \" \" else None)\n            for token in d.compare(text1, text2)\n        ]\n    \n    demo = gr.Interface(\n        diff_texts,\n        [\n            gr.Textbox(\n                label=\"Text 1\",\n                info=\"Initial text\",\n                lines=3,\n                value=\"The quick brown fox jumped over the lazy dogs.\",\n            ),\n            gr.Textbox(\n                label=\"Text 2\",\n                info=\"Text to compare\",\n                lines=3,\n                value=\"The fast brown fox jumps over lazy dogs.\",\n            ),\n        ],\n        gr.HighlightedText(\n            label=\"Diff\",\n            combine_adjacent=True,\n            show_legend=True,\n            color_map={\"+\": \"red\", \"-\": \"green\"}),\n        theme=gr.themes.Base()\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe HighlightedText component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`HighlightedText.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the HighlightedText changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`HighlightedText.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the HighlightedText. Uses event data gradio.SelectData to carry `value` referring to the label of the HighlightedText, and `selected` to refer to state of the HighlightedText. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "s should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "on on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": " `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "| str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "The Progress class provides a custom progress tracker that is used in a\nfunction signature. To attach a Progress tracker to a function, simply add a\nparameter right after the input parameters that has a default value set to a\n`gradio.Progress()` instance. The Progress tracker can then be updated in the\nfunction by calling the Progress object or using the `tqdm` method on an\nIterable.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "import gradio as gr\n    import time\n    def my_function(x, progress=gr.Progress()):\n        progress(0, desc=\"Starting...\")\n        time.sleep(1)\n        for i in progress.tqdm(range(100)):\n            time.sleep(0.1)\n        return x\n    gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    track_tqdm: bool\n\ndefault `= False`\n\nIf True, the Progress object will track any tqdm.tqdm iterations with the tqdm\nlibrary in the function.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Progress.__call__(progress, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fontic", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nUpdates progress tracker with progress and message text.\n\nParameters \u25bc\n\n\n    \n    \n    progress: float | tuple[int, int | None] | None\n\nIf float, should be between 0 and 1 representing completion. If Tuple, first\nnumber represents steps completed, and second value represents total steps or\nNone if unknown. If None, hides progress bar.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total ", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "\nNone if unknown. If None, hides progress bar.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Progress.tqdm(iterable, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nAttaches progress tracker to iterable, like tqdm.\n\nParameters \u25bc\n\n\n    \n    \n    iterable: Iterable | None\n\niterable to attach progress tracker to.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Progress.__call__(progress, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fontic", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nUpdates progress tracker with progress and message text.\n\nParameters \u25bc\n\n\n    \n    \n    progress: float | tuple[int, int | None] | None\n\nIf float, should be between 0 and 1 representing completion. If Tuple, first\nnumber represents steps completed, and second value represents total steps or\nNone if unknown. If None, hides progress bar.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total ", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "\nNone if unknown. If None, hides progress bar.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Progress.tqdm(iterable, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nAttaches progress tracker to iterable, like tqdm.\n\nParameters \u25bc\n\n\n    \n    \n    iterable: Iterable | None\n\niterable to attach progress tracker to.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "Any code in a `if gr.NO_RELOAD` code-block will not be re-evaluated when\nthe source file is reloaded. This is helpful for importing modules that do not\nlike to be reloaded (tiktoken, numpy) as well as database connections and long\nrunning set up code.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/NO_RELOAD", "source_page_title": "Gradio - No_Reload Docs"}, {"text": "import gradio as gr\n    \n    if gr.NO_RELOAD:\n    \tfrom transformers import pipeline\n    \tpipe = pipeline(\"text-classification\", model=\"cardiffnlp/twitter-roberta-base-sentiment-latest\")\n    \n    gr.Interface.from_pipeline(pipe).launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/NO_RELOAD", "source_page_title": "Gradio - No_Reload Docs"}, {"text": "Creates a scatter plot component to display data from a pandas DataFrame.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "**As input component** : The data to display in a line plot.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: AltairPlotData | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a pandas DataFrame containing the data to\ndisplay in the line plot. The DataFrame should contain at least two columns,\none for the x-axis (corresponding to this component's `x` argument) and one\nfor the y-axis (corresponding to `y`).\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> pd.DataFrame | dict | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: pd.DataFrame | Callable | None\n\ndefault `= None`\n\nThe pandas dataframe containing the data to display in the plot.\n\n\n    \n    \n    x: str | None\n\ndefault `= None`\n\nColumn corresponding to the x axis. Column can be numeric, datetime, or\nstring/category.\n\n\n    \n    \n    y: str | None\n\ndefault `= None`\n\nColumn corresponding to the y axis. Column must be numeric.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nColumn corresponding to series, visualized by color. Column must be\nstring/category.\n\n\n    \n    \n    title: str | None\n\ndefault `= None`\n\nThe title to display on top of the chart.\n\n\n    \n    \n    x_title: str | None\n\ndefault `= None`\n\nThe title given to the x axis. By default, uses the value of the x parameter.\n\n\n    \n    \n    y_title: str | None\n\ndefault `= None`\n\nThe title given to the y axis. By default, uses the value of the y parameter.\n\n\n    \n    \n    color_title: str | None\n\ndefault `= None`\n\nThe title given to the color legend. By default, uses the value of color\nparameter.\n\n\n    \n    \n    x_bin: str | float | None\n\ndefault `= None`\n\nGrouping used to cluster x values. If x column is numeric, should be number to\nbin the x values. If x column is datetime, should be string such as \"1h\",\n\"15m\", \"10s\", using \"s\", \"m\", \"h\", \"d\" suffixes.\n\n\n    \n    \n    y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None\n\ndefault `= None`\n\nAggregation function used to aggregate y values, used if x_bin is provided or\nx is a string/category. Must be one of \"sum\", \"mean\", \"median\", \"min\", \"max\".\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nMapping of series to color names or codes. For example, {\"success\": \"green\",\n\"fail\": \"FF8888\"}.\n\n\n    \n    \n    x_lim: list[float] | None\n\ndefault `= None`\n\nA tuple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "ple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA tuple of list containing the limits for the y-axis, specified as [y_min,\ny_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum to value.\n\n\n    \n    \n    x_label_angle: float\n\ndefault `= 0`\n\nThe angle of the x-axis labels in degrees offset clockwise.\n\n\n    \n    \n    y_label_angle: float\n\ndefault `= 0`\n\nThe angle of the y-axis labels in degrees offset clockwise.\n\n\n    \n    \n    x_axis_labels_visible: bool\n\ndefault `= True`\n\nWhether the x-axis labels should be visible. Can be hidden when many x-axis\nlabels are present.\n\n\n    \n    \n    caption: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) caption to display below the plot.\n\n\n    \n    \n    sort: Literal['x', 'y', '-x', '-y'] | list[str] | None\n\ndefault `= None`\n\nThe sorting order of the x values, if x column is type string/category. Can be\n\"x\", \"y\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n    \n    \n    tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hovering on a point. \"axis\" shows the values for\nthe axis columns, \"all\" shows all column values, and \"none\" shows no tooltips.\nCan also provide a list of strings representing columns to show in the\ntooltip, which will be displayed along with axis values.\n\n\n    \n    \n    height: int | None\n\ndefault `= None`\n\nThe height of the plot in pixels.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) label to display on the top left corner of the plot.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nWhether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Whether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the plot should be visible.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n  ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "default `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True, will show a button to make plot visible in fullscreen mode.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ScatterPlot` | \"scatterplot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "scatter_plot_demo\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd from random import randint, random import\ngradio as gr temp_sensor_data = pd.DataFrame( { \"time\":\npd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200), \"temperature\":\n[randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n\"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in\nrange(200)], \"location\": [\"indoor\", \"outdoor\"] * 100, } ) food_rating_data =\npd.DataFrame( { \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in\nrange(100)], \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n\"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)], \"wait\":\n[random() for i in range(100)], } ) with gr.Blocks() as scatter_plots: with\ngr.Row(): start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\") end =\ngr.DateTime(\"2021-01-05 00:00:00\", label=\"End\") apply_btn = gr.Button(\"Apply\",\nscale=0) with gr.Row(): group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"],\nvalue=\"None\", label=\"Group by\") aggregate = gr.Radio([\"sum\", \"mean\", \"median\",\n\"min\", \"max\"], value=\"sum\", label=\"Aggregation\") temp_by_time =\ngr.ScatterPlot( temp_sensor_data, x=\"time\", y=\"temperature\", )\ntemp_by_time_location = gr.ScatterPlot( temp_sensor_data, x=\"time\",\ny=\"temperature\", color=\"location\", ) time_graphs = [temp_by_time,\ntemp_by_time_location] group_by.change( lambda group:\n[gr.ScatterPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\ngroup_by, time_graphs ) aggregate.change( lambda aggregate:\n[gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs), aggregate,\ntime_graphs ) price_by_cuisine = gr.ScatterPlot( food_rating_data,\nx=\"cuisine\", y=\"price\", ) with gr.Row(): price_by_rating = gr.ScatterPlot(\nfood_rating_data, x=\"rating\", y=\"price\", color=\"wait\",\nshow_actions_button=True, ) price_by_rating_color = gr.ScatterPlot(\nfood_rating_data, x=\"rating\", y=\"price\", color=\"cuisine\", ) if __name__ ==\n\"__main__\": scatter_plots.launch()\n\n    \n    \n    import pandas as pd\n    from random i", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "ice_by_rating_color = gr.ScatterPlot(\nfood_rating_data, x=\"rating\", y=\"price\", color=\"cuisine\", ) if __name__ ==\n\"__main__\": scatter_plots.launch()\n\n    \n    \n    import pandas as pd\n    from random import randint, random\n    import gradio as gr\n    \n    \n    temp_sensor_data = pd.DataFrame(\n        {\n            \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n            \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"location\": [\"indoor\", \"outdoor\"] * 100,\n        }\n    )\n    \n    food_rating_data = pd.DataFrame(\n        {\n            \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n            \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n            \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n            \"wait\": [random() for i in range(100)],\n        }\n    )\n    \n    with gr.Blocks() as scatter_plots:\n        with gr.Row():\n            start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n            end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n            apply_btn = gr.Button(\"Apply\", scale=0)\n        with gr.Row():\n            group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n            aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n    \n        temp_by_time = gr.ScatterPlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n        )\n        temp_by_time_location = gr.ScatterPlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n            color=\"location\",\n        )\n    \n        time_graphs = [temp_by_time, temp_by_time_location]\n        group_by.change(\n            lambda group: [gr.ScatterPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n            group_by", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "me_graphs = [temp_by_time, temp_by_time_location]\n        group_by.change(\n            lambda group: [gr.ScatterPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n            group_by,\n            time_graphs\n        )\n        aggregate.change(\n            lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),\n            aggregate,\n            time_graphs\n        )\n    \n        price_by_cuisine = gr.ScatterPlot(\n            food_rating_data,\n            x=\"cuisine\",\n            y=\"price\",\n        )\n        with gr.Row():\n            price_by_rating = gr.ScatterPlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                color=\"wait\",\n                show_actions_button=True,\n            )\n            price_by_rating_color = gr.ScatterPlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                color=\"cuisine\",\n            )\n    \n    if __name__ == \"__main__\":\n        scatter_plots.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ScatterPlot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ScatterPlot.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the NativePlot. Uses event data gradio.SelectData to carry `value` referring to the label of the NativePlot, and `selected` to refer to state of the NativePlot. See EventData documentation on how to use this event data  \n`ScatterPlot.double_click(fn, \u00b7\u00b7\u00b7)` | Triggered when the NativePlot is double clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the e", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": " be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "e the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input a", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": ") would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Gradio features a built-in theming engine that lets you customize the look\nand feel of your app. You can choose from a variety of themes, or create your\nown. To do so, pass the `theme=` kwarg to the `Blocks` or `Interface`\nconstructor. For example:\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Soft()) as demo:\n        ...\n\nGradio comes with a set of prebuilt themes which you can load from\n`gr.themes.*`. These are:\n\n  * \u2014 `gr.themes.Base()`\n  * \u2014 `gr.themes.Default()`\n  * \u2014 `gr.themes.Glass()`\n  * \u2014 `gr.themes.Monochrome()`\n  * \u2014 `gr.themes.Soft()`\n\nEach of these themes set values for hundreds of CSS variables. You can use\nprebuilt themes as a starting point for your own custom themes, or you can\ncreate your own themes from scratch. Let\u2019s take a look at each approach.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The easiest way to build a theme is using the Theme Builder. To launch the\nTheme Builder locally, run the following code:\n\n    \n    \n    import gradio as gr\n    \n    gr.themes.builder()\n\nYou can use the Theme Builder running on Spaces above, though it runs much\nfaster when you launch it locally via `gr.themes.builder()`.\n\nAs you edit the values in the Theme Builder, the app will preview updates\nin real time. You can download the code to generate the theme you\u2019ve created\nso you can use it in any Gradio app.\n\nIn the rest of the guide, we will cover building themes programmatically.\n\n", "heading1": "Using the Theme Builder", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Constructor\n\nAlthough each theme has hundreds of CSS variables, the values for most\nthese variables are drawn from 8 core variables which can be set through the\nconstructor of each prebuilt theme. Modifying these 8 arguments allows you to\nquickly change the look and feel of your app.\n\n", "heading1": "Extending Themes via the", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The first 3 constructor arguments set the colors of the theme and are\n`gradio.themes.Color` objects. Internally, these Color objects hold brightness\nvalues for the palette of a single hue, ranging from 50, 100, 200\u2026, 800, 900,\n950. Other CSS variables are derived from these 3 colors.\n\nThe 3 color constructor arguments are:\n\n  * \u2014 `primary_hue`: This is the color draws attention in your theme. In the default theme, this is set to `gradio.themes.colors.orange`.\n  * \u2014 `secondary_hue`: This is the color that is used for secondary elements in your theme. In the default theme, this is set to `gradio.themes.colors.blue`.\n  * \u2014 `neutral_hue`: This is the color that is used for text and other neutral elements in your theme. In the default theme, this is set to `gradio.themes.colors.gray`.\n\nYou could modify these values using their string shortcuts, such as\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(primary_hue=\"red\", secondary_hue=\"pink\")) as demo:\n        ...\n\nor you could use the `Color` objects directly, like this:\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.pink)) as demo:\n        ...\n\nPredefined colors are:\n\n  * \u2014 `slate`\n  * \u2014 `gray`\n  * \u2014 `zinc`\n  * \u2014 `neutral`\n  * \u2014 `stone`\n  * \u2014 `red`\n  * \u2014 `orange`\n  * \u2014 `amber`\n  * \u2014 `yellow`\n  * \u2014 `lime`\n  * \u2014 `green`\n  * \u2014 `emerald`\n  * \u2014 `teal`\n  * \u2014 `cyan`\n  * \u2014 `sky`\n  * \u2014 `blue`\n  * \u2014 `indigo`\n  * \u2014 `violet`\n  * \u2014 `purple`\n  * \u2014 `fuchsia`\n  * \u2014 `pink`\n  * \u2014 `rose`\n\nYou could also create your own custom `Color` objects and pass them in.\n\n", "heading1": "Core Colors", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The next 3 constructor arguments set the sizing of the theme and are\n`gradio.themes.Size` objects. Internally, these Size objects hold pixel size\nvalues that range from `xxs` to `xxl`. Other CSS variables are derived from\nthese 3 sizes.\n\n  * \u2014 `spacing_size`: This sets the padding within and spacing between elements. In the default theme, this is set to `gradio.themes.sizes.spacing_md`.\n  * \u2014 `radius_size`: This sets the roundedness of corners of elements. In the default theme, this is set to `gradio.themes.sizes.radius_md`.\n  * \u2014 `text_size`: This sets the font size of text. In the default theme, this is set to `gradio.themes.sizes.text_md`.\n\nYou could modify these values using their string shortcuts, such as\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(spacing_size=\"sm\", radius_size=\"none\")) as demo:\n        ...\n\nor you could use the `Size` objects directly, like this:\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(spacing_size=gr.themes.sizes.spacing_sm, radius_size=gr.themes.sizes.radius_none)) as demo:\n        ...\n\nThe predefined size objects are:\n\n  * \u2014 `radius_none`\n  * \u2014 `radius_sm`\n  * \u2014 `radius_md`\n  * \u2014 `radius_lg`\n  * \u2014 `spacing_sm`\n  * \u2014 `spacing_md`\n  * \u2014 `spacing_lg`\n  * \u2014 `text_sm`\n  * \u2014 `text_md`\n  * \u2014 `text_lg`\n\nYou could also create your own custom `Size` objects and pass them in.\n\n", "heading1": "Core Sizing", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The final 2 constructor arguments set the fonts of the theme. You can pass\na list of fonts to each of these arguments to specify fallbacks. If you\nprovide a string, it will be loaded as a system font. If you provide a\n`gradio.themes.GoogleFont`, the font will be loaded from Google Fonts.\n\n  * \u2014 `font`: This sets the primary font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"Source Sans Pro\")`.\n  * \u2014 `font_mono`: This sets the monospace font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Mono\")`.\n\nYou could modify these values such as the following:\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont(\"Inconsolata\"), \"Arial\", \"sans-serif\"])) as demo:\n        ...\n\n", "heading1": "Core Fonts", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "You can also modify the values of CSS variables after the theme has been\nloaded. To do so, use the `.set()` method of the theme object to get access to\nthe CSS variables. For example:\n\n    \n    \n    theme = gr.themes.Default(primary_hue=\"blue\").set(\n        loader_color=\"FF0000\",\n        slider_color=\"FF0000\",\n    )\n    \n    with gr.Blocks(theme=theme) as demo:\n        ...\n\nIn the example above, we\u2019ve set the `loader_color` and `slider_color`\nvariables to `FF0000`, despite the overall `primary_color` using the blue\ncolor palette. You can set any CSS variable that is defined in the theme in\nthis manner.\n\nYour IDE type hinting should help you navigate these variables. Since there\nare so many CSS variables, let\u2019s take a look at how these variables are named\nand organized.\n\n", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Conventions\n\nCSS variable names can get quite long, like\n`button_primary_background_fill_hover_dark`! However they follow a common\nnaming convention that makes it easy to understand what they do and to find\nthe variable you\u2019re looking for. Separated by underscores, the variable name\nis made up of:\n\n  * \u2014 1. The target element, such as `button`, `slider`, or `block`.\n  * \u2014 2. The target element type or sub-element, such as `button_primary`, or `block_label`.\n  * \u2014 3. The property, such as `button_primary_background_fill`, or `block_label_border_width`.\n  * \u2014 4. Any relevant state, such as `button_primary_background_fill_hover`.\n  * \u2014 5. If the value is different in dark mode, the suffix `_dark`. For example, `input_border_color_focus_dark`.\n\nOf course, many CSS variable names are shorter than this, such as\n`table_border_color`, or `input_shadow`.\n\n", "heading1": "CSS Variable Naming", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Though there are hundreds of CSS variables, they do not all have to have\nindividual values. They draw their values by referencing a set of core\nvariables and referencing each other. This allows us to only have to modify a\nfew variables to change the look and feel of the entire theme, while also\ngetting finer control of individual elements that we may want to modify.\n\nReferencing Core Variables\n\nTo reference one of the core constructor variables, precede the variable\nname with an asterisk. To reference a core color, use the `*primary_`,\n`*secondary_`, or `*neutral_` prefix, followed by the brightness value. For\nexample:\n\n    \n    \n    theme = gr.themes.Default(primary_hue=\"blue\").set(\n        button_primary_background_fill=\"*primary_200\",\n        button_primary_background_fill_hover=\"*primary_300\",\n    )\n\nIn the example above, we\u2019ve set the `button_primary_background_fill` and\n`button_primary_background_fill_hover` variables to `*primary_200` and\n`*primary_300`. These variables will be set to the 200 and 300 brightness\nvalues of the blue primary color palette, respectively.\n\nSimilarly, to reference a core size, use the `*spacing_`, `*radius_`, or\n`*text_` prefix, followed by the size value. For example:\n\n    \n    \n    theme = gr.themes.Default(radius_size=\"md\").set(\n        button_primary_border_radius=\"*radius_xl\",\n    )\n\nIn the example above, we\u2019ve set the `button_primary_border_radius` variable\nto `*radius_xl`. This variable will be set to the `xl` setting of the medium\nradius size range.\n\n", "heading1": "CSS Variable Organization", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Variables can also reference each other. For example, look at the example\nbelow:\n\n    \n    \n    theme = gr.themes.Default().set(\n        button_primary_background_fill=\"FF0000\",\n        button_primary_background_fill_hover=\"FF0000\",\n        button_primary_border=\"FF0000\",\n    )\n\nHaving to set these values to a common color is a bit tedious. Instead, we\ncan reference the `button_primary_background_fill` variable in the\n`button_primary_background_fill_hover` and `button_primary_border` variables,\nusing a `*` prefix.\n\n    \n    \n    theme = gr.themes.Default().set(\n        button_primary_background_fill=\"FF0000\",\n        button_primary_background_fill_hover=\"*button_primary_background_fill\",\n        button_primary_border=\"*button_primary_background_fill\",\n    )\n\nNow, if we change the `button_primary_background_fill` variable, the\n`button_primary_background_fill_hover` and `button_primary_border` variables\nwill automatically update as well.\n\nThis is particularly useful if you intend to share your theme - it makes it\neasy to modify the theme without having to change every variable.\n\nNote that dark mode variables automatically reference each other. For\nexample:\n\n    \n    \n    theme = gr.themes.Default().set(\n        button_primary_background_fill=\"FF0000\",\n        button_primary_background_fill_dark=\"AAAAAA\",\n        button_primary_border=\"*button_primary_background_fill\",\n        button_primary_border_dark=\"*button_primary_background_fill_dark\",\n    )\n\n`button_primary_border_dark` will draw its value from\n`button_primary_background_fill_dark`, because dark mode always draw from the\ndark version of the variable.\n\n", "heading1": "Referencing Other Variables", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Let\u2019s say you want to create a theme from scratch! We\u2019ll go through it step\nby step - you can also see the source of prebuilt themes in the gradio source\nrepo for reference - [here\u2019s the source](https://github.com/gradio-\napp/gradio/blob/main/gradio/themes/monochrome.py) for the Monochrome theme.\n\nOur new theme class will inherit from `gradio.themes.Base`, a theme that\nsets a lot of convenient defaults. Let\u2019s make a simple demo that creates a\ndummy theme called Seafoam, and make a simple app that uses it.\n\n$code_theme_new_step_1\n\nThe Base theme is very barebones, and uses `gr.themes.Blue` as it primary\ncolor - you\u2019ll note the primary button and the loading animation are both blue\nas a result. Let\u2019s change the defaults core arguments of our app. We\u2019ll\noverwrite the constructor and pass new defaults for the core constructor\narguments.\n\nWe\u2019ll use `gr.themes.Emerald` as our primary color, and set secondary and\nneutral hues to `gr.themes.Blue`. We\u2019ll make our text larger using `text_lg`.\nWe\u2019ll use `Quicksand` as our default font, loaded from Google Fonts.\n\n$code_theme_new_step_2\n\nSee how the primary button and the loading animation are now green? These CSS\nvariables are tied to the `primary_hue` variable.\n\nLet\u2019s modify the theme a bit more directly. We\u2019ll call the `set()` method to\noverwrite CSS variable values explicitly. We can use any CSS logic, and\nreference our core constructor arguments using the `*` prefix.\n\n$code_theme_new_step_3\n\nLook how fun our theme looks now! With just a few variable changes, our theme\nlooks completely different.\n\nYou may find it helpful to explore the [source code of the other prebuilt\nthemes](https://github.com/gradio-app/gradio/blob/main/gradio/themes) to see\nhow they modified the base theme. You can also find your browser\u2019s Inspector\nuseful to select elements from the UI and see what CSS variables are being\nused in the styles panel.\n\nSharing Themes\n\nOnce you have created a theme, you can upload it to the HuggingFace Hub to", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "ctor\nuseful to select elements from the UI and see what CSS variables are being\nused in the styles panel.\n\nSharing Themes\n\nOnce you have created a theme, you can upload it to the HuggingFace Hub to let\nothers view it, use it, and build off of it!\n\n", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "There are two ways to upload a theme, via the theme class instance or the\ncommand line. We will cover both of them with the previously created `seafoam`\ntheme.\n\n  * Via the class instance\n\nEach theme instance has a method called `push_to_hub` we can use to upload a\ntheme to the HuggingFace hub.\n\n    \n    \n    seafoam.push_to_hub(repo_name=\"seafoam\",\n                        version=\"0.0.1\",\n    \t\t\t\t\thf_token=\"<token>\")\n\n  * Via the command line\n\nFirst save the theme to disk\n\n    \n    \n    seafoam.dump(filename=\"seafoam.json\")\n\nThen use the `upload_theme` command:\n\n    \n    \n    upload_theme\\\n    \"seafoam.json\"\\\n    \"seafoam\"\\\n    --version \"0.0.1\"\\\n    --hf_token \"<token>\"\n\nIn order to upload a theme, you must have a HuggingFace account and pass your\n[Access Token](https://huggingface.co/docs/huggingface_hub/quick-startlogin)\nas the `hf_token` argument. However, if you log in via the [HuggingFace\ncommand line](https://huggingface.co/docs/huggingface_hub/quick-startlogin)\n(which comes installed with `gradio`), you can omit the `hf_token` argument.\n\nThe `version` argument lets you specify a valid [semantic\nversion](https://www.geeksforgeeks.org/introduction-semantic-versioning/)\nstring for your theme. That way your users are able to specify which version\nof your theme they want to use in their apps. This also lets you publish\nupdates to your theme without worrying about changing how previously created\napps look. The `version` argument is optional. If omitted, the next patch\nversion is automatically applied.\n\n", "heading1": "Uploading a Theme", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "By calling `push_to_hub` or `upload_theme`, the theme assets will be stored in\na [HuggingFace space](https://huggingface.co/docs/hub/spaces-overview).\n\nThe theme preview for our seafoam theme is here: [seafoam\npreview](https://huggingface.co/spaces/gradio/seafoam).\n\n", "heading1": "Theme Previews", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The [Theme Gallery](https://huggingface.co/spaces/gradio/theme-gallery) shows\nall the public gradio themes. After publishing your theme, it will\nautomatically show up in the theme gallery after a couple of minutes.\n\nYou can sort the themes by the number of likes on the space and from most to\nleast recently created as well as toggling themes between light and dark mode.\n\n", "heading1": "Discovering Themes", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "To use a theme from the hub, use the `from_hub` method on the `ThemeClass` and\npass it to your app:\n\n    \n    \n    my_theme = gr.Theme.from_hub(\"gradio/seafoam\")\n    \n    with gr.Blocks(theme=my_theme) as demo:\n        ....\n\nYou can also pass the theme string directly to `Blocks` or `Interface`\n(`gr.Blocks(theme=\"gradio/seafoam\")`)\n\nYou can pin your app to an upstream theme version by using semantic versioning\nexpressions.\n\nFor example, the following would ensure the theme we load from the `seafoam`\nrepo was between versions `0.0.1` and `0.1.0`:\n\n    \n    \n    with gr.Blocks(theme=\"gradio/seafoam@>=0.0.1,<0.1.0\") as demo:\n        ....\n\nEnjoy creating your own themes! If you make one you\u2019re proud of, please share\nit with the world by uploading it to the hub! If you tag us on\n[Twitter](https://twitter.com/gradio) we can give your theme a shout out!\n\n", "heading1": "Downloading", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Creates a textarea for user to enter string input or display string output.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "**As input component** : Passes text value as a `str` into the function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` returned from function and sets\ntextarea value to it.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | I18nData | Callable | None\n\ndefault `= None`\n\ntext to show in textbox. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    type: Literal['text', 'password', 'email']\n\ndefault `= \"text\"`\n\nThe type of textbox. One of: 'text' (which allows users to enter any text),\n'password' (which masks text entered by the user), 'email' (which suggests\nemail input to the browser). For \"password\" and \"email\" types, `lines` must be\n1 and `max_lines` must be None or 1.\n\n\n    \n    \n    lines: int\n\ndefault `= 1`\n\nminimum number of line rows to provide in textarea.\n\n\n    \n    \n    max_lines: int | None\n\ndefault `= None`\n\nmaximum number of line rows to provide in textarea. Must be at least `lines`.\nIf not provided, the maximum number of lines is max(lines, 20) for \"text\"\ntype, and 1 for \"password\" and \"email\" types.\n\n\n    \n    \n    placeholder: str | I18nData | None\n\ndefault `= None`\n\nplaceholder hint to provide behind textarea.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value`", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display the label. If False, the copy button is hidden as well\nas well as the label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= False`\n\nIf True, will focus on the textbox when the page loads. Use this carefully, as\nit can cause usability issues for sighted and non-sighted users.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textb", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "se this carefully, as\nit can cause usability issues for sighted and non-sighted users.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    text_align: Literal['left', 'right'] | None\n\ndefault `= None`\n\nHow to align the text in the textbox, can be: \"left\", \"right\", or None\n(default). If None, the alignment is left if `rtl` is False, or right if `rtl`\nis True. Can only be changed if `type` is \"text\".\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True and `type` is \"text\", sets the direction of the text to right-to-left\n(cursor appears on the left of the text). Default is False, which renders\ncursor on the right.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf Tru", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " direction of the text to right-to-left\n(cursor appears on the left of the text). Default is False, which renders\ncursor on the right.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, includes a copy button to copy the text in the textbox. Only applies\nif show_label is True.\n\n\n    \n    \n    max_length: int | None\n\ndefault `= None`\n\nmaximum number of characters (including newlines) allowed in the textbox. If\nNone, there is no maximum length.\n\n\n    \n    \n    submit_btn: str | bool | None\n\ndefault `= False`\n\nIf False, will not show a submit button. If True, will show a submit button\nwith an icon. If a string, will use that string as the submit button text.\nWhen the submit button is shown, the border of the textbox will be removed,\nwhich is useful for creating a chat interface.\n\n\n    \n    \n    stop_btn: str | bool | None\n\ndefault `= False`\n\nIf False, will not show a stop button. If True, will show a stop button with\nan icon. If a string, will use that string as the stop button text. When the\nstop button is shown, the border of the textbox will be removed, which is\nuseful for creating a chat interface.\n\n\n    \n    \n    html_attributes: InputHTMLAttributes | None\n\ndefault `= None`\n\nAn instance of gr.InputHTMLAttributes, which can be used to set HTML\nattributes for the input/textarea elements. Example:\nInputHTMLAttributes(autocorrect=\"off\", spellcheck=False) to disable\nautocorrect and spellcheck.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Textbox` | \"textbox\" | Uses default values  \n`gradio.TextArea` | \"textarea\" | Uses lines=7  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "hello_worlddiff_textssentence_builder\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name): return \"Hello \" + name + \"!\"\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\") if __name__\n== \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    \n    def greet(name):\n        return \"Hello \" + name + \"!\"\n    \n    \n    demo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 from difflib import Differ import gradio as gr def\ndiff_texts(text1, text2): d = Differ() return [ (token[2:], token[0] if\ntoken[0] != \" \" else None) for token in d.compare(text1, text2) ] demo =\ngr.Interface( diff_texts, [ gr.Textbox( label=\"Text 1\", info=\"Initial text\",\nlines=3, value=\"The quick brown fox jumped over the lazy dogs.\", ),\ngr.Textbox( label=\"Text 2\", info=\"Text to compare\", lines=3, value=\"The fast\nbrown fox jumps over lazy dogs.\", ), ], gr.HighlightedText( label=\"Diff\",\ncombine_adjacent=True, show_legend=True, color_map={\"+\": \"red\", \"-\":\n\"green\"}), theme=gr.themes.Base() ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    from difflib import Differ\n    \n    import gradio as gr\n    \n    def diff_texts(text1, text2):\n        d = Differ()\n        return [\n            (token[2:], token[0] if token[0] != \" \" else None)\n            for token in d.compare(text1, text2)\n        ]\n    \n    demo = gr.Interface(\n        diff_texts,\n        [\n            gr.Textbox(\n                label=\"Text 1\",\n                info=\"Initial text\",\n                lines=3,\n                value=\"The quick brown fox jumped over the lazy dogs.\",\n            ),\n            gr.Textbox(\n                label=\"Text 2\",\n                info=\"Text to compare\",\n                lines=3,\n                value=\"The fast brown fox jumps over lazy dogs.\",\n            ),\n        ],\n        gr.HighlightedText(\n            label=\"Diff\",\n            combine_adjacent=True,\n            show_legend=True,\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "  value=\"The fast brown fox jumps over lazy dogs.\",\n            ),\n        ],\n        gr.HighlightedText(\n            label=\"Diff\",\n            combine_adjacent=True,\n            show_legend=True,\n            color_map={\"+\": \"red\", \"-\": \"green\"}),\n        theme=gr.themes.Base()\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Textbox component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Textbox.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Textbox changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Textbox.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Textbox.  \n`Textbox.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Textbox. Uses event data gradio.SelectData to carry `value` referring to the label of the Textbox, and `selected` to refer to state of the Textbox. See EventData documentation on how to use this event data  \n`Textbox.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the Textbox is focused.  \n`Textbox.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Textbox is focused.  \n`Textbox.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Textbox is unfocused/blurred.  \n`Textbox.stop(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the Textbox.  \n`Textbox.copy(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user copies content from the Textbox. Uses event data gradio.CopyData to carry information about the copied content. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to cal", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Lite", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show thi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "A Gradio Interface includes a \u2018Flag\u2019 button that appears underneath the\noutput. By default, clicking on the Flag button sends the input and output\ndata back to the machine where the gradio demo is running, and saves it to a\nCSV log file. But this default behavior can be changed. To set what happens\nwhen the Flag button is clicked, you pass an instance of a subclass of\n_FlaggingCallback_ to the _flagging_callback_ parameter in the _Interface_\nconstructor. You can use one of the _FlaggingCallback_ subclasses that are\nlisted below, or you can create your own, which lets you do whatever you want\nwith the data that is being flagged.\n\nSimpleCSVLogger\n\n    \n    \n    gradio.SimpleCSVLogger(\u00b7\u00b7\u00b7)\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "A simplified implementation of the FlaggingCallback abstract class provided\nfor illustrative purposes. Each flagged sample (both the input and output\ndata) is logged to a CSV file on the machine running the gradio app.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "import gradio as gr\n    def image_classifier(inp):\n        return {'cat': 0.3, 'dog': 0.7}\n    demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\",\n                        flagging_callback=SimpleCSVLogger())\n\nCSVLogger\n\n    \n    \n    gradio.CSVLogger(\u00b7\u00b7\u00b7)\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "The default implementation of the FlaggingCallback abstract class in\ngradio>=5.0. Each flagged sample (both the input and output data) is logged to\na CSV file with headers on the machine running the gradio app. Unlike\nClassicCSVLogger, this implementation is concurrent-safe and it creates a new\ndataset file every time the headers of the CSV (derived from the labels of the\ncomponents) change. It also only creates columns for \"username\" and \"flag\" if\nthe flag_option and username are provided, respectively.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "import gradio as gr\n    def image_classifier(inp):\n        return {'cat': 0.3, 'dog': 0.7}\n    demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\",\n                        flagging_callback=CSVLogger())\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    simplify_file_data: bool\n\ndefault `= True`\n\nIf True, the file data will be simplified before being written to the CSV\nfile. If CSVLogger is being used to cache examples, this is set to False to\npreserve the original FileData class\n\n\n    \n    \n    verbose: bool\n\ndefault `= True`\n\nIf True, prints messages to the console about the dataset file creation\n\n\n    \n    \n    dataset_file_name: str | None\n\ndefault `= None`\n\nThe name of the dataset file to be created (should end in \".csv\"). If None,\nthe dataset file will be named \"dataset1.csv\" or the next available number.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "ChatInterface is Gradio's high-level abstraction for creating chatbot UIs,\nand allows you to create a web-based demo around a chatbot model in a few\nlines of code. Only one parameter is required: fn, which takes a function that\ngoverns the response of the chatbot based on the user input and chat history.\nAdditional parameters can be used to control the appearance and behavior of\nthe demo.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "**Basic Example** : A chatbot that echoes back the users\u2019s message\n\n    \n    \n    import gradio as gr\n    \n    def echo(message, history):\n        return message\n    \n    demo = gr.ChatInterface(fn=echo, type=\"messages\", examples=[\"hello\", \"hola\", \"merhaba\"], title=\"Echo Bot\")\n    demo.launch()\n\n**Custom Chatbot** : A `gr.ChatInterface` with a custom `gr.Chatbot` that\nincludes a placeholder as well as upvote/downvote buttons. The upvote/downvote\nbuttons are automatically added when a `.like()` event is attached to a\n`gr.Chatbot`. In order to attach event listeners to your custom chatbot, wrap\nthe `gr.Chatbot` as well as the `gr.ChatInterface` inside of a `gr.Blocks`\nlike this:\n\n    \n    \n    import gradio as gr\n    \n    def yes(message, history):\n        return \"yes\"\n    \n    def vote(data: gr.LikeData):\n        if data.liked:\n            print(\"You upvoted this response: \" + data.value[\"value\"])\n        else:\n            print(\"You downvoted this response: \" + data.value[\"value\"])\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(placeholder=\"<strong>Your Personal Yes-Man</strong><br>Ask Me Anything\")\n        chatbot.like(vote, None, None)\n        gr.ChatInterface(fn=yes, type=\"messages\", chatbot=chatbot)\n        \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    fn: Callable\n\nthe function to wrap the chat interface around. Normally (assuming `type` is set to \"messages\"), the function should accept two parameters: a `str` representing the input message and `list` of openai-style dictionaries: {\"role\": \"user\" | \"assistant\", \"content\": `str` | {\"path\": `str`} | `gr.Component`} representing the chat history. The function should return/yield a `str` (for a simple message), a supported Gradio component (e.g. gr.Image to return an image), a `dict` (for a complete openai-style message response), or a `list` of such messages.\n\n\n    \n    \n    multimodal: bool\n\ndefault `= False`\n\nif True, the chat interface will use a `gr.MultimodalTextbox` component for\nthe input, which allows for the uploading of multimedia files. If False, the\nchat interface will use a gr.Textbox component for the input. If this is True,\nthe first argument of `fn` should accept not a `str` message but a `dict`\nmessage with keys \"text\" and \"files\"\n\n\n    \n    \n    type: Literal['messages', 'tuples'] | None\n\ndefault `= None`\n\nThe format of the messages passed into the chat history parameter of `fn`. If \"messages\", passes the history as a list of dictionaries with openai-style \"role\" and \"content\" keys. The \"content\" key's value should be one of the following - (1) strings in valid Markdown (2) a dictionary with a \"path\" key and value corresponding to the file to display or (3) an instance of a Gradio component: at the moment gr.Image, gr.Plot, gr.Video, gr.Gallery, gr.Audio, and gr.HTML are supported. The \"role\" key should be one of 'user' or 'assistant'. Any other roles will not be displayed in the output. If this parameter is 'tuples' (deprecated), passes the chat history as a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message.\n\n\n    \n    \n    chatbot: Chatbot | None\n\ndefault `= None`\n\nan instance of the gr.Chatbot component to use for the chat interfac", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": " list should have 2 elements: the user message and the response message.\n\n\n    \n    \n    chatbot: Chatbot | None\n\ndefault `= None`\n\nan instance of the gr.Chatbot component to use for the chat interface, if you\nwould like to customize the chatbot properties. If not provided, a default\ngr.Chatbot component will be created.\n\n\n    \n    \n    textbox: Textbox | MultimodalTextbox | None\n\ndefault `= None`\n\nan instance of the gr.Textbox or gr.MultimodalTextbox component to use for the\nchat interface, if you would like to customize the textbox properties. If not\nprovided, a default gr.Textbox or gr.MultimodalTextbox component will be\ncreated.\n\n\n    \n    \n    additional_inputs: str | Component | list[str | Component] | None\n\ndefault `= None`\n\nan instance or list of instances of gradio components (or their string\nshortcuts) to use as additional inputs to the chatbot. If the components are\nnot already rendered in a surrounding Blocks, then the components will be\ndisplayed under the chatbot, in an accordion. The values of these components\nwill be passed into `fn` as arguments in order after the chat history.\n\n\n    \n    \n    additional_inputs_accordion: str | Accordion | None\n\ndefault `= None`\n\nif a string is provided, this is the label of the `gr.Accordion` to use to\ncontain additional inputs. A `gr.Accordion` object can be provided as well to\nconfigure other properties of the container holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    additional_outputs: Component | list[Component] | None\n\ndefault `= None`\n\nan instance or list of instances of gradio components to use as additional\noutputs from the chat function. These must be components that are already\ndefined in the same Blocks scope. If provided, the chat function should return\nadditional values for these components. See\n[demo/chatinterface_artifacts](https://gradio.app/playground?demo=Blank&code=a", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "efined in the same Blocks scope. If provided, the chat function should return\nadditional values for these components. See\n[demo/chatinterface_artifacts](https://gradio.app/playground?demo=Blank&code=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%2BPGgxPldyaXRlIFB5dGhvbiBvciBKYXZhU2NyaXB0PC9oMT48L2NlbnRlcj4iKQogICAgICAgICAgICBnci5DaGF0SW50ZXJmYWNlKAogICAgICAgICAgICAgICAgY2hhdCwKICAgICAgICAgICAgICAgIGV4YW1wbGVzPVsiUHl0aG9uIiwgIkphdmFTY3JpcHQiXSwKICAgICAgICAgICAgICAgIGFkZGl0aW9uYWxfb3V0cHV0cz1bY29kZV0sCiAgICAgICAgICAgICAgICB0eXBlPSJtZXNzYWdlcyIKICAgICAgICAgICAgKQogICAgICAgIHdpdGggZ3IuQ29sdW1uKCk6CiAgICAgICAgICAgIGdyLk1hcmtkb3duKCI8Y2VudGVyPjxoMT5Db2RlIEFydGlmYWN0czwvaDE%2BPC9jZW50ZXI%2BIikKICAgICAgICAgICAgY29kZS5yZW5kZXIoKQoKZGVtby5sYXVuY2goKQo%3D).\n\n\n    \n    \n    editable: bool\n\ndefault `= False`\n\nif True, users can edit past messages to regenerate responses.\n\n\n    \n    \n    examples: list[str] | list[MultimodalValue] | list[list] | N", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "oKQo%3D).\n\n\n    \n    \n    editable: bool\n\ndefault `= False`\n\nif True, users can edit past messages to regenerate responses.\n\n\n    \n    \n    examples: list[str] | list[MultimodalValue] | list[list] | None\n\ndefault `= None`\n\nsample inputs for the function; if provided, appear within the chatbot and can\nbe clicked to populate the chatbot input. Should be a list of strings\nrepresenting text-only examples, or a list of dictionaries (with keys `text`\nand `files`) representing multimodal examples. If `additional_inputs` are\nprovided, the examples must be a list of lists, where the first element of\neach inner list is the string or dictionary example message and the remaining\nelements are the example values for the additional inputs -- in this case, the\nexamples will appear under the chatbot.\n\n\n    \n    \n    example_labels: list[str] | None\n\ndefault `= None`\n\nlabels for the examples, to be displayed instead of the examples themselves.\nIf provided, should be a list of strings with the same length as the examples\nlist. Only applies when examples are displayed within the chatbot (i.e. when\n`additional_inputs` is not provided).\n\n\n    \n    \n    example_icons: list[str] | None\n\ndefault `= None`\n\nicons for the examples, to be displayed above the examples. If provided,\nshould be a list of string URLs or local paths with the same length as the\nexamples list. Only applies when examples are displayed within the chatbot\n(i.e. when `additional_inputs` is not provided).\n\n\n    \n    \n    run_examples_on_click: bool\n\ndefault `= True`\n\nif True, clicking on an example will run the example through the chatbot fn\nand the response will be displayed in the chatbot. If False, clicking on an\nexample will only populate the chatbot input with the example message. Has no\neffect if `cache_examples` is True\n\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nif True, caches examples in the server for fast runtime in examples. The\ndefault option in HuggingFace Spaces is True. The default option", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nif True, caches examples in the server for fast runtime in examples. The\ndefault option in HuggingFace Spaces is True. The default option elsewhere is\nFalse. Note that examples are cached separately from Gradio's queue() so\ncertain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will\nnot be displayed in Gradio's UI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"eager\", all examples are cached at app launch. If \"lazy\", examples are\ncached for all users after the first use by any user of the app. If None, will\nuse the GRADIO_CACHE_MODE environment variable if defined, or default to\n\"eager\".\n\n\n    \n    \n    title: str | I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above chatbot in large font.\nAlso used as the tab title when opened in a browser window.\n\n\n    \n    \n    description: str | None\n\ndefault `= None`\n\na description for the interface; if provided, appears above the chatbot and\nbeneath the title in regular font. Accepts Markdown and HTML content.\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\na Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    flagging_mode: Literal['never', 'manual'] | None\n\ndefault `= None`\n\none of \"never\", \"manual\". If \"never\", users will not see a button to flag an\ninput and output. If \"manual\", users will see a button to flag.\n\n\n    \n    \n    flagging_options: list[str] | tuple[str, ...] | None\n\ndefault `= ('Like', 'Dislike')`\n\na list of strings representing the options that users can choose from when\nflagging a message. Defaults to [\"Like\", \"Dislike\"]. These two case-sensitive\nstrings will render as \"thumbs up\" and \"thumbs down\" icon resp", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "gs representing the options that users can choose from when\nflagging a message. Defaults to [\"Like\", \"Dislike\"]. These two case-sensitive\nstrings will render as \"thumbs up\" and \"thumbs down\" icon respectively next to\neach bot message, but any other strings appear under a separate flag icon.\n\n\n    \n    \n    flagging_dir: str\n\ndefault `= \".gradio/flagged\"`\n\npath to the the directory where flagged data is stored. If the directory does\nnot exist, it will be created.\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n    \n    \n    css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a code string. The custom js should be in the form of a single js\nfunction. This function will automatically be executed when the page loads.\nFor more flexibility, use the head parameter to insert js inside <script>\ntags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html code to insert into the head of the demo webpage. This can be used\nto add custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n\n    \n    \n    head_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` parameter is also set, the html from `head` will\nbe included first.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= True`\n\nif True, autofo", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "e`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= True`\n\nif True, autofocuses to the textbox when the page loads.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the chatbot when a new\nmessage appears, unless the user scrolls up. If False, will not scroll to the\nbottom of the chatbot automatically.\n\n\n    \n    \n    submit_btn: str | bool | None\n\ndefault `= True`\n\nIf True, will show a submit button with a submit icon within the textbox. If a\nstring, will use that string as the submit button text in place of the icon.\nIf False, will not show a submit button.\n\n\n    \n    \n    stop_btn: str | bool | None\n\ndefault `= True`\n\nIf True, will show a button with a stop icon during generator executions, to\nstop generating. If a string, will use that string as the submit button text\nin place of the stop icon. If False, will not show a stop button.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nif set, this is the maximum number of chatbot submissions that can be running\nsimultaneously. Can be set to None to mean no limit (any number of chatbot\nsubmissions can be running simultaneously). Set to \"default\" to use the\ndefault concurrency limit (defined by the `default_concurrency_limit`\nparameter in `.queue()`, which is 1 by default).\n\n\n    \n    \n    delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\na tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n    \n    \n    show_progress: Literal['ful", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"minimal\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    fill_height: bool\n\ndefault `= True`\n\nif True, the chat interface will expand to the height of window.\n\n\n    \n    \n    fill_width: bool\n\ndefault `= False`\n\nWhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width.\n\n\n    \n    \n    api_name: str | Literal[False]\n\ndefault `= \"chat\"`\n\ndefines how the chat endpoint appears in the API docs. Can be a string or\nFalse. If set to a string, the chat endpoint will be exposed in the API docs\nwith the given name. If False, the chat endpoint will not be exposed in the\nAPI docs and downstream apps (including those that `gr.load` this app) will\nnot be able to call this chat endpoint.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show the chat endpoint in the \"view API\" page of the Gradio app, or\nin the \".view_api()\" method of the Gradio clients. Unlike setting api_name to\nFalse, setting show_api to False will still allow downstream apps as well as\nthe Clients to use this event. If fn is None, show_api will automatically be\nset to False.\n\n\n    \n    \n    save_history: bool", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "lse, setting show_api to False will still allow downstream apps as well as\nthe Clients to use this event. If fn is None, show_api will automatically be\nset to False.\n\n\n    \n    \n    save_history: bool\n\ndefault `= False`\n\nif True, will save the chat history to the browser's local storage and display\nprevious conversations in a side panel.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "chatinterface_random_responsechatinterface_streaming_echochatinterface_artifacts\n\nOpen in \ud83c\udfa2 \u2197 import random import gradio as gr def random_response(message,\nhistory): return random.choice([\"Yes\", \"No\"]) demo =\ngr.ChatInterface(random_response, type=\"messages\", autofocus=False) if\n__name__ == \"__main__\": demo.launch()\n\n    \n    \n    import random\n    import gradio as gr\n    \n    def random_response(message, history):\n        return random.choice([\"Yes\", \"No\"])\n    \n    demo = gr.ChatInterface(random_response, type=\"messages\", autofocus=False)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import time import gradio as gr def slow_echo(message, history):\nfor i in range(len(message)): time.sleep(0.05) yield \"You typed: \" + message[:\ni + 1] demo = gr.ChatInterface( slow_echo, type=\"messages\",\nflagging_mode=\"manual\", flagging_options=[\"Like\", \"Spam\", \"Inappropriate\",\n\"Other\"], save_history=True, ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import time\n    import gradio as gr\n    \n    def slow_echo(message, history):\n        for i in range(len(message)):\n            time.sleep(0.05)\n            yield \"You typed: \" + message[: i + 1]\n    \n    demo = gr.ChatInterface(\n        slow_echo,\n        type=\"messages\",\n        flagging_mode=\"manual\",\n        flagging_options=[\"Like\", \"Spam\", \"Inappropriate\", \"Other\"],\n        save_history=True,\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr python_code = \"\"\" def fib(n): if n <= 0:\nreturn 0 elif n == 1: return 1 else: return fib(n-1) + fib(n-2) \"\"\" js_code =\n\"\"\" function fib(n) { if (n <= 0) return 0; if (n === 1) return 1; return\nfib(n - 1) + fib(n - 2); } \"\"\" def chat(message, history): if \"python\" in\nmessage.lower(): return \"Type Python or JavaScript to see the code.\",\ngr.Code(language=\"python\", value=python_code) elif \"javascript\" in\nmessage.lower(): return \"Type Python or JavaScript to see the code.\",\ngr.Code(language=\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "Type Python or JavaScript to see the code.\",\ngr.Code(language=\"python\", value=python_code) elif \"javascript\" in\nmessage.lower(): return \"Type Python or JavaScript to see the code.\",\ngr.Code(language=\"javascript\", value=js_code) else: return \"Please ask about\nPython or JavaScript.\", None with gr.Blocks() as demo: code =\ngr.Code(render=False) with gr.Row(): with gr.Column():\ngr.Markdown(\"<center><h1>Write Python or JavaScript</h1></center>\")\ngr.ChatInterface( chat, examples=[\"Python\", \"JavaScript\"],\nadditional_outputs=[code], type=\"messages\" ) with gr.Column():\ngr.Markdown(\"<center><h1>Code Artifacts</h1></center>\") code.render()\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    python_code = \"\"\"\n    def fib(n):\n        if n <= 0:\n            return 0\n        elif n == 1:\n            return 1\n        else:\n            return fib(n-1) + fib(n-2)\n    \"\"\"\n    \n    js_code = \"\"\"\n    function fib(n) {\n        if (n <= 0) return 0;\n        if (n === 1) return 1;\n        return fib(n - 1) + fib(n - 2);\n    }\n    \"\"\"\n    \n    def chat(message, history):\n        if \"python\" in message.lower():\n            return \"Type Python or JavaScript to see the code.\", gr.Code(language=\"python\", value=python_code)\n        elif \"javascript\" in message.lower():\n            return \"Type Python or JavaScript to see the code.\", gr.Code(language=\"javascript\", value=js_code)\n        else:\n            return \"Please ask about Python or JavaScript.\", None\n    \n    with gr.Blocks() as demo:\n        code = gr.Code(render=False)\n        with gr.Row():\n            with gr.Column():\n                gr.Markdown(\"\n    \n    Write Python or JavaScript\n    \n    \")\n                gr.ChatInterface(\n                    chat,\n                    examples=[\"Python\", \"JavaScript\"],\n                    additional_outputs=[code],\n                    type=\"messages\"\n                )\n            with gr.Column():\n                gr.Markdown(\"\n    \n    Code Artifacts\n    \n    \")\n                code.rend", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "tional_outputs=[code],\n                    type=\"messages\"\n                )\n            with gr.Column():\n                gr.Markdown(\"\n    \n    Code Artifacts\n    \n    \")\n                code.render()\n    \n    demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "This component displays a table of value spreadsheet-like component. Can be\nused to display data as an output component, or as an input to collect data\nfrom the user.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "**As input component** : Passes the uploaded spreadsheet data as a\n`pandas.DataFrame`, `numpy.array`, `polars.DataFrame`, or native 2D Python\n`list[list]` depending on `type`\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: pd.DataFrame | np.ndarray | pl.DataFrame | list[list]\n    )\n    \t...\n\n  \n\n**As output component** : Expects data in any of these formats:\n`pandas.DataFrame`, `pandas.Styler`, `numpy.array`, `polars.DataFrame`,\n`list[list]`, `list`, or a `dict` with keys 'data' (and optionally 'headers'),\nor `str` path to a csv, which is rendered as the spreadsheet.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | Callable | None\n\ndefault `= None`\n\nDefault value to display in the DataFrame. Supports pandas, numpy, polars, and\nlist of lists. If a Styler is provided, it will be used to set the displayed\nvalue in the DataFrame (e.g. to set precision of numbers) if the `interactive`\nis False. If a Callable function is provided, the function will be called\nwhenever the app loads to set the initial value of the component.\n\n\n    \n    \n    headers: list[str] | None\n\ndefault `= None`\n\nList of str header names. These are used to set the column headers of the\ndataframe if the value does not have headers. If None, no headers are shown.\n\n\n    \n    \n    row_count: int | tuple[int, str]\n\ndefault `= (1, 'dynamic')`\n\nLimit number of rows for input and decide whether user can create new rows or\ndelete existing rows. The first element of the tuple is an `int`, the row\ncount; the second should be 'fixed' or 'dynamic', the new row behaviour. If an\n`int` is passed the rows default to 'dynamic'\n\n\n    \n    \n    col_count: int | tuple[int, str] | None\n\ndefault `= None`\n\nLimit number of columns for input and decide whether user can create new\ncolumns or delete existing columns. The first element of the tuple is an\n`int`, the number of columns; the second should be 'fixed' or 'dynamic', the\nnew column behaviour. If an `int` is passed the columns default to 'dynamic'\n\n\n    \n    \n    datatype: Literal['str', 'number', 'bool', 'date', 'markdown', 'html', 'image', 'auto'] | list[Literal['str', 'number', 'bool', 'date', 'markdown', 'html']]\n\ndefault `= \"str\"`\n\nDatatype of values in sheet. Can be provided per column as a list of strings,\nor for the entire sheet as a single string. Valid datatypes are \"str\",\n\"number\", \"bool\", \"date\", and \"markdown\". Boolean columns will display as\ncheckboxes. If the datatype \"auto\" is used, the column datatypes are\nautomatically selected based on the value ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "pes are \"str\",\n\"number\", \"bool\", \"date\", and \"markdown\". Boolean columns will display as\ncheckboxes. If the datatype \"auto\" is used, the column datatypes are\nautomatically selected based on the value input if possible.\n\n\n    \n    \n    type: Literal['pandas', 'numpy', 'array', 'polars']\n\ndefault `= \"pandas\"`\n\nType of value to be returned by component. \"pandas\" for pandas dataframe,\n\"numpy\" for numpy array, \"polars\" for polars dataframe, or \"array\" for a\nPython list of lists.\n\n\n    \n    \n    latex_delimiters: list[dict[str, str | bool]] | None\n\ndefault `= None`\n\nA list of dicts of the form {\"left\": open delimiter (str), \"right\": close\ndelimiter (str), \"display\": whether to display in newline (bool)} that will be\nused to render LaTeX expressions. If not provided, `latex_delimiters` is set\nto `[{ \"left\": \"$$\", \"right\": \"$$\", \"display\": True }]`, so only expressions\nenclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass\nin an empty list to disable LaTeX rendering. For more information, see the\n[KaTeX documentation](https://katex.org/docs/autorender.html). Only applies to\ncolumns whose datatype is \"markdown\".\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "l for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    max_height: int | str\n\ndefault `= 500`\n\nThe maximum height of the dataframe, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If more rows are created than\ncan fit in the height, a scrollbar will appear.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to edit the dataframe; if False, can only be used to\ndisplay data. If not provided, this is inferred based on whether the component\nis used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign eve", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "e used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    wrap: bool\n\ndefault `= False`\n\nIf True, the text in table cells will wrap when appropriate. If False and the\n`column_width` parameter is not set, the column widths will expand based on\nthe cell contents and the table may need to be horizontally scrolled. If\n`column_width` is set, then any overflow text will be hidden.\n\n\n    \n    \n    line_breaks: bool\n\ndefault `= True`\n\nIf True (default), will enable Github-flavored Markdown line breaks in chatbot\nmessages. If False, single new lines will be ignored. Only applies for columns\nof type \"markdown.\"\n\n\n    \n    \n    column_widths: list[str | int] | None\n\ndefault `= None`\n\nAn optional list representing the width of each column. The elements of the\nlist should be in the format \"100px\" (ints are also accepted and converted to\npixel values) or \"10%\". The percentage width is calculated based on the\nviewport width of the table. If not provided, the column widths will be\nautomatically determined based on the content of the cells.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "port width of the table. If not provided, the column widths will be\nautomatically determined based on the content of the cells.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True, will show a button to view the values in the table in fullscreen\nmode.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, will show a button to copy the table data to the clipboard.\n\n\n    \n    \n    show_row_numbers: bool\n\ndefault `= False`\n\nIf True, will display row numbers in a separate column.\n\n\n    \n    \n    max_chars: int | None\n\ndefault `= None`\n\nMaximum number of characters to display in each cell before truncating\n(single-clicking a cell value will still reveal the full content). If None, no\ntruncation is applied.\n\n\n    \n    \n    show_search: Literal['none', 'search', 'filter']\n\ndefault `= \"none\"`\n\nShow a search input in the toolbar. If \"search\", a search input is shown. If\n\"filter\", a search input and filter buttons are shown. If \"none\", no search\ninput is shown.\n\n\n    \n    \n    pinned_columns: int | None\n\ndefault `= None`\n\nIf provided, will pin the specified number of columns from the left.\n\n\n    \n    \n    static_columns: list[int] | None\n\ndefault `= None`\n\nList of column indices (int) that should not be editable. Only applies when\ninteractive=True. When specified, col_count is automatically set to \"fixed\"\nand columns cannot be inserted or deleted.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Dataframe` | \"dataframe\" | Uses default values  \n`gradio.Numpy` | \"numpy\" | Uses type=\"numpy\"  \n`gradio.Matrix` | \"matrix\" | Uses type=\"array\"  \n`gradio.List` | \"list\" | Uses type=\"array\", col_count=1  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "filter_recordsmatrix_transposetax_calculatorsort_records\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def filter_records(records, gender): return\nrecords[records[\"gender\"] == gender] demo = gr.Interface( filter_records, [\ngr.Dataframe( headers=[\"name\", \"age\", \"gender\"], datatype=[\"str\", \"number\",\n\"str\"], row_count=5, col_count=(3, \"fixed\"), ), gr.Dropdown([\"M\", \"F\", \"O\"]),\n], \"dataframe\", description=\"Enter gender as 'M', 'F', or 'O' for other.\", )\nif __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def filter_records(records, gender):\n        return records[records[\"gender\"] == gender]\n    \n    demo = gr.Interface(\n        filter_records,\n        [\n            gr.Dataframe(\n                headers=[\"name\", \"age\", \"gender\"],\n                datatype=[\"str\", \"number\", \"str\"],\n                row_count=5,\n                col_count=(3, \"fixed\"),\n            ),\n            gr.Dropdown([\"M\", \"F\", \"O\"]),\n        ],\n        \"dataframe\",\n        description=\"Enter gender as 'M', 'F', or 'O' for other.\",\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr def transpose(matrix):\nreturn matrix.T demo = gr.Interface( transpose, gr.Dataframe(type=\"numpy\",\ndatatype=\"number\", row_count=5, col_count=3, show_fullscreen_button=True),\n\"numpy\", examples=[ [np.zeros((30, 30)).tolist()], [np.ones((2, 2)).tolist()],\n[np.random.randint(0, 10, (3, 10)).tolist()], [np.random.randint(0, 10, (10,\n3)).tolist()], [np.random.randint(0, 10, (10, 10)).tolist()], ],\ncache_examples=False ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    \n    import gradio as gr\n    \n    def transpose(matrix):\n        return matrix.T\n    \n    demo = gr.Interface(\n        transpose,\n        gr.Dataframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3, show_fullscreen_button=True),\n        \"numpy\",\n        examples=[\n            [np.zeros((30, 30)).tolist()],\n            [np.ones((2, ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "aframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3, show_fullscreen_button=True),\n        \"numpy\",\n        examples=[\n            [np.zeros((30, 30)).tolist()],\n            [np.ones((2, 2)).tolist()],\n            [np.random.randint(0, 10, (3, 10)).tolist()],\n            [np.random.randint(0, 10, (10, 3)).tolist()],\n            [np.random.randint(0, 10, (10, 10)).tolist()],\n        ],\n        cache_examples=False\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def tax_calculator(income, marital_status,\nassets): tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\ntotal_deductible = sum(assets[\"Cost\"]) taxable_income = income -\ntotal_deductible total_tax = 0 for bracket, rate in tax_brackets: if\ntaxable_income > bracket: total_tax += (taxable_income - bracket) * rate / 100\nif marital_status == \"Married\": total_tax *= 0.75 elif marital_status ==\n\"Divorced\": total_tax *= 0.8 return round(total_tax) demo = gr.Interface(\ntax_calculator, [ \"number\", gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\ngr.Dataframe( headers=[\"Item\", \"Cost\"], datatype=[\"str\", \"number\"],\nlabel=\"Assets Purchased this Year\", ), ], \"number\", examples=[ [10000,\n\"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]], [80000,\n\"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]], ], ) demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def tax_calculator(income, marital_status, assets):\n        tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\n        total_deductible = sum(assets[\"Cost\"])\n        taxable_income = income - total_deductible\n    \n        total_tax = 0\n        for bracket, rate in tax_brackets:\n            if taxable_income > bracket:\n                total_tax += (taxable_income - bracket) * rate / 100\n    \n        if marital_status == \"Married\":\n            total_tax *= 0.75\n        elif marital_status == \"Divorced\":\n            total_tax *= 0.8\n    \n        return round(to", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": " - bracket) * rate / 100\n    \n        if marital_status == \"Married\":\n            total_tax *= 0.75\n        elif marital_status == \"Divorced\":\n            total_tax *= 0.8\n    \n        return round(total_tax)\n    \n    demo = gr.Interface(\n        tax_calculator,\n        [\n            \"number\",\n            gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\n            gr.Dataframe(\n                headers=[\"Item\", \"Cost\"],\n                datatype=[\"str\", \"number\"],\n                label=\"Assets Purchased this Year\",\n            ),\n        ],\n        \"number\",\n        examples=[\n            [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n            [80000, \"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]],\n        ],\n    )\n    \n    demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sort_records(records): return\nrecords.sort(\"Quantity\") demo = gr.Interface( sort_records, gr.Dataframe(\nheaders=[\"Item\", \"Quantity\"], datatype=[\"str\", \"number\"], row_count=3,\ncol_count=(2, \"fixed\"), type=\"polars\" ), \"dataframe\", description=\"Sort by\nQuantity\" ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sort_records(records):\n        return records.sort(\"Quantity\")\n    \n    demo = gr.Interface(\n        sort_records,\n        gr.Dataframe(\n            headers=[\"Item\", \"Quantity\"],\n            datatype=[\"str\", \"number\"],\n            row_count=3,\n            col_count=(2, \"fixed\"),\n            type=\"polars\"\n        ),\n        \"dataframe\",\n        description=\"Sort by Quantity\"\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Dataframe component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Dataframe.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Dataframe changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Dataframe.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Dataframe.  \n`Dataframe.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Dataframe. Uses event data gradio.SelectData to carry `value` referring to the label of the Dataframe, and `selected` to refer to state of the Dataframe. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockC", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": ".components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if th", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "r_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies a", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "ool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "This class allows you to pass custom error messages to the user. You can do\nso by raising a gr.Error(\"custom message\") anywhere in the code, and when that\nline is executed the custom message will appear in a modal on the demo.\n\nYou can control for how long the error message is displayed with the\n`duration` parameter. If it\u2019s `None`, the message will be displayed forever\nuntil the user closes it. If it\u2019s a number, it will be shown for that many\nseconds.\n\nYou can also hide the error modal from being shown in the UI by setting\n`visible=False`.\n\nBelow is a demo of how different values of duration control the error,\ninfo, and warning messages. You can see the code\n[here](https://huggingface.co/spaces/freddyaboulton/gradio-error-\nduration/blob/244331cf53f6b5fa2fd406ece3bf55c6ccb9f5f2/app.pyL17).\n\n![modal_control](https://github.com/gradio-\napp/gradio/assets/41651716/f0977bcd-eaec-4eca-a2fd-ede95fdb8fd2)\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "import gradio as gr\n    def divide(numerator, denominator):\n        if denominator == 0:\n            raise gr.Error(\"Cannot divide by zero!\")\n    gr.Interface(divide, [\"number\", \"number\"], \"number\").launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    message: str\n\ndefault `= \"Error raised.\"`\n\nThe error message to be displayed to the user. Can be HTML, which will be\nrendered in the modal.\n\n\n    \n    \n    duration: float | None\n\ndefault `= 10`\n\nThe duration in seconds to display the error message. If None or 0, the error\nmessage will be displayed until the user closes it.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the error message should be displayed in the UI.\n\n\n    \n    \n    title: str\n\ndefault `= \"Error\"`\n\nThe title to be displayed to the user at the top of the error modal.\n\n\n    \n    \n    print_exception: bool\n\ndefault `= True`\n\nWhether to print traceback of the error to the console when the error is\nraised.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "calculatorblocks_chained_events\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def calculator(num1, operation, num2): if\noperation == \"add\": return num1 + num2 elif operation == \"subtract\": return\nnum1 - num2 elif operation == \"multiply\": return num1 * num2 elif operation ==\n\"divide\": if num2 == 0: raise gr.Error(\"Cannot divide by zero!\") return num1 /\nnum2 demo = gr.Interface( calculator, [ \"number\", gr.Radio([\"add\", \"subtract\",\n\"multiply\", \"divide\"]), \"number\" ], \"number\", examples=[ [45, \"add\", 3],\n[3.14, \"divide\", 2], [144, \"multiply\", 2.5], [0, \"subtract\", 1.2], ],\ntitle=\"Toy Calculator\", description=\"Here's a sample toy calculator.\", ) if\n__name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def calculator(num1, operation, num2):\n        if operation == \"add\":\n            return num1 + num2\n        elif operation == \"subtract\":\n            return num1 - num2\n        elif operation == \"multiply\":\n            return num1 * num2\n        elif operation == \"divide\":\n            if num2 == 0:\n                raise gr.Error(\"Cannot divide by zero!\")\n            return num1 / num2\n    \n    demo = gr.Interface(\n        calculator,\n        [\n            \"number\",\n            gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n            \"number\"\n        ],\n        \"number\",\n        examples=[\n            [45, \"add\", 3],\n            [3.14, \"divide\", 2],\n            [144, \"multiply\", 2.5],\n            [0, \"subtract\", 1.2],\n        ],\n        title=\"Toy Calculator\",\n        description=\"Here's a sample toy calculator.\",\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def failure(): raise gr.Error(\"This should\nfail!\") def exception(): raise ValueError(\"Something went wrong\") def\nsuccess(): return True def warning_fn(): gr.Warning(\"This is a warning!\") def\ninfo_fn(): gr.Info(\"This is some info\") with gr.Blocks() as demo:\ngr.Markdown(\"Used in E2E tests of success event trigger. The then event\ncovered", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "def warning_fn(): gr.Warning(\"This is a warning!\") def\ninfo_fn(): gr.Info(\"This is some info\") with gr.Blocks() as demo:\ngr.Markdown(\"Used in E2E tests of success event trigger. The then event\ncovered in chatbot E2E tests.\" \" Also testing that the status modals show\nup.\") with gr.Row(): result = gr.Textbox(label=\"Result\") result_2 =\ngr.Textbox(label=\"Consecutive Event\") with gr.Row(): success_btn =\ngr.Button(value=\"Trigger Success\") success_btn_2 = gr.Button(value=\"Trigger\nConsecutive Success\") failure_btn = gr.Button(value=\"Trigger Failure\")\nfailure_exception = gr.Button(value=\"Trigger Failure With ValueError\") with\ngr.Row(): trigger_warning = gr.Button(value=\"Trigger Warning\") trigger_info =\ngr.Button(value=\"Trigger Info\") success_btn_2.click(success, None,\nNone).success(lambda: \"First Event Trigered\", None, result).success(lambda:\n\"Consecutive Event Triggered\", None, result_2) success_btn.click(success,\nNone, None).success(lambda: \"Success event triggered\", inputs=None,\noutputs=result) failure_btn.click(failure, None, None).success(lambda: \"Should\nnot be triggered\", inputs=None, outputs=result)\nfailure_exception.click(exception, None, None)\ntrigger_warning.click(warning_fn, None, None) trigger_info.click(info_fn,\nNone, None) if __name__ == \"__main__\": demo.launch(show_error=True)\n\n    \n    \n    import gradio as gr\n    \n    def failure():\n        raise gr.Error(\"This should fail!\")\n    \n    def exception():\n        raise ValueError(\"Something went wrong\")\n    \n    def success():\n        return True\n    \n    def warning_fn():\n        gr.Warning(\"This is a warning!\")\n    \n    def info_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n                    \" Also testing that the status modals show up.\")\n        with gr.Row():\n            result = gr.Textbox(label=\"Result\")\n            result_2 = gr.Textbox(label=\"Consecutive Event\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "\n                    \" Also testing that the status modals show up.\")\n        with gr.Row():\n            result = gr.Textbox(label=\"Result\")\n            result_2 = gr.Textbox(label=\"Consecutive Event\")\n        with gr.Row():\n            success_btn = gr.Button(value=\"Trigger Success\")\n            success_btn_2 = gr.Button(value=\"Trigger Consecutive Success\")\n            failure_btn = gr.Button(value=\"Trigger Failure\")\n            failure_exception = gr.Button(value=\"Trigger Failure With ValueError\")\n        with gr.Row():\n            trigger_warning = gr.Button(value=\"Trigger Warning\")\n            trigger_info = gr.Button(value=\"Trigger Info\")\n    \n            success_btn_2.click(success, None, None).success(lambda: \"First Event Trigered\", None, result).success(lambda: \"Consecutive Event Triggered\", None, result_2)\n            success_btn.click(success, None, None).success(lambda: \"Success event triggered\", inputs=None, outputs=result)\n            failure_btn.click(failure, None, None).success(lambda: \"Should not be triggered\", inputs=None, outputs=result)\n            failure_exception.click(exception, None, None)\n            trigger_warning.click(warning_fn, None, None)\n            trigger_info.click(info_fn, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch(show_error=True)\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "Creates a button, that when clicked, allows a user to download a single\nfile of arbitrary type.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "**As input component** : (Rarely used) passes the file as a `str` into the\nfunction.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` or `pathlib.Path` filepath\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str\n\ndefault `= \"Download\"`\n\nText to display on the button. Defaults to \"Download\".\n\n\n    \n    \n    value: str | Path | Callable | None\n\ndefault `= None`\n\nA str or pathlib.Path filepath or URL to download, or a Callable that returns\na str or pathlib.Path filepath or URL to download.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop']\n\ndefault `= \"secondary\"`\n\n'primary' for main call-to-action, 'secondary' for a more subdued style,\n'stop' for a stop button.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    int", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "ot sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nIf False, the UploadButton will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.DownloadButton` | \"downloadbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "upload_and_download\n\nOpen in \ud83c\udfa2 \u2197 from pathlib import Path import gradio as gr def\nupload_file(filepath): name = Path(filepath).name return\n[gr.UploadButton(visible=False), gr.DownloadButton(label=f\"Download {name}\",\nvalue=filepath, visible=True)] def download_file(): return\n[gr.UploadButton(visible=True), gr.DownloadButton(visible=False)] with\ngr.Blocks() as demo: gr.Markdown(\"First upload a file and and then you'll be\nable download it (but only once!)\") with gr.Row(): u = gr.UploadButton(\"Upload\na file\", file_count=\"single\") d = gr.DownloadButton(\"Download the file\",\nvisible=False) u.upload(upload_file, u, [u, d]) d.click(download_file, None,\n[u, d]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    from pathlib import Path\n    import gradio as gr\n    \n    def upload_file(filepath):\n        name = Path(filepath).name\n        return [gr.UploadButton(visible=False), gr.DownloadButton(label=f\"Download {name}\", value=filepath, visible=True)]\n    \n    def download_file():\n        return [gr.UploadButton(visible=True), gr.DownloadButton(visible=False)]\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"First upload a file and and then you'll be able download it (but only once!)\")\n        with gr.Row():\n            u = gr.UploadButton(\"Upload a file\", file_count=\"single\")\n            d = gr.DownloadButton(\"Download the file\", visible=False)\n    \n        u.upload(upload_file, u, [u, d])\n        d.click(download_file, None, [u, d])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe DownloadButton component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`DownloadButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the DownloadButton is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n   ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "e used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "s then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to Non", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "nents.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "Special component that ticks at regular intervals when active. It is not\nvisible, and only used to trigger events at a regular interval through the\n`tick` event listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "**As input component** : The interval of the timer as a float.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: float | None\n    )\n    \t...\n\n  \n\n**As output component** : The interval of the timer as a float or None.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: float\n\ndefault `= 1`\n\nInterval in seconds between each tick.\n\n\n    \n    \n    active: bool\n\ndefault `= True`\n\nWhether the timer is active.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Timer` | \"timer\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Timer component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Timer.tick(fn, \u00b7\u00b7\u00b7)` | This listener is triggered at regular intervals defined by the Timer.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n   ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "ill be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"hidden\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "ction is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "t components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "Creates a color picker for user to select a color as string input. Can be\nused as an input to pass a color value to a function or as an output to\ndisplay a color value.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "**As input component** : Passes selected color value as a hex `str` into\nthe function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a hex `str` returned from function and\nsets color picker value to it.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Callable | None\n\ndefault `= None`\n\ndefault color hex code to provide in color picker. If a function is provided,\nthe function will be called each time the app loads to set the initial value\nof this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a cer", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable color picker; if False, editing will\nbe disabled. If not provided, this is inferred based on whether the component\nis used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ColorPicker` | \"colorpicker\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "color_picker\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np from PIL import Image,\nImageColor def change_color(icon, color): \"\"\" Function that given an icon in\n.png format changes its color Args: icon: Icon whose color needs to be\nchanged. color: Chosen color with which to edit the input icon. Returns:\nedited_image: Edited icon. \"\"\" img = icon.convert(\"LA\") img =\nimg.convert(\"RGBA\") image_np = np.array(icon) _, _, _, alpha = image_np.T mask\n= alpha > 0 image_np[..., :-1][mask.T] = ImageColor.getcolor(color, \"RGB\")\nedited_image = Image.fromarray(image_np) return edited_image inputs = [\ngr.Image(label=\"icon\", type=\"pil\", image_mode=\"RGBA\"),\ngr.ColorPicker(label=\"color\"), ] outputs = gr.Image(label=\"colored icon\") demo\n= gr.Interface( fn=change_color, inputs=inputs, outputs=outputs ) if __name__\n== \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    from PIL import Image, ImageColor\n    \n    def change_color(icon, color):\n    \n        \"\"\"\n        Function that given an icon in .png format changes its color\n        Args:\n            icon: Icon whose color needs to be changed.\n            color: Chosen color with which to edit the input icon.\n        Returns:\n            edited_image: Edited icon.\n        \"\"\"\n        img = icon.convert(\"LA\")\n        img = img.convert(\"RGBA\")\n        image_np = np.array(icon)\n        _, _, _, alpha = image_np.T\n        mask = alpha > 0\n        image_np[..., :-1][mask.T] = ImageColor.getcolor(color, \"RGB\")\n        edited_image = Image.fromarray(image_np)\n        return edited_image\n    \n    inputs = [\n        gr.Image(label=\"icon\", type=\"pil\", image_mode=\"RGBA\"),\n        gr.ColorPicker(label=\"color\"),\n    ]\n    outputs = gr.Image(label=\"colored icon\")\n    \n    demo = gr.Interface(\n        fn=change_color,\n        inputs=inputs,\n        outputs=outputs\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ColorPicker component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ColorPicker.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the ColorPicker changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`ColorPicker.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the ColorPicker.  \n`ColorPicker.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the ColorPicker is focused.  \n`ColorPicker.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the ColorPicker is focused.  \n`ColorPicker.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the ColorPicker is unfocused/blurred.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | Bloc", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will pla", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "ent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions whi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": " to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "Creates a button that can be assigned arbitrary .click() events. The value\n(label) of the button can be used as an input to the function (rarely used) or\nset via the output of a function.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "**As input component** : (Rarely used) the `str` corresponding to the\nbutton label when the button is clicked\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : string corresponding to the button label\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | I18nData | Callable\n\ndefault `= \"Run\"`\n\ndefault text for the button to display. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop', 'huggingface']\n\ndefault `= \"secondary\"`\n\nsets the background and text color of the button. Use 'primary' for main call-\nto-action buttons, 'secondary' for a more subdued style, 'stop' for a stop\nbutton, 'huggingface' for a black background with white text, consistent with\nHugging Face's button styles.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | Path | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    link: str | None\n\ndefault `= None`\n\nURL to open when the button is clicked. If None, no link will be used.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nif False, the Button will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": " is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Button` | \"button\" | Uses default values  \n`gradio.ClearButton` | \"clearbutton\" | Uses default values  \n`gradio.DeepLinkButton` | \"deeplinkbutton\" | Uses default values  \n`gradio.DuplicateButton` | \"duplicatebutton\" | Uses default values  \n`gradio.LoginButton` | \"loginbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Button component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Button.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Button is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "nt. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number o", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "urn a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "oncurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "Creates a button that redirects the user to Sign with Hugging Face using\nOAuth. If created inside of a Blocks context, it will add an event to check if\nthe user is logged in and update the button text accordingly. If created\noutside of a Blocks context, call the `LoginButton.activate()` method to add\nthe event.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "**As input component** : (Rarely used) the `str` corresponding to the\nbutton label when the button is clicked\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : string corresponding to the button label\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str\n\ndefault `= \"Sign in with Hugging Face\"`\n\n\n    \n    \n    logout_value: str\n\ndefault `= \"Logout ({})\"`\n\nThe text to display when the user is signed in. The string should contain a\nplaceholder for the username with a call-to-action to logout, e.g. \"Logout\n({})\".\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop', 'huggingface']\n\ndefault `= \"huggingface\"`\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\n\n    \n    \n    icon: str | Path | None\n\ndefault `= \"/home/runner/work/gradio/gradio/gradio/icons/huggingface-\nlogo.svg\"`\n\n\n    \n    \n    link: str | None\n\ndefault `= None`\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.LoginButton` | \"loginbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe LoginButton component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`LoginButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Button is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_descripti", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximu", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "ed* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no c", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "Creates an image component that can be used to upload images (as an input)\nor display images (as an output).\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "**As input component** : A `str` containing the path to the image.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` or `pathlib.Path` object\ncontaining the path to the image.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | None\n\ndefault `= None`\n\nA path or URL for the default value that SimpleImage component is going to\ntake. If a function is provided, the function will be called each time the app\nloads to set the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_download_button: bool\n\ndefault `= True`\n\nIf True, will display button to download image.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "ll_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload and edit an image; if False, can only be\nused to display images. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": " user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.SimpleImage` | \"simpleimage\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe SimpleImage component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`SimpleImage.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the SimpleImage using the clear button for the component.  \n`SimpleImage.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the SimpleImage changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`SimpleImage.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the SimpleImage.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "ponent | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "rue, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second subm", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "bmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "n @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "Creates an image component that can be used to upload images (as an input)\nor display images (as an output).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "**As input component** : Passes the uploaded image as a tuple of\n`numpy.array`, `PIL.Image` or `str` filepath depending on `type`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: tuple[\n        str | PIL.Image.Image | np.ndarray | None, str | PIL.Image.Image | np.ndarray | None\n    ]\n    )\n    \t...\n\n  \n\n**As output component** : Expects a tuple of `numpy.array`, `PIL.Image`, or\n`str` or `pathlib.Path` filepath to an image which is displayed.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> np.ndarray | PIL.Image.Image | str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: image_tuple | Callable | None\n\ndefault `= None`\n\nA tuple of PIL Image, numpy array, path or URL for the default value that\nImageSlider component is going to take, this pair of images should be of equal\nsize. If a function is provided, the function will be called each time the app\nloads to set the initial value of this component.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFile format (e.g. \"png\" or \"gif\"). Used to save image if it does not already\nhave a valid format (e.g. if the image is being returned to the frontend as a\nnumpy array or PIL Image). The format should be supported by the PIL library.\nApplies both when this component is used as an input or output. This parameter\nhas no effect on SVG files.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed tuple\nof image file or numpy array, but will affect the displayed image.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed tuple\nof image file or numpy array, but will affect the displayed image.\n\n\n    \n    \n    image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None\n\ndefault `= \"RGB\"`\n\nThe pixel format and color depth that the image should be loaded and\npreprocessed as. \"RGB\" will load the image as a color image, or \"L\" as black-\nand-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html\nfor other supported image modes and their meaning. This parameter has no\neffect on SVG or GIF files. If set to None, the image_mode will be inferred\nfrom the image file types (e.g. \"RGBA\" for a .png image, \"RGB\" in most other\ncases).\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe for", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "image_mode will be inferred\nfrom the image file types (e.g. \"RGBA\" for a .png image, \"RGB\" in most other\ncases).\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe format the images are converted to before being passed into the prediction\nfunction. \"numpy\" converts the images to numpy arrays with shape (height,\nwidth, 3) and values from 0 to 255, \"pil\" converts the images to PIL image\nobjects, \"filepath\" passes str paths to temporary files containing the images.\nTo support animated GIFs in input, the `type` should be set to \"filepath\" or\n\"pil\". To support SVGs, the `type` should be set to \"filepath\".\n\n\n    \n    \n    label: str | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_download_button: bool\n\ndefault `= True`\n\nIf True, will display button to download image. Only applies if interactive is\nFalse (e.g. if the component is used as an output).\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Comp", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload and edit an image; if False, can only be\nused to display images. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parame", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "e component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a fullscreen icon in the corner of the component that\nallows user to view the image in fullscreen mode. If False, icon does not\nappear.\n\n\n    \n    \n    slider_position: float\n\ndefault `= 50`\n\nThe position of the slider as a percentage of the width of the image, between\n0 and 100.\n\n\n    \n    \n    max_height: int\n\ndefault `= 500`\n\nThe maximum height of the image.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ImageSlider` | \"imageslider\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "imageslider\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr from PIL import ImageFilter def\nimg_to_slider(im): if not im: return im return (im,\nim.filter(filter=ImageFilter.GaussianBlur(radius=10))) def slider_to_self(im):\nif not im or not im[0]: return im return (im[0],\nim[0].filter(filter=ImageFilter.GaussianBlur(radius=10))) def\nslider_to_self_two(im): return im def position_to_slider(pos): return\ngr.ImageSlider(slider_position=pos) with gr.Blocks() as demo: gr.Markdown(\"\nimg to image slider\") with gr.Row(): img1 = gr.Image(label=\"Blur image\",\ntype=\"pil\") img2 = gr.ImageSlider(label=\"Blur image\", type=\"pil\") btn =\ngr.Button(\"Blur image\") btn.click(img_to_slider, inputs=img1, outputs=img2)\ngr.Markdown(\"unified image slider\") with gr.Row(): img3 =\ngr.ImageSlider(label=\"Blur image\", type=\"pil\") img3.upload(slider_to_self,\ninputs=img3, outputs=img3) pos = gr.Slider(label=\"Position\", value=50,\nminimum=0, maximum=100, step=0.01) pos.change(position_to_slider, inputs=pos,\noutputs=img3, show_progress=\"hidden\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    from PIL import ImageFilter\n    \n    \n    def img_to_slider(im):\n        if not im:\n            return im\n        return (im, im.filter(filter=ImageFilter.GaussianBlur(radius=10)))\n    \n    \n    def slider_to_self(im):\n        if not im or not im[0]:\n            return im\n        return (im[0], im[0].filter(filter=ImageFilter.GaussianBlur(radius=10)))\n    \n    \n    def slider_to_self_two(im):\n        return im\n    \n    \n    def position_to_slider(pos):\n        return gr.ImageSlider(slider_position=pos)\n    \n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"img to image slider\")\n        with gr.Row():\n            img1 = gr.Image(label=\"Blur image\", type=\"pil\")\n            img2 = gr.ImageSlider(label=\"Blur image\", type=\"pil\")\n        btn = gr.Button(\"Blur image\")\n        btn.click(img_to_slider, inputs=img1, outputs=img2)\n        gr.Markdown(\"unified image slider\")\n        with gr.Row()", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "r(label=\"Blur image\", type=\"pil\")\n        btn = gr.Button(\"Blur image\")\n        btn.click(img_to_slider, inputs=img1, outputs=img2)\n        gr.Markdown(\"unified image slider\")\n        with gr.Row():\n            img3 = gr.ImageSlider(label=\"Blur image\", type=\"pil\")\n            img3.upload(slider_to_self, inputs=img3, outputs=img3)\n        pos = gr.Slider(label=\"Position\", value=50, minimum=0, maximum=100, step=0.01)\n        pos.change(position_to_slider, inputs=pos, outputs=img3, show_progress=\"hidden\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ImageSlider component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ImageSlider.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the ImageSlider using the clear button for the component.  \n`ImageSlider.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the ImageSlider changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`ImageSlider.stream(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user streams the ImageSlider.  \n`ImageSlider.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the ImageSlider. Uses event data gradio.SelectData to carry `value` referring to the label of the ImageSlider, and `selected` to refer to state of the ImageSlider. See EventData documentation on how to use this event data  \n`ImageSlider.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the ImageSlider.  \n`ImageSlider.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the ImageSlider.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one out", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "ss animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functio", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "ts to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automati", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "thod of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "Creates a gallery or table to display data samples. This component is\nprimarily designed for internal use to display examples. However, it can also\nbe used directly to display a dataset and let users select examples.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "**As input component** : Passes the selected sample either as a `list` of\ndata corresponding to each input component (if `type` is \"value\") or as an\n`int` index (if `type` is \"index\"), or as a `tuple` of the index and the data\n(if `type` is \"tuple\").\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: int | list | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects an `int` index or `list` of sample data.\nReturns the index of the sample in the dataset or `None` if the sample is not\nfound.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> list[list]\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, appears above the component.\n\n\n    \n    \n    show_label: bool\n\ndefault `= True`\n\nIf True, the label will be shown above the component.\n\n\n    \n    \n    components: list[Component] | list[str] | None\n\ndefault `= None`\n\nWhich component types to show in this dataset widget, can be passed in as a\nlist of string names or Components instances. The following components are\nsupported in a Dataset: Audio, Checkbox, CheckboxGroup, ColorPicker,\nDataframe, Dropdown, File, HTML, Image, Markdown, Model3D, Number, Radio,\nSlider, Textbox, TimeSeries, Video\n\n\n    \n    \n    component_props: list[dict[str, Any]] | None\n\ndefault `= None`\n\n\n    \n    \n    samples: list[list[Any]] | None\n\ndefault `= None`\n\na nested list of samples. Each sublist within the outer list represents a data\nsample, and each element within the sublist represents an value for each\ncomponent\n\n\n    \n    \n    headers: list[str] | None\n\ndefault `= None`\n\nColumn headers in the Dataset widget, should be the same len as components. If\nnot provided, inferred from component labels\n\n\n    \n    \n    type: Literal['values', 'index', 'tuple']\n\ndefault `= \"values\"`\n\n\"values\" if clicking on a sample should pass the value of the sample, \"index\"\nif it should pass the index of the sample, or \"tuple\" if it should pass both\nthe index and the value of the sample.\n\n\n    \n    \n    layout: Literal['gallery', 'table'] | None\n\ndefault `= None`\n\n\"gallery\" if the dataset should be displayed as a gallery with each sample in\na clickable card, or \"table\" if it should be displayed as a table with each\nsample in a row. By default, \"gallery\" is used if there is a single component,\nand \"table\" is used if there are more than one component. If there are more\nthan one component, the layout can only be \"table\".\n\n\n    \n    \n    samples_per_page: int\n\ndefault `= 10`\n\nhow many examples to show per page.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "re more\nthan one component, the layout can only be \"table\".\n\n\n    \n    \n    samples_per_page: int\n\ndefault `= 10`\n\nhow many examples to show per page.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n  ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": " in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    proxy_url: str | None\n\ndefault `= None`\n\nThe URL of the external Space used to load this component. Set automatically\nwhen using `gr.load()`. This should not be set manually.\n\n\n    \n    \n    sample_labels: list[str] | None\n\ndefault `= None`\n\nA list of labels for each sample. If provided, the length of this list should\nbe the same as the number of samples, and these labels will be used in the UI\ninstead of rendering the sample values.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Dataset` | \"dataset\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "**Updating a Dataset**\n\nIn this example, we display a text dataset using `gr.Dataset` and then update\nit when the user clicks a button:\n\n    \n    \n    import gradio as gr\n    \n    philosophy_quotes = [\n        [\"I think therefore I am.\"],\n        [\"The unexamined life is not worth living.\"]\n    ]\n    \n    startup_quotes = [\n        [\"Ideas are easy. Implementation is hard\"],\n        [\"Make mistakes faster.\"]\n    ]\n    \n    def show_startup_quotes():\n        return gr.Dataset(samples=startup_quotes)\n    \n    with gr.Blocks() as demo:\n        textbox = gr.Textbox()\n        dataset = gr.Dataset(components=[textbox], samples=philosophy_quotes)\n        button = gr.Button()\n    \n        button.click(show_startup_quotes, None, dataset)\n    \n    demo.launch()\n\n", "heading1": "Examples", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Dataset component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Dataset.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Dataset is clicked.  \n`Dataset.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Dataset. Uses event data gradio.SelectData to carry `value` referring to the label of the Dataset, and `selected` to refer to state of the Dataset. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API doc", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of '", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "r the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "Creates a dropdown of choices from which a single entry or multiple entries\ncan be selected (as an input component) or displayed (as an output component).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "**As input component** : Passes the value of the selected dropdown choice as a `str | int | float` or its index as an `int` into the function, depending on `type`. Or, if `multiselect` is True, passes the values of the selected dropdown choices as a list of corresponding values/indices instead.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | int | float | list[str | int | float] | list[int | None] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str | int | float` corresponding to the value of the dropdown entry to be selected. Or, if `multiselect` is True, expects a `list` of values corresponding to the selected dropdown entries.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | int | float | list[str | int | float] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    choices: list[str | int | float | tuple[str, str | int | float]] | None\n\ndefault `= None`\n\na list of string or numeric options to choose from. An option can also be a\ntuple of the form (name, value), where name is the displayed name of the\ndropdown choice and value is the value to be passed to the function, or\nreturned by the function.\n\n\n    \n    \n    value: str | int | float | list[str | int | float] | Callable | DefaultValue | None\n\ndefault `= DefaultValue()`\n\nthe value selected in dropdown. If `multiselect` is true, this should be list,\notherwise a single string or number from among `choices`. By default, the\nfirst choice in `choices` is initally selected. If set explicitly to None, no\nvalue is initally selected. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    type: Literal['value', 'index']\n\ndefault `= \"value\"`\n\ntype of value to be returned by component. \"value\" returns the string of the\nchoice selected, \"index\" returns the index of the choice selected.\n\n\n    \n    \n    multiselect: bool | None\n\ndefault `= None`\n\nif True, multiple choices can be selected.\n\n\n    \n    \n    allow_custom_value: bool\n\ndefault `= False`\n\nif True, allows user to enter a custom value that is not in the list of\nchoices.\n\n\n    \n    \n    max_choices: int | None\n\ndefault `= None`\n\nmaximum number of choices that can be selected. If None, no limit is enforced.\n\n\n    \n    \n    filterable: bool\n\ndefault `= True`\n\nif True, user will be able to type into the dropdown and filter the choices by\ntyping. Can only be set to False if `allow_custom_value` is False.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this compon", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "`show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, choices in this dropdown will be selectable; if False, selection will\nbe disabled. If not provided, this is inferred based on whether the component\nis used as an input or output.\n\n\n    \n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "ne`\n\nif True, choices in this dropdown will be selectable; if False, selection will\nbe disabled. If not provided, this is inferred based on whether the component\nis used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not be rendered in the Blocks context. Should be used\nif the intention is to assign event listeners now but render the component\nlater.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Dropdown` | \"dropdown\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "sentence_builder\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "  gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Dropdown component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Dropdown.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Dropdown changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Dropdown.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Dropdown.  \n`Dropdown.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Dropdown. Uses event data gradio.SelectData to carry `value` referring to the label of the Dropdown, and `selected` to refer to state of the Dropdown. See EventData documentation on how to use this event data  \n`Dropdown.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Dropdown is focused.  \n`Dropdown.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Dropdown is unfocused/blurred.  \n`Dropdown.key_up(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses a key while the Dropdown is focused.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Compone", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "nd the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Com", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "utput component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled,", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "ick_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": " to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "Tab (or its alias TabItem) is a layout element. Components defined within\nthe Tab will be visible when this tab is selected tab.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "with gr.Blocks() as demo:\n        with gr.Tab(\"Lion\"):\n            gr.Image(\"lion.jpg\")\n            gr.Button(\"New Lion\")\n        with gr.Tab(\"Tiger\"):\n            gr.Image(\"tiger.jpg\")\n            gr.Button(\"New Tiger\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe visual label for the tab\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, Tab will be hidden.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nIf False, Tab will not be clickable.\n\n\n    \n    \n    id: int | str | None\n\ndefault `= None`\n\nAn optional identifier for the tab, required if you wish to control the\nselected tab from a predict function.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of the <div> containing the\ncontents of the Tab layout. The same string followed by \"-button\" is attached\nto the Tab button. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent elements. 1 or greater indicates the Tab\nwill expand in size.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Tab.select(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nEvent listener for when the user selects or deselects the Tab. Uses event data\ngradio.SelectData to carry `value` referring to the label of the Tab, and\n`selected` to refer to state of the Tab. See EventData documentation on how to\nuse this event data\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ni", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "eral['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner w", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "put component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nse", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "g 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "\n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "Row is a layout element within Blocks that renders all children\nhorizontally.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/row", "source_page_title": "Gradio - Row Docs"}, {"text": "with gr.Blocks() as demo:\n        with gr.Row():\n            gr.Image(\"lion.jpg\", scale=2)\n            gr.Image(\"tiger.jpg\", scale=1)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/row", "source_page_title": "Gradio - Row Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    variant: Literal['default', 'panel', 'compact']\n\ndefault `= \"default\"`\n\nrow type, 'default' (no background), 'panel' (gray background color and\nrounded corners), or 'compact' (rounded corners and no internal gap).\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, row will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative height compared to adjacent elements. 1 or greater indicates the Row\nwill expand in height, and any child columns will also expand to fill the\nheight.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the row, specified in pixels if a number is passed, or in CSS\nunits if a string is passed. If content exceeds the height, the row will\nscroll vertically. If not set, the row will expand to fit the content.\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= None`\n\nThe maximum height of the row, specified in pixels if a number is passed, or\nin CSS units if a string is passed. If content exceeds the height, the row\nwill scroll vertically. If content is shorter than the height, the row will\nshrink to fit the content. Will not have any effect if `height` is set and is\nsmaller than `max_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the row, specified in pixels if a number is passed, or\nin CSS units if a string is passed. If content e", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/row", "source_page_title": "Gradio - Row Docs"}, {"text": "x_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the row, specified in pixels if a number is passed, or\nin CSS units if a string is passed. If content exceeds the height, the row\nwill expand to fit the content. Will not have any effect if `height` is set\nand is larger than `min_height`.\n\n\n    \n    \n    equal_height: bool\n\ndefault `= False`\n\nIf True, makes every child element have equal height\n\n\n    \n    \n    show_progress: bool\n\ndefault `= False`\n\nIf True, shows progress animation when being updated.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/row", "source_page_title": "Gradio - Row Docs"}, {"text": "Creates a bar plot component to display data from a pandas DataFrame.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "**As input component** : The data to display in a line plot.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: AltairPlotData\n    )\n    \t...\n\n  \n\n**As output component** : Expects a pandas DataFrame containing the data to\ndisplay in the line plot. The DataFrame should contain at least two columns,\none for the x-axis (corresponding to this component's `x` argument) and one\nfor the y-axis (corresponding to `y`).\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> pd.DataFrame | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: pd.DataFrame | Callable | None\n\ndefault `= None`\n\nThe pandas dataframe containing the data to display in the plot.\n\n\n    \n    \n    x: str | None\n\ndefault `= None`\n\nColumn corresponding to the x axis. Column can be numeric, datetime, or\nstring/category.\n\n\n    \n    \n    y: str | None\n\ndefault `= None`\n\nColumn corresponding to the y axis. Column must be numeric.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nColumn corresponding to series, visualized by color. Column must be\nstring/category.\n\n\n    \n    \n    title: str | None\n\ndefault `= None`\n\nThe title to display on top of the chart.\n\n\n    \n    \n    x_title: str | None\n\ndefault `= None`\n\nThe title given to the x axis. By default, uses the value of the x parameter.\n\n\n    \n    \n    y_title: str | None\n\ndefault `= None`\n\nThe title given to the y axis. By default, uses the value of the y parameter.\n\n\n    \n    \n    color_title: str | None\n\ndefault `= None`\n\nThe title given to the color legend. By default, uses the value of color\nparameter.\n\n\n    \n    \n    x_bin: str | float | None\n\ndefault `= None`\n\nGrouping used to cluster x values. If x column is numeric, should be number to\nbin the x values. If x column is datetime, should be string such as \"1h\",\n\"15m\", \"10s\", using \"s\", \"m\", \"h\", \"d\" suffixes.\n\n\n    \n    \n    y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None\n\ndefault `= None`\n\nAggregation function used to aggregate y values, used if x_bin is provided or\nx is a string/category. Must be one of \"sum\", \"mean\", \"median\", \"min\", \"max\".\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nMapping of series to color names or codes. For example, {\"success\": \"green\",\n\"fail\": \"FF8888\"}.\n\n\n    \n    \n    x_lim: list[float] | None\n\ndefault `= None`\n\nA tuple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "ple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA tuple of list containing the limits for the y-axis, specified as [y_min,\ny_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum to value.\n\n\n    \n    \n    x_label_angle: float\n\ndefault `= 0`\n\nThe angle of the x-axis labels in degrees offset clockwise.\n\n\n    \n    \n    y_label_angle: float\n\ndefault `= 0`\n\nThe angle of the y-axis labels in degrees offset clockwise.\n\n\n    \n    \n    x_axis_labels_visible: bool\n\ndefault `= True`\n\nWhether the x-axis labels should be visible. Can be hidden when many x-axis\nlabels are present.\n\n\n    \n    \n    caption: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) caption to display below the plot.\n\n\n    \n    \n    sort: Literal['x', 'y', '-x', '-y'] | list[str] | None\n\ndefault `= None`\n\nThe sorting order of the x values, if x column is type string/category. Can be\n\"x\", \"y\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n    \n    \n    tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hovering on a point. \"axis\" shows the values for\nthe axis columns, \"all\" shows all column values, and \"none\" shows no tooltips.\nCan also provide a list of strings representing columns to show in the\ntooltip, which will be displayed along with axis values.\n\n\n    \n    \n    height: int | None\n\ndefault `= None`\n\nThe height of the plot in pixels.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) label to display on the top left corner of the plot.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nWhether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Whether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the plot should be visible.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n  ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "default `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True, will show a button to make plot visible in fullscreen mode.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.BarPlot` | \"barplot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "bar_plot_demo\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd from random import randint, random import\ngradio as gr temp_sensor_data = pd.DataFrame( { \"time\":\npd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200), \"temperature\":\n[randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n\"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in\nrange(200)], \"location\": [\"indoor\", \"outdoor\"] * 100, } ) food_rating_data =\npd.DataFrame( { \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in\nrange(100)], \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n\"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)], \"wait\":\n[random() for i in range(100)], } ) with gr.Blocks() as bar_plots: with\ngr.Row(): start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\") end =\ngr.DateTime(\"2021-01-05 00:00:00\", label=\"End\") apply_btn = gr.Button(\"Apply\",\nscale=0) with gr.Row(): group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"],\nvalue=\"None\", label=\"Group by\") aggregate = gr.Radio([\"sum\", \"mean\", \"median\",\n\"min\", \"max\"], value=\"sum\", label=\"Aggregation\") temp_by_time = gr.BarPlot(\ntemp_sensor_data, x=\"time\", y=\"temperature\", ) temp_by_time_location =\ngr.BarPlot( temp_sensor_data, x=\"time\", y=\"temperature\", color=\"location\", )\ntime_graphs = [temp_by_time, temp_by_time_location] group_by.change( lambda\ngroup: [gr.BarPlot(x_bin=None if group == \"None\" else group)] *\nlen(time_graphs), group_by, time_graphs ) aggregate.change( lambda aggregate:\n[gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs), aggregate, time_graphs\n) def rescale(select: gr.SelectData): return select.index rescale_evt =\ngr.on([plot.select for plot in time_graphs], rescale, None, [start, end]) for\ntrigger in [apply_btn.click, rescale_evt.then]: trigger( lambda start, end:\n[gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n) with gr.Row(): price_by_cuisine = gr.BarPlot( food_rating_data, x=\"cuisine\",\ny=\"price\", ) with gr.Column(scale=0): gr.Button(\"Sort $", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n) with gr.Row(): price_by_cuisine = gr.BarPlot( food_rating_data, x=\"cuisine\",\ny=\"price\", ) with gr.Column(scale=0): gr.Button(\"Sort $ > $$$\").click(lambda:\ngr.BarPlot(sort=\"y\"), None, price_by_cuisine) gr.Button(\"Sort $$$ >\n$\").click(lambda: gr.BarPlot(sort=\"-y\"), None, price_by_cuisine)\ngr.Button(\"Sort A > Z\").click(lambda: gr.BarPlot(sort=[\"Chinese\", \"Italian\",\n\"Mexican\"]), None, price_by_cuisine) with gr.Row(): price_by_rating =\ngr.BarPlot( food_rating_data, x=\"rating\", y=\"price\", x_bin=1, )\nprice_by_rating_color = gr.BarPlot( food_rating_data, x=\"rating\", y=\"price\",\ncolor=\"cuisine\", x_bin=1, color_map={\"Italian\": \"red\", \"Mexican\": \"green\",\n\"Chinese\": \"blue\"}, ) if __name__ == \"__main__\": bar_plots.launch()\n\n    \n    \n    import pandas as pd\n    from random import randint, random\n    import gradio as gr\n    \n    \n    temp_sensor_data = pd.DataFrame(\n        {\n            \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n            \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"location\": [\"indoor\", \"outdoor\"] * 100,\n        }\n    )\n    \n    food_rating_data = pd.DataFrame(\n        {\n            \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n            \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n            \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n            \"wait\": [random() for i in range(100)],\n        }\n    )\n    \n    with gr.Blocks() as bar_plots:\n        with gr.Row():\n            start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n            end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n            apply_btn = gr.Button(\"Apply\", scale=0)\n        with gr.Row():\n            group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Gro", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "021-01-05 00:00:00\", label=\"End\")\n            apply_btn = gr.Button(\"Apply\", scale=0)\n        with gr.Row():\n            group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n            aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n    \n        temp_by_time = gr.BarPlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n        )\n        temp_by_time_location = gr.BarPlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n            color=\"location\",\n        )\n    \n        time_graphs = [temp_by_time, temp_by_time_location]\n        group_by.change(\n            lambda group: [gr.BarPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n            group_by,\n            time_graphs\n        )\n        aggregate.change(\n            lambda aggregate: [gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs),\n            aggregate,\n            time_graphs\n        )\n    \n        def rescale(select: gr.SelectData):\n            return select.index\n        rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])\n    \n        for trigger in [apply_btn.click, rescale_evt.then]:\n            trigger(\n                lambda start, end: [gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n            )\n    \n        with gr.Row():\n            price_by_cuisine = gr.BarPlot(\n                food_rating_data,\n                x=\"cuisine\",\n                y=\"price\",\n            )\n            with gr.Column(scale=0):\n                gr.Button(\"Sort $ > $$$\").click(lambda: gr.BarPlot(sort=\"y\"), None, price_by_cuisine)\n                gr.Button(\"Sort $$$ > $\").click(lambda: gr.BarPlot(sort=\"-y\"), None, price_by_cuisine)\n                gr.Button(\"Sort A > Z\").click(lambda: gr.BarPlot(sort=[\"Chinese\", \"Italian\", \"Mexican\"]), None, price_by_cuisine)\n    \n        with gr.Row():\n      ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "sort=\"-y\"), None, price_by_cuisine)\n                gr.Button(\"Sort A > Z\").click(lambda: gr.BarPlot(sort=[\"Chinese\", \"Italian\", \"Mexican\"]), None, price_by_cuisine)\n    \n        with gr.Row():\n            price_by_rating = gr.BarPlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                x_bin=1,\n            )\n            price_by_rating_color = gr.BarPlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                color=\"cuisine\",\n                x_bin=1,\n                color_map={\"Italian\": \"red\", \"Mexican\": \"green\", \"Chinese\": \"blue\"},\n            )\n    \n    if __name__ == \"__main__\":\n        bar_plots.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe BarPlot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`BarPlot.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the NativePlot. Uses event data gradio.SelectData to carry `value` referring to the label of the NativePlot, and `selected` to refer to state of the NativePlot. See EventData documentation on how to use this event data  \n`BarPlot.double_click(fn, \u00b7\u00b7\u00b7)` | Triggered when the NativePlot is double clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": " list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each pa", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "w a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "The gr.CopyData class is a subclass of gr.EventData that specifically\ncarries information about the `.copy()` event. When gr.CopyData is added as a\ntype hint to an argument of an event listener method, a gr.CopyData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/copydata", "source_page_title": "Gradio - Copydata Docs"}, {"text": "import gradio as gr\n    def on_copy(copy_data: gr.CopyData):\n        return f\"Copied text: {copy_data.value}\"\n    with gr.Blocks() as demo:\n        textbox = gr.Textbox(\"Hello World!\")\n        copied = gr.Textbox()\n        textbox.copy(on_copy, None, copied)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/copydata", "source_page_title": "Gradio - Copydata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: Any\n\nThe value that was copied.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/copydata", "source_page_title": "Gradio - Copydata Docs"}, {"text": "The gr.DownloadData class is a subclass of gr.EventData that specifically\ncarries information about the `.download()` event. When gr.DownloadData is\nadded as a type hint to an argument of an event listener method, a\ngr.DownloadData object will automatically be passed as the value of that\nargument. The attributes of this object contains information about the event\nthat triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/downloaddata", "source_page_title": "Gradio - Downloaddata Docs"}, {"text": "import gradio as gr\n    def on_download(download_data: gr.DownloadData):\n        return f\"Downloaded file: {download_data.file.path}\"\n    with gr.Blocks() as demo:\n        files = gr.File()\n        textbox = gr.Textbox()\n        files.download(on_download, None, textbox)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/downloaddata", "source_page_title": "Gradio - Downloaddata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    file: FileData\n\nThe file that was downloaded, as a FileData object.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/downloaddata", "source_page_title": "Gradio - Downloaddata Docs"}, {"text": "Creates a line plot component to display data from a pandas DataFrame.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "**As input component** : The data to display in a line plot.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: AltairPlotData | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a pandas DataFrame containing the data to\ndisplay in the line plot. The DataFrame should contain at least two columns,\none for the x-axis (corresponding to this component's `x` argument) and one\nfor the y-axis (corresponding to `y`).\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> pd.DataFrame | dict | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: pd.DataFrame | Callable | None\n\ndefault `= None`\n\nThe pandas dataframe containing the data to display in the plot.\n\n\n    \n    \n    x: str | None\n\ndefault `= None`\n\nColumn corresponding to the x axis. Column can be numeric, datetime, or\nstring/category.\n\n\n    \n    \n    y: str | None\n\ndefault `= None`\n\nColumn corresponding to the y axis. Column must be numeric.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nColumn corresponding to series, visualized by color. Column must be\nstring/category.\n\n\n    \n    \n    title: str | None\n\ndefault `= None`\n\nThe title to display on top of the chart.\n\n\n    \n    \n    x_title: str | None\n\ndefault `= None`\n\nThe title given to the x axis. By default, uses the value of the x parameter.\n\n\n    \n    \n    y_title: str | None\n\ndefault `= None`\n\nThe title given to the y axis. By default, uses the value of the y parameter.\n\n\n    \n    \n    color_title: str | None\n\ndefault `= None`\n\nThe title given to the color legend. By default, uses the value of color\nparameter.\n\n\n    \n    \n    x_bin: str | float | None\n\ndefault `= None`\n\nGrouping used to cluster x values. If x column is numeric, should be number to\nbin the x values. If x column is datetime, should be string such as \"1h\",\n\"15m\", \"10s\", using \"s\", \"m\", \"h\", \"d\" suffixes.\n\n\n    \n    \n    y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None\n\ndefault `= None`\n\nAggregation function used to aggregate y values, used if x_bin is provided or\nx is a string/category. Must be one of \"sum\", \"mean\", \"median\", \"min\", \"max\".\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nMapping of series to color names or codes. For example, {\"success\": \"green\",\n\"fail\": \"FF8888\"}.\n\n\n    \n    \n    x_lim: list[float] | None\n\ndefault `= None`\n\nA tuple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA tuple of list containing the limits for the y-axis, specified as [y_min,\ny_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum to value.\n\n\n    \n    \n    x_label_angle: float\n\ndefault `= 0`\n\nThe angle of the x-axis labels in degrees offset clockwise.\n\n\n    \n    \n    y_label_angle: float\n\ndefault `= 0`\n\nThe angle of the y-axis labels in degrees offset clockwise.\n\n\n    \n    \n    x_axis_labels_visible: bool\n\ndefault `= True`\n\nWhether the x-axis labels should be visible. Can be hidden when many x-axis\nlabels are present.\n\n\n    \n    \n    caption: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) caption to display below the plot.\n\n\n    \n    \n    sort: Literal['x', 'y', '-x', '-y'] | list[str] | None\n\ndefault `= None`\n\nThe sorting order of the x values, if x column is type string/category. Can be\n\"x\", \"y\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n    \n    \n    tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hovering on a point. \"axis\" shows the values for\nthe axis columns, \"all\" shows all column values, and \"none\" shows no tooltips.\nCan also provide a list of strings representing columns to show in the\ntooltip, which will be displayed along with axis values.\n\n\n    \n    \n    height: int | None\n\ndefault `= None`\n\nThe height of the plot in pixels.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) label to display on the top left corner of the plot.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nWhether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Whether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the plot should be visible.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n  ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "default `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True, will show a button to make plot visible in fullscreen mode.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.LinePlot` | \"lineplot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "line_plot_demo\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd from random import randint, random import\ngradio as gr temp_sensor_data = pd.DataFrame( { \"time\":\npd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200), \"temperature\":\n[randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n\"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in\nrange(200)], \"location\": [\"indoor\", \"outdoor\"] * 100, } ) food_rating_data =\npd.DataFrame( { \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in\nrange(100)], \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n\"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)], \"wait\":\n[random() for i in range(100)], } ) with gr.Blocks() as line_plots: with\ngr.Row(): start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\") end =\ngr.DateTime(\"2021-01-05 00:00:00\", label=\"End\") apply_btn = gr.Button(\"Apply\",\nscale=0) with gr.Row(): group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"],\nvalue=\"None\", label=\"Group by\") aggregate = gr.Radio([\"sum\", \"mean\", \"median\",\n\"min\", \"max\"], value=\"sum\", label=\"Aggregation\") temp_by_time = gr.LinePlot(\ntemp_sensor_data, x=\"time\", y=\"temperature\", ) temp_by_time_location =\ngr.LinePlot( temp_sensor_data, x=\"time\", y=\"temperature\", color=\"location\", )\ntime_graphs = [temp_by_time, temp_by_time_location] group_by.change( lambda\ngroup: [gr.LinePlot(x_bin=None if group == \"None\" else group)] *\nlen(time_graphs), group_by, time_graphs ) aggregate.change( lambda aggregate:\n[gr.LinePlot(y_aggregate=aggregate)] * len(time_graphs), aggregate,\ntime_graphs ) def rescale(select: gr.SelectData): return select.index\nrescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None,\n[start, end]) for trigger in [apply_btn.click, rescale_evt.then]: trigger(\nlambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs),\n[start, end], time_graphs ) price_by_cuisine = gr.LinePlot( food_rating_data,\nx=\"cuisine\", y=\"price\", ) with gr.Row(): price_by_rating = gr.LinePlot(\nfoo", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ePlot(x_lim=[start, end])] * len(time_graphs),\n[start, end], time_graphs ) price_by_cuisine = gr.LinePlot( food_rating_data,\nx=\"cuisine\", y=\"price\", ) with gr.Row(): price_by_rating = gr.LinePlot(\nfood_rating_data, x=\"rating\", y=\"price\", ) price_by_rating_color =\ngr.LinePlot( food_rating_data, x=\"rating\", y=\"price\", color=\"cuisine\",\ncolor_map={\"Italian\": \"red\", \"Mexican\": \"green\", \"Chinese\": \"blue\"}, ) if\n__name__ == \"__main__\": line_plots.launch()\n\n    \n    \n    import pandas as pd\n    from random import randint, random\n    import gradio as gr\n    \n    \n    temp_sensor_data = pd.DataFrame(\n        {\n            \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n            \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"location\": [\"indoor\", \"outdoor\"] * 100,\n        }\n    )\n    \n    food_rating_data = pd.DataFrame(\n        {\n            \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n            \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n            \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n            \"wait\": [random() for i in range(100)],\n        }\n    )\n    \n    with gr.Blocks() as line_plots:\n        with gr.Row():\n            start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n            end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n            apply_btn = gr.Button(\"Apply\", scale=0)\n        with gr.Row():\n            group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n            aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n    \n        temp_by_time = gr.LinePlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n        )\n        temp_by_time_location = gr.LinePlot(\n            temp_sensor_data,\n          ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "_by_time = gr.LinePlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n        )\n        temp_by_time_location = gr.LinePlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n            color=\"location\",\n        )\n    \n        time_graphs = [temp_by_time, temp_by_time_location]\n        group_by.change(\n            lambda group: [gr.LinePlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n            group_by,\n            time_graphs\n        )\n        aggregate.change(\n            lambda aggregate: [gr.LinePlot(y_aggregate=aggregate)] * len(time_graphs),\n            aggregate,\n            time_graphs\n        )\n    \n        def rescale(select: gr.SelectData):\n            return select.index\n        rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])\n    \n        for trigger in [apply_btn.click, rescale_evt.then]:\n            trigger(\n                lambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n            )\n    \n        price_by_cuisine = gr.LinePlot(\n            food_rating_data,\n            x=\"cuisine\",\n            y=\"price\",\n        )\n        with gr.Row():\n            price_by_rating = gr.LinePlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n            )\n            price_by_rating_color = gr.LinePlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                color=\"cuisine\",\n                color_map={\"Italian\": \"red\", \"Mexican\": \"green\", \"Chinese\": \"blue\"},\n            )\n    \n    if __name__ == \"__main__\":\n        line_plots.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe LinePlot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`LinePlot.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the NativePlot. Uses event data gradio.SelectData to carry `value` referring to the label of the NativePlot, and `selected` to refer to state of the NativePlot. See EventData documentation on how to use this event data  \n`LinePlot.double_click(fn, \u00b7\u00b7\u00b7)` | Triggered when the NativePlot is double clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint w", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "pty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "llow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "A Gradio request object that can be used to access the request headers,\ncookies, query parameters and other information about the request from within\nthe prediction function. The class is a thin wrapper around the\nfastapi.Request class. Attributes of this class include: `headers`, `client`,\n`query_params`, `session_hash`, and `path_params`. If auth is enabled, the\n`username` attribute can be used to get the logged in user. In some\nenvironments, the dict-like attributes (e.g. `requests.headers`,\n`requests.query_params`) of this class are automatically converted to\ndictionaries, so we recommend converting them to dictionaries before accessing\nattributes for consistent behavior in different environments.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/request", "source_page_title": "Gradio - Request Docs"}, {"text": "import gradio as gr\n    def echo(text, request: gr.Request):\n        if request:\n            print(\"Request headers dictionary:\", request.headers)\n            print(\"IP address:\", request.client.host)\n            print(\"Query parameters:\", dict(request.query_params))\n            print(\"Session hash:\", request.session_hash)\n        return text\n    io = gr.Interface(echo, \"textbox\", \"textbox\").launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/request", "source_page_title": "Gradio - Request Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    request: fastapi.Request | None\n\ndefault `= None`\n\nA fastapi.Request\n\n\n    \n    \n    username: str | None\n\ndefault `= None`\n\nThe username of the logged in user (if auth is enabled)\n\n\n    \n    \n    session_hash: str | None\n\ndefault `= None`\n\nThe session hash of the current session. It is unique for each page load.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/request", "source_page_title": "Gradio - Request Docs"}, {"text": "request_ip_headers\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def predict(text, request: gr.Request):\nheaders = request.headers host = request.client.host user_agent =\nrequest.headers[\"user-agent\"] return { \"ip\": host, \"user_agent\": user_agent,\n\"headers\": headers, } gr.Interface(predict, \"text\", \"json\").launch()\n\n    \n    \n    import gradio as gr\n    \n    def predict(text, request: gr.Request):\n        headers = request.headers\n        host = request.client.host\n        user_agent = request.headers[\"user-agent\"]\n        return {\n            \"ip\": host,\n            \"user_agent\": user_agent,\n            \"headers\": headers,\n        }\n    \n    gr.Interface(predict, \"text\", \"json\").launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/request", "source_page_title": "Gradio - Request Docs"}, {"text": "Used to display arbitrary JSON output prettily. As this component does not\naccept user input, it is rarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "**As input component** : Passes the JSON value as a `dict` or `list`\ndepending on the value.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: dict | list | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a valid JSON `str` \\-- or a `list` or\n`dict` that can be serialized to a JSON string. The `list` or `dict` value can\ncontain numpy arrays.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> dict | list | str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | dict | list | Callable | None\n\ndefault `= None`\n\nDefault value as a valid JSON `str` -- or a `list` or `dict` that can be\nserialized to a JSON string. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "rap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    open: bool\n\ndefault `= False`\n\nIf True, all JSON nodes will be expanded when rendered. By default, node\nlevels deeper than 3 are collapsed.\n\n\n    \n    \n    show_indices: bool\n\ndefault `= False`\n\nWhether to show numerical indices when displaying the elements of a list\nwithin the JSON object.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nHeight of the JSON component in pixels if a number is passed, or in CSS units\nif ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": " displaying the elements of a list\nwithin the JSON object.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nHeight of the JSON component in pixels if a number is passed, or in CSS units\nif a string is passed. Overflow will be scrollable. If None, the height will\nbe automatically adjusted to fit the content.\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= 500`\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.JSON` | \"json\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "zip_to_jsonblocks_xray\n\nOpen in \ud83c\udfa2 \u2197 from zipfile import ZipFile import gradio as gr def\nzip_to_json(file_obj): files = [] with ZipFile(file_obj.name) as zfile: for\nzinfo in zfile.infolist(): files.append( { \"name\": zinfo.filename,\n\"file_size\": zinfo.file_size, \"compressed_size\": zinfo.compress_size, } )\nreturn files demo = gr.Interface(zip_to_json, \"file\", \"json\") if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    from zipfile import ZipFile\n    \n    import gradio as gr\n    \n    def zip_to_json(file_obj):\n        files = []\n        with ZipFile(file_obj.name) as zfile:\n            for zinfo in zfile.infolist():\n                files.append(\n                    {\n                        \"name\": zinfo.filename,\n                        \"file_size\": zinfo.file_size,\n                        \"compressed_size\": zinfo.compress_size,\n                    }\n                )\n        return files\n    \n    demo = gr.Interface(zip_to_json, \"file\", \"json\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr disease_values = [0.25, 0.5, 0.75] def\nxray_model(diseases, img): return [{disease: disease_values[idx] for\nidx,disease in enumerate(diseases)}] def ct_model(diseases, img): return\n[{disease: 0.1 for disease in diseases}] with gr.Blocks(fill_width=True) as\ndemo: gr.Markdown( \"\"\" Detect Disease From Scan With this model you can\nlorem ipsum \\- ipsum 1 \\- ipsum 2 \"\"\" ) gr.DuplicateButton() disease =\ngr.CheckboxGroup( info=\"Select the diseases you want to scan for.\",\nchoices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\" )\nslider = gr.Slider(0, 100) with gr.Tab(\"X-ray\") as x_tab: with gr.Row():\nxray_scan = gr.Image() xray_results = gr.JSON(show_indices=True) xray_run =\ngr.Button(\"Run\") xray_run.click( xray_model, inputs=[disease, xray_scan],\noutputs=xray_results, api_name=\"xray_model\" ) with gr.Tab(\"CT Scan\"): with\ngr.Row(): ct_scan = gr.Image() ct_results = gr.JSON(show_indices=True) ct_run\n= gr.Button(\"Ru", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "l, inputs=[disease, xray_scan],\noutputs=xray_results, api_name=\"xray_model\" ) with gr.Tab(\"CT Scan\"): with\ngr.Row(): ct_scan = gr.Image() ct_results = gr.JSON(show_indices=True) ct_run\n= gr.Button(\"Run\") ct_run.click( ct_model, inputs=[disease, ct_scan],\noutputs=ct_results, api_name=\"ct_model\" ) upload_btn = gr.Button(\"Upload\nResults\", variant=\"primary\") upload_btn.click( lambda ct, xr: None,\ninputs=[ct_results, xray_results], outputs=[], ) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    disease_values = [0.25, 0.5, 0.75]\n    \n    def xray_model(diseases, img):\n        return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]\n    \n    def ct_model(diseases, img):\n        return [{disease: 0.1 for disease in diseases}]\n    \n    with gr.Blocks(fill_width=True) as demo:\n        gr.Markdown(\n            \"\"\"\n    Detect Disease From Scan\n    With this model you can lorem ipsum\n    - ipsum 1\n    - ipsum 2\n    \"\"\"\n        )\n        gr.DuplicateButton()\n        disease = gr.CheckboxGroup(\n            info=\"Select the diseases you want to scan for.\",\n            choices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\"\n        )\n        slider = gr.Slider(0, 100)\n    \n        with gr.Tab(\"X-ray\") as x_tab:\n            with gr.Row():\n                xray_scan = gr.Image()\n                xray_results = gr.JSON(show_indices=True)\n            xray_run = gr.Button(\"Run\")\n            xray_run.click(\n                xray_model,\n                inputs=[disease, xray_scan],\n                outputs=xray_results,\n                api_name=\"xray_model\"\n            )\n    \n        with gr.Tab(\"CT Scan\"):\n            with gr.Row():\n                ct_scan = gr.Image()\n                ct_results = gr.JSON(show_indices=True)\n            ct_run = gr.Button(\"Run\")\n            ct_run.click(\n                ct_model,\n                inputs=[disease, ct_scan],\n                outputs=ct_results,\n                api_name=\"ct", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "e)\n            ct_run = gr.Button(\"Run\")\n            ct_run.click(\n                ct_model,\n                inputs=[disease, ct_scan],\n                outputs=ct_results,\n                api_name=\"ct_model\"\n            )\n    \n        upload_btn = gr.Button(\"Upload Results\", variant=\"primary\")\n        upload_btn.click(\n            lambda ct, xr: None,\n            inputs=[ct_results, xray_results],\n            outputs=[],\n        )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe JSON component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`JSON.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the JSON changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the fun", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": " how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). Th", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "n the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "teral[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "Used to create an upload button, when clicked allows a user to upload files\nthat satisfy the specified file type or generic files (if file_type not set).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "**As input component** : Passes the file as a `str` or `bytes` object, or a\nlist of `str` or list of `bytes` objects, depending on `type` and\n`file_count`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: bytes | str | list[bytes] | list[str] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` filepath or URL, or a `list[str]`\nof filepaths/URLs.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | list[str] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str\n\ndefault `= \"Upload a File\"`\n\nText to display on the button. Defaults to \"Upload a File\".\n\n\n    \n    \n    value: str | I18nData | list[str] | Callable | None\n\ndefault `= None`\n\nFile or list of files to upload by default.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop']\n\ndefault `= \"secondary\"`\n\n'primary' for main call-to-action, 'secondary' for a more subdued style,\n'stop' for a stop button.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nIf False, the UploadButto", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "ain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nIf False, the UploadButton will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    type: Literal['filepath', 'binary']\n\ndefault `= \"filepath\"`\n\nType of value to be returned by component. \"file\" returns a temporary file\nobject with the same base name as the uploaded file, whose full path can be\nretrieved by file_obj.name, \"binary\" returns an bytes object.\n\n\n    \n    \n    file_count: Literal['single', 'multiple', 'directory']\n\ndefault `= \"single\"`\n\nif single, allows user to upload one file. If \"multiple\", user uploads\nmultiple files. If \"directory\", user ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "    \n    \n    file_count: Literal['single', 'multiple', 'directory']\n\ndefault `= \"single\"`\n\nif single, allows user to upload one file. If \"multiple\", user uploads\nmultiple files. If \"directory\", user uploads all files in selected directory.\nReturn type will be list for each file in case of \"multiple\" or \"directory\".\n\n\n    \n    \n    file_types: list[str] | None\n\ndefault `= None`\n\nList of type of files to be uploaded. \"file\" allows any file to be uploaded,\n\"image\" allows only image files to be uploaded, \"audio\" allows only audio\nfiles to be uploaded, \"video\" allows only video files to be uploaded, \"text\"\nallows only text files to be uploaded.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.UploadButton` | \"uploadbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "upload_and_downloadupload_button\n\nOpen in \ud83c\udfa2 \u2197 from pathlib import Path import gradio as gr def\nupload_file(filepath): name = Path(filepath).name return\n[gr.UploadButton(visible=False), gr.DownloadButton(label=f\"Download {name}\",\nvalue=filepath, visible=True)] def download_file(): return\n[gr.UploadButton(visible=True), gr.DownloadButton(visible=False)] with\ngr.Blocks() as demo: gr.Markdown(\"First upload a file and and then you'll be\nable download it (but only once!)\") with gr.Row(): u = gr.UploadButton(\"Upload\na file\", file_count=\"single\") d = gr.DownloadButton(\"Download the file\",\nvisible=False) u.upload(upload_file, u, [u, d]) d.click(download_file, None,\n[u, d]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    from pathlib import Path\n    import gradio as gr\n    \n    def upload_file(filepath):\n        name = Path(filepath).name\n        return [gr.UploadButton(visible=False), gr.DownloadButton(label=f\"Download {name}\", value=filepath, visible=True)]\n    \n    def download_file():\n        return [gr.UploadButton(visible=True), gr.DownloadButton(visible=False)]\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"First upload a file and and then you'll be able download it (but only once!)\")\n        with gr.Row():\n            u = gr.UploadButton(\"Upload a file\", file_count=\"single\")\n            d = gr.DownloadButton(\"Download the file\", visible=False)\n    \n        u.upload(upload_file, u, [u, d])\n        d.click(download_file, None, [u, d])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def upload_file(files): file_paths =\n[file.name for file in files] return file_paths with gr.Blocks() as demo:\nfile_output = gr.File() upload_button = gr.UploadButton(\"Click to Upload a\nFile\", file_types=[\"image\", \"video\"], file_count=\"multiple\")\nupload_button.upload(upload_file, upload_button, file_output) demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def upload_file(files):\n        file_paths = [file.name fo", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "ile_count=\"multiple\")\nupload_button.upload(upload_file, upload_button, file_output) demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def upload_file(files):\n        file_paths = [file.name for file in files]\n        return file_paths\n    \n    with gr.Blocks() as demo:\n        file_output = gr.File()\n        upload_button = gr.UploadButton(\"Click to Upload a File\", file_types=[\"image\", \"video\"], file_count=\"multiple\")\n        upload_button.upload(upload_file, upload_button, file_output)\n    \n    demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe UploadButton component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`UploadButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the UploadButton is clicked.  \n`UploadButton.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the UploadButton.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand d", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "oint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), wit", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "r each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"defau", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "ments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "Creates a file component that allows uploading one or more generic files\n(when used as an input) or displaying generic files or URLs for download (as\noutput).  \nDemo: zip_files, zip_to_json\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "**As input component** : Passes the file as a `str` or `bytes` object, or a\nlist of `str` or list of `bytes` objects, depending on `type` and\n`file_count`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: bytes | str | list[bytes] | list[str] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` filepath or URL, or a `list[str]`\nof filepaths/URLs.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | list[str] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | list[str] | Callable | None\n\ndefault `= None`\n\nDefault file(s) to display, given as a str file path or URL, or a list of str\nfile paths / URLs. If a function is provided, the function will be called each\ntime the app loads to set the initial value of this component.\n\n\n    \n    \n    file_count: Literal['single', 'multiple', 'directory']\n\ndefault `= \"single\"`\n\nif single, allows user to upload one file. If \"multiple\", user uploads\nmultiple files. If \"directory\", user uploads all files in selected directory.\nReturn type will be list for each file in case of \"multiple\" or \"directory\".\n\n\n    \n    \n    file_types: list[str] | None\n\ndefault `= None`\n\nList of file extensions or types of files to be uploaded (e.g. ['image',\n'.json', '.mp4']). \"file\" allows any file to be uploaded, \"image\" allows only\nimage files to be uploaded, \"audio\" allows only audio files to be uploaded,\n\"video\" allows only video files to be uploaded, \"text\" allows only text files\nto be uploaded.\n\n\n    \n    \n    type: Literal['filepath', 'binary']\n\ndefault `= \"filepath\"`\n\nType of value to be returned by component. \"file\" returns a temporary file\nobject with the same base name as the uploaded file, whose full path can be\nretrieved by file_obj.name, \"binary\" returns an bytes object.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponent", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "r whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    height: int | str | float | None\n\ndefault `= None`\n\nThe default height of the file component when no files have been uploaded, or\nthe maximum height of the file component when files are present. Specified in\npixels if a number is passed, or in CSS units if a string is passed. If more\nfiles are uploaded than can fit in the height, a scrollbar will appear.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload a file; if False, can only be used to\ndisplay files. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": " \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    allow_reordering: bool\n\ndefault `= False`\n\nif True, will allow users to reorder uploaded files by dragging and dropping.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.File` | \"file\" | Uses default values  \n`gradio.Files` | \"files\" | Uses file_count=\"multiple\"  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe File component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`File.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the File changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`File.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the File. Uses event data gradio.SelectData to carry `value` referring to the label of the File, and `selected` to refer to state of the File. See EventData documentation on how to use this event data  \n`File.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the File using the clear button for the component.  \n`File.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the File.  \n`File.delete(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user deletes and item from the File. Uses event data gradio.DeletedFileData to carry `value` referring to the file that was deleted as an instance of FileData. See EventData documentation on how to use this event data  \n`File.download(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user downloads a file from the File. Uses event data gradio.DownloadData to carry information about the downloaded file as a FileData object. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event i", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "ation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minima", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "l be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "\n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"vie", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "Creates a file explorer component that allows users to browse files on the\nmachine hosting the Gradio app. As an input component, it also allows users to\nselect files to be used as input to a function, while as an output component,\nit displays selected files.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "**As input component** : Passes the selected file or directory as a `str`\npath (relative to `root`) or `list[str}` depending on `file_count`\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: list[str] | str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects function to return a `str` path to a\nfile, or `list[str]` consisting of paths to files.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | list[str] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    glob: str\n\ndefault `= \"**/*\"`\n\nThe glob-style pattern used to select which files to display, e.g. \"*\" to\nmatch all files, \"*.png\" to match all .png files, \"**/*.txt\" to match any .txt\nfile in any subdirectory, etc. The default value matches all files and folders\nrecursively. See the Python glob documentation at\nhttps://docs.python.org/3/library/glob.html for more information.\n\n\n    \n    \n    value: str | list[str] | Callable | None\n\ndefault `= None`\n\nThe file (or list of files, depending on the `file_count` parameter) to show\nas \"selected\" when the component is first loaded. If a callable is provided,\nit will be called when the app loads to set the initial value of the\ncomponent. If not provided, no files are shown as selected.\n\n\n    \n    \n    file_count: Literal['single', 'multiple']\n\ndefault `= \"multiple\"`\n\nWhether to allow single or multiple files to be selected. If \"single\", the\ncomponent will return a single absolute file path as a string. If \"multiple\",\nthe component will return a list of absolute file paths as a list of strings.\n\n\n    \n    \n    root_dir: str | Path\n\ndefault `= \".\"`\n\nPath to root directory to select files from. If not provided, defaults to\ncurrent working directory.\n\n\n    \n    \n    ignore_glob: str | None\n\ndefault `= None`\n\nThe glob-style, case-sensitive pattern that will be used to exclude files from\nthe list. For example, \"*.py\" will exclude all .py files from the list. See\nthe Python glob documentation at https://docs.python.org/3/library/glob.html\nfor more information.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "bel will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe maximum height of the file component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If more files are uploaded than\ncan fit in the height, a scrollbar will appear.\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= 500`\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to select file(s); if False, will only display\nfiles. If not provided, this is inferred based on wheth", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "lt `= None`\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to select file(s); if False, will only display\nfiles. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.FileExplorer` | \"fileexplorer\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe FileExplorer component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`FileExplorer.change(fn, \u00b7\u00b7\u00b7)` |   \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\nd", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "nt will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch togeth", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "en if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nth", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "The gr.RetryData class is a subclass of gr.Event data that specifically\ncarries information about the `.retry()` event. When gr.RetryData is added as\na type hint to an argument of an event listener method, a gr.RetryData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/retrydata", "source_page_title": "Gradio - Retrydata Docs"}, {"text": "import gradio as gr\n    \n    def retry(retry_data: gr.RetryData, history: list[gr.MessageDict]):\n        history_up_to_retry = history[:retry_data.index]\n        new_response = \"\"\n        for token in api.chat_completion(history):\n            new_response += token\n            yield history + [new_response]\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot()\n        chatbot.retry(retry, chatbot, chatbot)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/retrydata", "source_page_title": "Gradio - Retrydata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the user message that should be retried.\n\n\n    \n    \n    value: Any\n\nThe value of the user message that should be retried.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/retrydata", "source_page_title": "Gradio - Retrydata Docs"}, {"text": "Creates a gallery component that allows displaying a grid of images or\nvideos, and optionally captions. If used as an input, the user can upload\nimages or videos to the gallery. If used as an output, the user can click on\nindividual images or videos to view them at a higher resolution.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "**As input component** : Passes the list of images or videos as a list of\n(media, caption) tuples, or a list of (media, None) tuples if no captions are\nprovided (which is usually the case). Images can be a `str` file path, a\n`numpy` array, or a `PIL.Image` object depending on `type`. Videos are always\n`str` file path.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: List[tuple[str, str | None]] | List[tuple[PIL.Image.Image, str | None]] | List[tuple[np.ndarray, str | None]] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects the function to return a `list` of images\nor videos, or `list` of (media, `str` caption) tuples. Each image can be a\n`str` file path, a `numpy` array, or a `PIL.Image` object. Each video can be a\n`str` file path.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> list[GalleryImageType | CaptionedGalleryImageType] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: list[np.ndarray | PIL.Image.Image | str | Path | tuple] | Callable | None\n\ndefault `= None`\n\nList of images or videos to display in the gallery by default. If a function\nis provided, the function will be called each time the app loads to set the\ninitial value of this component.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFormat to save images before they are returned to the frontend, such as 'jpeg'\nor 'png'. This parameter only applies to images that are returned from the\nprediction function as numpy arrays or PIL Images. The format should be\nsupported by the PIL library.\n\n\n    \n    \n    file_types: list[str] | None\n\ndefault `= None`\n\nList of file extensions or types of files to be uploaded (e.g. ['image',\n'.mp4']), when this is used as an input component. \"image\" allows only image\nfiles to be uploaded, \"video\" allows only video files to be uploaded, \".mp4\"\nallows only mp4 files to be uploaded, etc. If None, any image and video files\ntypes are allowed.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf Tru", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "re not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    columns: int | None\n\ndefault `= 2`\n\nRepresents the number of images that should be shown in one row.\n\n\n    \n    \n    rows: int | None\n\ndefault `= None`\n\nRepresents the number of rows in the image grid.\n\n\n    \n    \n    height: int | float | str | None\n\ndefault `= None`\n\nThe height of the gallery component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If more images are displayed\nthan can fit in the height, a scrollbar will appear.\n\n\n    \n    \n    allow_preview: bool\n\ndefault `= True`\n\nIf True, images in the gallery will be enlarged when they are clicked. Default\nis True.\n\n\n    \n    \n    preview: bool | None\n\ndefault `= None`\n\nIf True, Gallery will start in preview mode, which shows all of the images as\nthumbnails and allows the user to click on them to view them in full size.\nOnly works if allow_preview is True.\n\n\n    \n    \n    selected_index: int | None\n\ndefault `= None`\n\nThe index of the image that should be initially selected. If None, no image\nwill be selected at start. If provided, will set Gallery to preview mode\nunless allow_preview is set to False.\n\n\n    \n    \n    object_fit: Literal['contain', 'cover', 'fill', 'none', 'scale-down'] | None\n\ndefault `= None`\n\nCSS object-fit property for the thumbnail images in the gallery. Can be\n\"contain\", \"cover\", \"fill\", \"none\", or \"scale-down\".\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussio", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "wn\".\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    show_download_button: bool | None\n\ndefault `= True`\n\nIf True, will show a download button in the corner of the selected image. If\nFalse, the icon does not appear. Default is True.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, the gallery will be interactive, allowing the user to upload images.\nIf False, the gallery will be static. Default is True.\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"filepath\"`\n\nThe format the image is converted to before being passed into the prediction\nfunction. \"numpy\" converts the image to a numpy array with shape (height,\nwidth, 3) and values from 0 to 255, \"pil\" converts the image to a PIL image\nobject, \"filepath\" passes a str path to a temporary file containing the image.\nIf the image is SVG, the `type` is ignored and the filepath of the SVG is\nreturned.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a fullscreen icon in the corner of the component that\nallows user to view the gallery in fullscreen mode. If False, icon does not\nappear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Gallery` | \"gallery\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "fake_gan\n\nOpen in \ud83c\udfa2 \u2197 This demo needs to be run from the repo folder. python\ndemo/fake_gan/run.py import random import gradio as gr def fake_gan(): images\n= [ (random.choice( [ \"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg\",\n\"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg\",\n\"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg\",\n\"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg\",\n\"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\", ] ), f\"label\n{i}\") for i in range(3) ] return images with gr.Blocks() as demo: gallery =\ngr.Gallery( label=\"Generated images\", show_label=False, elem_id=\"gallery\" ,\ncolumns=1, object_fit=\"contain\", height=\"auto\") btn = gr.Button(\"Generate\nimages\", scale=0) btn.click(fake_gan, None, gallery) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    This demo needs to be run from the repo folder.\n    python demo/fake_gan/run.py\n    import random\n    \n    import gradio as gr\n    \n    def fake_gan():\n        images = [\n            (random.choice(\n                [\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg\",\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg\",\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg\",\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg\",\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\",\n                ]\n            ), f\"label {i}\")\n            for i in range(3", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "                  \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\",\n                ]\n            ), f\"label {i}\")\n            for i in range(3)\n        ]\n        return images\n    \n    with gr.Blocks() as demo:\n        gallery = gr.Gallery(\n            label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n        , columns=1, object_fit=\"contain\", height=\"auto\")\n        btn = gr.Button(\"Generate images\", scale=0)\n    \n        btn.click(fake_gan, None, gallery)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Gallery component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Gallery.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Gallery. Uses event data gradio.SelectData to carry `value` referring to the label of the Gallery, and `selected` to refer to state of the Gallery. See EventData documentation on how to use this event data  \n`Gallery.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Gallery.  \n`Gallery.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Gallery changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Gallery.preview_close(fn, \u00b7\u00b7\u00b7)` | This event is triggered when the Gallery preview is closed by the user  \n`Gallery.preview_open(fn, \u00b7\u00b7\u00b7)` | This event is triggered when the Gallery preview is opened by the user  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.co", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "ple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "ows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_m", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "nents .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "Sidebar is a collapsible panel that renders child components on the left\nside of the screen within a Blocks layout.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "with gr.Blocks() as demo:\n        with gr.Sidebar():\n            gr.Textbox()\n            gr.Button()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nname of the sidebar. Not displayed to the user.\n\n\n    \n    \n    open: bool\n\ndefault `= True`\n\nif True, sidebar is open by default.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    width: int | str\n\ndefault `= 320`\n\nThe width of the sidebar, specified in pixels if a number is passed, or in CSS\nunits if a string is passed.\n\n\n    \n    \n    position: Literal['left', 'right']\n\ndefault `= \"left\"`\n\nThe position of the sidebar in the layout, either \"left\" or \"right\". Defaults\nto \"left\".\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Sidebar.expand(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%2", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Sidebar is expanded.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | B", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": " to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    ", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "spinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are ite", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "ample,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: i", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Sidebar.collapse(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Sidebar is collapsed.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "nds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": " a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": " example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "me to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "Creates an audio component that can be used to upload/record audio (as an\ninput) or display audio (as an output).\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "**As input component** : passes audio as one of these formats (depending on\n`type`): a `str` filepath, or `tuple` of (sample rate in Hz, audio data as\nnumpy array). If the latter, the audio data is a 16-bit `int` array whose\nvalues range from -32768 to 32767 and shape of the audio data array is\n(samples,) for mono audio or (samples, channels) for multi-channel audio.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | tuple[int, np.ndarray] | None\n    )\n    \t...\n\n  \n\n**As output component** : expects audio data in any of these formats: a\n`str` or `pathlib.Path` filepath or URL to an audio file, or a `bytes` object\n(recommended for streaming), or a `tuple` of (sample rate in Hz, audio data as\nnumpy array). Note: if audio is supplied as a numpy array, the audio will be\nnormalized by its peak value to avoid distortion or clipping in the resulting\naudio.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | bytes | tuple[int, np.ndarray] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Path | tuple[int, np.ndarray] | Callable | None\n\ndefault `= None`\n\nA path, URL, or [sample_rate, numpy array] tuple (sample rate in Hz, audio\ndata as a float or int numpy array) for the default value that Audio component\nis going to take. If a function is provided, the function will be called each\ntime the app loads to set the initial value of this component.\n\n\n    \n    \n    sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None\n\ndefault `= None`\n\nA list of sources permitted for audio. \"upload\" creates a box where user can\ndrop an audio file, \"microphone\" creates a microphone input. The first element\nin the list will be used as the default source. If None, defaults to\n[\"upload\", \"microphone\"], or [\"microphone\"] if `streaming` is True.\n\n\n    \n    \n    type: Literal['numpy', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe format the audio file is converted to before being passed into the\nprediction function. \"numpy\" converts the audio to a tuple consisting of: (int\nsample rate, numpy.array for the data), \"filepath\" passes a str path to a\ntemporary file containing the audio.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nRelative width compared to adjacent Components in a Row. For example, if\nComponent A has scale=2, and Component B has scale=1, A will be twice as wide\nas B. Should be an integer.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nMinimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, will allow users to upload and edit an audio file. If False, can only\nbe used to play audio. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    streaming: bool\n\ndefault `= False`\n\nIf set to True when used in a `live` interface as an input, will automatically\nstream webcam feed. When used set as an output, takes audio chunks yield from\nthe backend and combines them into one streaming audio output.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component w", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    format: Literal['wav', 'mp3'] | None\n\ndefault `= None`\n\nthe file extension with which to save audio files. Either 'wav' or 'mp3'. wav\nfiles are lossless but will tend to be larger files. mp3 files tend to be\nsmaller. This parameter applies both when this component is used as an input\n(and `type` is \"filepath\") to determine which file format to convert user-\nprovided audio to, and when this component is used as an output to determine\nthe format of audio returned to the user. If None, no file format conversion\nis done and the audio is kept as is. In the case where output audio is\nreturned from the prediction function as numpy array and no `format` is\nprovided, it will be returned as a \"wav\" file.\n\n\n    \n    \n    autoplay: bool\n\ndefault `= False`\n\nWhether to automatically play the audio when the component is used as an\noutput. Note: browsers will not autoplay audio files if the user has not\ninteracted with the page yet.\n\n\n    \n    \n    show_down", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "hether to automatically play the audio when the component is used as an\noutput. Note: browsers will not autoplay audio files if the user has not\ninteracted with the page yet.\n\n\n    \n    \n    show_download_button: bool | None\n\ndefault `= None`\n\nIf True, will show a download button in the corner of the component for saving\naudio. If False, icon does not appear. By default, it will be True for output\ncomponents and False for input components.\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    editable: bool\n\ndefault `= True`\n\nIf True, allows users to manipulate the audio file if the component is\ninteractive. Defaults to True.\n\n\n    \n    \n    min_length: int | None\n\ndefault `= None`\n\nThe minimum length of audio (in seconds) that the user can pass into the\nprediction function. If None, there is no minimum length.\n\n\n    \n    \n    max_length: int | None\n\ndefault `= None`\n\nThe maximum length of audio (in seconds) that the user can pass into the\nprediction function. If None, there is no maximum length.\n\n\n    \n    \n    waveform_options: WaveformOptions | dict | None\n\ndefault `= None`\n\nA dictionary of options for the waveform display. Options include:\nwaveform_color (str), waveform_progress_color (str), show_controls (bool),\nskip_length (int), trim_region_color (str). Default is None, which uses the\ndefault values for these options. See `gr.WaveformOptions` docs.\n\n\n    \n    \n    loop: bool\n\ndefault `= False`\n\nIf True, the audio will loop when it reaches the end and continue playing from\nthe beginning.\n\n\n    \n    \n    recording: bool\n\ndefault `= False`\n\nIf True, the audio component will be set to record audio from the microphone\nif the source is set to \"microphone\". Def", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "e playing from\nthe beginning.\n\n\n    \n    \n    recording: bool\n\ndefault `= False`\n\nIf True, the audio component will be set to record audio from the microphone\nif the source is set to \"microphone\". Defaults to False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Audio` | \"audio\" | Uses default values  \n`gradio.Microphone` | \"microphone\" | Uses sources=[\"microphone\"]  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "generate_tonereverse_audio\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr notes = [\"C\", \"C\", \"D\",\n\"D\", \"E\", \"F\", \"F\", \"G\", \"G\", \"A\", \"A\", \"B\"] def generate_tone(note,\noctave, duration): sr = 48000 a4_freq, tones_from_a4 = 440, 12 * (octave - 4)\n+ (note - 9) frequency = a4_freq * 2 ** (tones_from_a4 / 12) duration =\nint(duration) audio = np.linspace(0, duration, duration * sr) audio = (20000 *\nnp.sin(audio * (2 * np.pi * frequency))).astype(np.int16) return sr, audio\ndemo = gr.Interface( generate_tone, [ gr.Dropdown(notes, type=\"index\"),\ngr.Slider(4, 6, step=1), gr.Textbox(value=\"1\", label=\"Duration in seconds\"),\n], \"audio\", ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    import gradio as gr\n    \n    notes = [\"C\", \"C\", \"D\", \"D\", \"E\", \"F\", \"F\", \"G\", \"G\", \"A\", \"A\", \"B\"]\n    \n    def generate_tone(note, octave, duration):\n        sr = 48000\n        a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n        frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n        duration = int(duration)\n        audio = np.linspace(0, duration, duration * sr)\n        audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n        return sr, audio\n    \n    demo = gr.Interface(\n        generate_tone,\n        [\n            gr.Dropdown(notes, type=\"index\"),\n            gr.Slider(4, 6, step=1),\n            gr.Textbox(value=\"1\", label=\"Duration in seconds\"),\n        ],\n        \"audio\",\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr def reverse_audio(audio):\nsr, data = audio return (sr, np.flipud(data)) input_audio = gr.Audio(\nsources=[\"microphone\"], waveform_options=gr.WaveformOptions(\nwaveform_color=\"01C6FF\", waveform_progress_color=\"0066B4\", skip_length=2,\nshow_controls=False, ), ) demo = gr.Interface( fn=reverse_audio,\ninputs=input_audio, outputs=\"audio\" ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    \n    ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "ip_length=2,\nshow_controls=False, ), ) demo = gr.Interface( fn=reverse_audio,\ninputs=input_audio, outputs=\"audio\" ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    \n    import gradio as gr\n    \n    def reverse_audio(audio):\n        sr, data = audio\n        return (sr, np.flipud(data))\n    \n    input_audio = gr.Audio(\n        sources=[\"microphone\"],\n        waveform_options=gr.WaveformOptions(\n            waveform_color=\"01C6FF\",\n            waveform_progress_color=\"0066B4\",\n            skip_length=2,\n            show_controls=False,\n        ),\n    )\n    demo = gr.Interface(\n        fn=reverse_audio,\n        inputs=input_audio,\n        outputs=\"audio\"\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Audio component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Audio.stream(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user streams the Audio.  \n`Audio.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Audio changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Audio.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Audio using the clear button for the component.  \n`Audio.play(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user plays the media in the Audio.  \n`Audio.pause(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the media in the Audio stops for any reason.  \n`Audio.stop(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the Audio.  \n`Audio.pause(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the media in the Audio stops for any reason.  \n`Audio.start_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user starts recording with the Audio.  \n`Audio.pause_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user pauses recording with the Audio.  \n`Audio.stop_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user stops recording with the Audio.  \n`Audio.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Audio.  \n`Audio.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Audio.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "s triggered when the user uploads a file into the Audio.  \n`Audio.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Audio.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "iption. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"minimal\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postpr", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "nt data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited b", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "gradio.WaveformOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the waveform display in the Audio\ncomponent. An instance of this class can be passed into the `waveform_options`\nparameter of `gr.Audio`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    waveform_color: str | None\n\ndefault `= None`\n\nThe color (as a hex string or valid CSS color) of the full waveform\nrepresenting the amplitude of the audio. Defaults to a light gray color.\n\n\n    \n    \n    waveform_progress_color: str | None\n\ndefault `= None`\n\nThe color (as a hex string or valid CSS color) that the waveform fills with to\nas the audio plays. Defaults to the accent color.\n\n\n    \n    \n    trim_region_color: str | None\n\ndefault `= None`\n\nThe color (as a hex string or valid CSS color) of the trim region. Defaults to\nthe accent color.\n\n\n    \n    \n    show_recording_waveform: bool\n\ndefault `= True`\n\nIf True, shows a waveform when recording audio or playing audio. If False,\nuses the default browser audio players. For streamed audio, the default\nbrowser audio player is always used.\n\n\n    \n    \n    show_controls: bool\n\ndefault `= False`\n\nDeprecated and has no effect. Use `show_recording_waveform` instead.\n\n\n    \n    \n    skip_length: int | float\n\ndefault `= 5`\n\nThe percentage (between 0 and 100) of the audio to skip when clicking on the\nskip forward / skip backward buttons.\n\n\n    \n    \n    sample_rate: int\n\ndefault `= 44100`\n\nThe output sample rate (in Hz) of the audio after editing.\n\n", "heading1": "WaveformOptions", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "Accordion is a layout element which can be toggled to show/hide the\ncontained content.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "with gr.Accordion(\"See Details\"):\n        gr.Markdown(\"lorem ipsum\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nname of accordion section.\n\n\n    \n    \n    open: bool\n\ndefault `= True`\n\nif True, accordion is open by default.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Accordion.expand(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Accordion is expanded.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "onds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "s a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "r example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limi", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "ame to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Accordion.collapse(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20In", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Accordion is collapsed.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Compon", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "esponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n  ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "hows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": " For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_l", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "i_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Accordion.expand(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Accordion is expanded.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "onds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "s a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "r example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limi", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "ame to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Accordion.collapse(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20In", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Accordion is collapsed.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Compon", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "esponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n  ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "hows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": " For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_l", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "i_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "A TabbedInterface is created by providing a list of Interfaces or Blocks,\neach of which gets rendered in a separate tab. Only the components from the\nInterface/Blocks will be rendered in the tab. Certain high-level attributes of\nthe Blocks (e.g. custom `css`, `js`, and `head` attributes) will not be\nloaded.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/tabbedinterface", "source_page_title": "Gradio - Tabbedinterface Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    interface_list: list[Blocks]\n\nA list of Interfaces (or Blocks) to be rendered in the tabs.\n\n\n    \n    \n    tab_names: list[str] | None\n\ndefault `= None`\n\nA list of tab names. If None, the tab names will be \"Tab 1\", \"Tab 2\", etc.\n\n\n    \n    \n    title: str | None\n\ndefault `= None`\n\nThe tab title to display when this demo is opened in a browser window.\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\nA Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nWhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable or default to True.\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a string or path to a css file. This css will be included in the\ndemo webpage.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a string or path to a js file. The custom js should in the form\nof a single js function. This function will automatically be executed when the\npage loads. For more flexibility, use the head parameter to insert js inside\n<script> tags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html to insert into the head of the demo webpage. This can be used to\nadd custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/tabbedinterface", "source_page_title": "Gradio - Tabbedinterface Docs"}, {"text": "tabbed_interface_lite\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr hello_world = gr.Interface(lambda name: \"Hello\n\" + name, \"text\", \"text\") bye_world = gr.Interface(lambda name: \"Bye \" + name,\n\"text\", \"text\") chat = gr.ChatInterface(lambda *args: \"Hello \" + args[0]) demo\n= gr.TabbedInterface([hello_world, bye_world, chat], [\"Hello World\", \"Bye\nWorld\", \"Chat\"]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    hello_world = gr.Interface(lambda name: \"Hello \" + name, \"text\", \"text\")\n    bye_world = gr.Interface(lambda name: \"Bye \" + name, \"text\", \"text\")\n    chat = gr.ChatInterface(lambda *args: \"Hello \" + args[0])\n    \n    demo = gr.TabbedInterface([hello_world, bye_world, chat], [\"Hello World\", \"Bye World\", \"Chat\"])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/tabbedinterface", "source_page_title": "Gradio - Tabbedinterface Docs"}, {"text": "Creates a component to display arbitrary HTML output. As this component\ndoes not accept user input, it is rarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "**As input component** : (Rarely used) passes the HTML as a `str`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` consisting of valid HTML.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Callable | None\n\ndefault `= None`\n\nThe HTML content to display. Only static HTML is rendered (e.g. no JavaScript.\nTo render JavaScript, use the `js` or `head` parameters in the `Blocks`\nconstructor). If a function is provided, the function will be called each time\nthe app loads to set the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe label for this component. Is used as the header if there are a table of\nexamples for this component. If None and used in a `gr.Interface`, the label\nwill be the name of the parameter this component is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool\n\ndefault `= False`\n\nIf True, the label will be displayed. If False, the label will be hidden.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    k", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "ault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    min_height: int | None\n\ndefault `= None`\n\nThe minimum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If HTML content exceeds the\nheight, the component will expand to fit the content.\n\n\n    \n    \n    max_height: int | None\n\ndefault `= None`\n\nThe maximum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If content exceeds the height,\nthe component will scroll.\n\n\n    \n    \n    container: bool\n\ndefault `= False`\n\nIf True, the HTML component will be displayed in a container. Default is\nFalse.\n\n\n    \n    \n    padding: bool\n\ndefault `= True`\n\nIf True, the HTML component will have a certain padding (set by the `--block-\npadding` CSS variable) in all directions. Default is True.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.HTML` | \"html\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "blocks_scroll\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr demo = gr.Blocks() with demo: inp =\ngr.Textbox(placeholder=\"Enter text.\") scroll_btn = gr.Button(\"Scroll\")\nno_scroll_btn = gr.Button(\"No Scroll\") big_block = gr.HTML(\"\"\" <div\nstyle='height: 800px; width: 100px; background-color: pink;'></div> \"\"\") out =\ngr.Textbox() scroll_btn.click(lambda x: x, inputs=inp, outputs=out,\nscroll_to_output=True) no_scroll_btn.click(lambda x: x, inputs=inp,\noutputs=out) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    demo = gr.Blocks()\n    \n    with demo:\n        inp = gr.Textbox(placeholder=\"Enter text.\")\n        scroll_btn = gr.Button(\"Scroll\")\n        no_scroll_btn = gr.Button(\"No Scroll\")\n        big_block = gr.HTML(\"\"\"\n        \n    \n    \n        \"\"\")\n        out = gr.Textbox()\n    \n        scroll_btn.click(lambda x: x,\n                   inputs=inp,\n                   outputs=out,\n                    scroll_to_output=True)\n        no_scroll_btn.click(lambda x: x,\n                   inputs=inp,\n                   outputs=out)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe HTML component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`HTML.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the HTML changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`HTML.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the HTML is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API do", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "i_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": ".\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "er the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "Creates a chatbot that displays user-submitted messages and responses.\nSupports a subset of Markdown including bold, italics, code, tables. Also\nsupports audio/video/image files, which are displayed in the Chatbot, and\nother kinds of files which are displayed as links. This component is usually\nused as an output component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "The data format accepted by the Chatbot is dictated by the `type` parameter.\nThis parameter can take two values, `'tuples'` and `'messages'`. The\n`'tuples'` type is deprecated and will be removed in a future version of\nGradio.\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "If the `type` is `'messages'`, then the data sent to/from the chatbot will be\na list of dictionaries with `role` and `content` keys. This format is\ncompliant with the format expected by most LLM APIs (HuggingChat, OpenAI,\nClaude). The `role` key is either `'user'` or `'assistant'` and the `content`\nkey can be one of the following should be a string (rendered as markdown/html)\nor a Gradio component (useful for displaying files).\n\nAs an example:\n\n    \n    \n    import gradio as gr\n    \n    history = [\n        {\"role\": \"assistant\", \"content\": \"I am happy to provide you that report and plot.\"},\n        {\"role\": \"assistant\", \"content\": gr.Plot(value=make_plot_from_file('quaterly_sales.txt'))}\n    ]\n    \n    with gr.Blocks() as demo:\n        gr.Chatbot(history, type=\"messages\")\n    \n    demo.launch()\n\nFor convenience, you can use the `ChatMessage` dataclass so that your text\neditor can give you autocomplete hints and typechecks.\n\n    \n    \n    import gradio as gr\n    \n    history = [\n        gr.ChatMessage(role=\"assistant\", content=\"How can I help you?\"),\n        gr.ChatMessage(role=\"user\", content=\"Can you make me a plot of quarterly sales?\"),\n        gr.ChatMessage(role=\"assistant\", content=\"I am happy to provide you that report and plot.\")\n    ]\n    \n    with gr.Blocks() as demo:\n        gr.Chatbot(history, type=\"messages\")\n    \n    demo.launch()\n\n", "heading1": "Message format", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: list[MessageDict | Message] | TupleFormat | Callable | None\n\ndefault `= None`\n\nDefault list of messages to show in chatbot, where each message is of the\nformat {\"role\": \"user\", \"content\": \"Help me.\"}. Role can be one of \"user\",\n\"assistant\", or \"system\". Content should be either text, or media passed as a\nGradio component, e.g. {\"content\": gr.Image(\"lion.jpg\")}. If a function is\nprovided, the function will be called each time the app loads to set the\ninitial value of this component.\n\n\n    \n    \n    type: Literal['messages', 'tuples'] | None\n\ndefault `= None`\n\nThe format of the messages passed into the chat history parameter of `fn`. If \"messages\", passes the value as a list of dictionaries with openai-style \"role\" and \"content\" keys. The \"content\" key's value should be one of the following - (1) strings in valid Markdown (2) a dictionary with a \"path\" key and value corresponding to the file to display or (3) an instance of a Gradio component. At the moment Image, Plot, Video, Gallery, Audio, HTML, and Model3D are supported. The \"role\" key should be one of 'user' or 'assistant'. Any other roles will not be displayed in the output. If this parameter is 'tuples', expects a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message, but this format is deprecated.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n   ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": " to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    render", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "omatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= 400`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. If messages exceed the height, the component\nwill scroll.\n\n\n    \n    \n    resizable: bool\n\ndefault `= False`\n\nIf True, the user of the Gradio app can resize the chatbot by dragging the\nbottom right corner.\n\n\n    \n    \n    resizeable: bool\n\ndefault `= False`\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= None`\n\nThe maximum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If messages exceed the height,\nthe component will scroll. If messages are shorter than the height, the\ncomponent will shrink to fit the content. Will not have any effect if `height`\nis set and is smaller than `max_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the c", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "ill shrink to fit the content. Will not have any effect if `height`\nis set and is smaller than `max_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If messages exceed the height,\nthe component will expand to fit the content. Will not have any effect if\n`height` is set and is larger than `min_height`.\n\n\n    \n    \n    editable: Literal['user', 'all'] | None\n\ndefault `= None`\n\nAllows user to edit messages in the chatbot. If set to \"user\", allows editing\nof user messages. If set to \"all\", allows editing of assistant messages as\nwell.\n\n\n    \n    \n    latex_delimiters: list[dict[str, str | bool]] | None\n\ndefault `= None`\n\nA list of dicts of the form {\"left\": open delimiter (str), \"right\": close\ndelimiter (str), \"display\": whether to display in newline (bool)} that will be\nused to render LaTeX expressions. If not provided, `latex_delimiters` is set\nto `[{ \"left\": \"$$\", \"right\": \"$$\", \"display\": True }]`, so only expressions\nenclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass\nin an empty list to disable LaTeX rendering. For more information, see the\n[KaTeX documentation](https://katex.org/docs/autorender.html).\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True, sets the direction of the rendered text to right-to-left. Default is\nFalse, which renders text left-to-right.\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, will show a copy button for each chatbot message.\n\n\n    \n    \n    watermark: str | None\n\ndefault `= None`\n\nIf provided, this tex", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "se.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, will show a copy button for each chatbot message.\n\n\n    \n    \n    watermark: str | None\n\ndefault `= None`\n\nIf provided, this text will be appended to the end of messages copied from the\nchatbot, after a blank line. Useful for indicating that the message is\ngenerated by an AI model.\n\n\n    \n    \n    avatar_images: tuple[str | Path | None, str | Path | None] | None\n\ndefault `= None`\n\nTuple of two avatar image paths or URLs for user and bot (in that order). Pass\nNone for either the user or bot image to skip. Must be within the working\ndirectory of the Gradio app or an external URL.\n\n\n    \n    \n    sanitize_html: bool\n\ndefault `= True`\n\nIf False, will disable HTML sanitization for chatbot messages. This is not\nrecommended, as it can lead to security vulnerabilities.\n\n\n    \n    \n    render_markdown: bool\n\ndefault `= True`\n\nIf False, will disable Markdown rendering for chatbot messages.\n\n\n    \n    \n    feedback_options: list[str] | tuple[str, ...] | None\n\ndefault `= ('Like', 'Dislike')`\n\nA list of strings representing the feedback options that will be displayed to\nthe user. The exact case-sensitive strings \"Like\" and \"Dislike\" will render as\nthumb icons, but any other choices will appear under a separate flag icon.\n\n\n    \n    \n    feedback_value: list[str | None] | None\n\ndefault `= None`\n\nA list of strings representing the feedback state for entire chat. Only works\nwhen type=\"messages\". Each entry in the list corresponds to that assistant\nmessage, in order, and the value is the feedback given (e.g. \"Like\",\n\"Dislike\", or any custom feedback option) or None if no feedback was given for\nthat message.\n\n\n    \n    \n    bubble_full_width: <class 'inspect._empty'>\n\ndefault `= None`\n\nDeprecated.\n\n\n    \n    \n    line_breaks: bool\n\ndefault `= True`\n\nIf True (default), will enable Github-flavored Markdown line breaks in chatbot\nmessages. If False, single new lines will be ignored. Only applies if\n`render_markdown`", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "e_breaks: bool\n\ndefault `= True`\n\nIf True (default), will enable Github-flavored Markdown line breaks in chatbot\nmessages. If False, single new lines will be ignored. Only applies if\n`render_markdown` is True.\n\n\n    \n    \n    layout: Literal['panel', 'bubble'] | None\n\ndefault `= None`\n\nIf \"panel\", will display the chatbot in a llm style layout. If \"bubble\", will\ndisplay the chatbot with message bubbles, with the user and bot messages on\nalterating sides. Will default to \"bubble\".\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\na placeholder message to display in the chatbot when it is empty. Centered\nvertically and horizontally in the Chatbot. Supports Markdown and HTML. If\nNone, no placeholder is displayed.\n\n\n    \n    \n    examples: list[ExampleMessage] | None\n\ndefault `= None`\n\nA list of example messages to display in the chatbot before any user/assistant\nmessages are shown. Each example should be a dictionary with an optional\n\"text\" key representing the message that should be populated in the Chatbot\nwhen clicked, an optional \"files\" key, whose value should be a list of files\nto populate in the Chatbot, an optional \"icon\" key, whose value should be a\nfilepath or URL to an image to display in the example box, and an optional\n\"display_text\" key, whose value should be the text to display in the example\nbox. If \"display_text\" is not provided, the value of \"text\" will be displayed.\n\n\n    \n    \n    show_copy_all_button: <class 'inspect._empty'>\n\ndefault `= False`\n\nIf True, will show a copy all button that copies all chatbot messages to the\nclipboard.\n\n\n    \n    \n    allow_file_downloads: <class 'inspect._empty'>\n\ndefault `= True`\n\nIf True, will show a download button for chatbot messages that contain media.\nDefaults to True.\n\n\n    \n    \n    group_consecutive_messages: bool\n\ndefault `= True`\n\nIf True, will display consecutive messages from the same role in the same\nbubble. If False, will display each message in a separate bubble. Defaults to\nTrue.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "sages: bool\n\ndefault `= True`\n\nIf True, will display consecutive messages from the same role in the same\nbubble. If False, will display each message in a separate bubble. Defaults to\nTrue.\n\n\n    \n    \n    allow_tags: list[str] | bool\n\ndefault `= False`\n\nIf a list of tags is provided, these tags will be preserved in the output\nchatbot messages, even if `sanitize_html` is `True`. For example, if this list\nis [\"thinking\"], the tags `<thinking>` and `</thinking>` will not be removed.\nIf True, all custom tags (non-standard HTML tags) will be preserved. If False,\nno tags will be preserved (default behavior).\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Chatbot` | \"chatbot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "**Displaying Thoughts/Tool Usage**\n\nWhen `type` is `messages`, you can provide additional metadata regarding any\ntools used to generate the response. This is useful for displaying the thought\nprocess of LLM agents. For example,\n\n    \n    \n    def generate_response(history):\n        history.append(\n            ChatMessage(role=\"assistant\",\n                        content=\"The weather API says it is 20 degrees Celcius in New York.\",\n                        metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool Weather API\"})\n            )\n        return history\n\nWould be displayed as following:\n\n![Gradio chatbot tool display](https://github.com/user-\nattachments/assets/c1514bc9-bc29-4af1-8c3f-cd4a7c2b217f)\n\nYou can also specify metadata with a plain python dictionary,\n\n    \n    \n    def generate_response(history):\n        history.append(\n            dict(role=\"assistant\",\n                 content=\"The weather API says it is 20 degrees Celcius in New York.\",\n                 metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool Weather API\"})\n            )\n        return history\n\n**Using Gradio Components Inside`gr.Chatbot`**\n\nThe `Chatbot` component supports using many of the core Gradio components\n(such as `gr.Image`, `gr.Plot`, `gr.Audio`, and `gr.HTML`) inside of the\nchatbot. Simply include one of these components in your list of tuples. Here\u2019s\nan example:\n\n    \n    \n    import gradio as gr\n    \n    def load():\n        return [\n            (\"Here's an audio\", gr.Audio(\"https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav\")),\n            (\"Here's an video\", gr.Video(\"https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4\"))\n        ]\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot()\n        button = gr.Button(\"Load audio and video\")\n        button.click(load, None, chatbot)\n    \n    demo.launch()\n\n", "heading1": "Examples", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "chatbot_simplechatbot_streamingchatbot_with_toolschatbot_core_components\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import random import time with gr.Blocks() as\ndemo: chatbot = gr.Chatbot(type=\"messages\") msg = gr.Textbox() clear =\ngr.ClearButton([msg, chatbot]) def respond(message, chat_history): bot_message\n= random.choice([\"How are you?\", \"Today is a great day\", \"I'm very hungry\"])\nchat_history.append({\"role\": \"user\", \"content\": message})\nchat_history.append({\"role\": \"assistant\", \"content\": bot_message})\ntime.sleep(2) return \"\", chat_history msg.submit(respond, [msg, chatbot],\n[msg, chatbot]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import random\n    import time\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(type=\"messages\")\n        msg = gr.Textbox()\n        clear = gr.ClearButton([msg, chatbot])\n    \n        def respond(message, chat_history):\n            bot_message = random.choice([\"How are you?\", \"Today is a great day\", \"I'm very hungry\"])\n            chat_history.append({\"role\": \"user\", \"content\": message})\n            chat_history.append({\"role\": \"assistant\", \"content\": bot_message})\n            time.sleep(2)\n            return \"\", chat_history\n    \n        msg.submit(respond, [msg, chatbot], [msg, chatbot])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import random import time with gr.Blocks() as\ndemo: chatbot = gr.Chatbot(type=\"messages\") msg = gr.Textbox() clear =\ngr.Button(\"Clear\") def user(user_message, history: list): return \"\", history +\n[{\"role\": \"user\", \"content\": user_message}] def bot(history: list):\nbot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\nhistory.append({\"role\": \"assistant\", \"content\": \"\"}) for character in\nbot_message: history[-1]['content'] += character time.sleep(0.05) yield\nhistory msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\nbot, chatbot, chatbot ) clear.click(lambda: None, N", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "\nbot_message: history[-1]['content'] += character time.sleep(0.05) yield\nhistory msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\nbot, chatbot, chatbot ) clear.click(lambda: None, None, chatbot, queue=False)\nif __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import random\n    import time\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(type=\"messages\")\n        msg = gr.Textbox()\n        clear = gr.Button(\"Clear\")\n    \n        def user(user_message, history: list):\n            return \"\", history + [{\"role\": \"user\", \"content\": user_message}]\n    \n        def bot(history: list):\n            bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n            history.append({\"role\": \"assistant\", \"content\": \"\"})\n            for character in bot_message:\n                history[-1]['content'] += character\n                time.sleep(0.05)\n                yield history\n    \n        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n            bot, chatbot, chatbot\n        )\n        clear.click(lambda: None, None, chatbot, queue=False)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr from gradio import ChatMessage import time def\ngenerate_response(history): history.append( ChatMessage( role=\"user\",\ncontent=\"What is the weather in San Francisco right now?\" ) ) yield history\ntime.sleep(0.25) history.append( ChatMessage( role=\"assistant\", content=\"In\norder to find the current weather in San Francisco, I will need to use my\nweather tool.\", ) ) yield history time.sleep(0.25) history.append(\nChatMessage( role=\"assistant\", content=\"API Error when connecting to weather\nservice.\", metadata={\"title\": \"\ud83d\udca5 Error using tool 'Weather'\"}, ) ) yield\nhistory time.sleep(0.25) history.append( ChatMessage( role=\"assistant\",\ncontent=\"I will try again\", ) ) yield history time.sleep(0.25) history.append(\nChatMessage( role=\"assistant\", content=\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": " ) yield\nhistory time.sleep(0.25) history.append( ChatMessage( role=\"assistant\",\ncontent=\"I will try again\", ) ) yield history time.sleep(0.25) history.append(\nChatMessage( role=\"assistant\", content=\"Weather 72 degrees Fahrenheit with 20%\nchance of rain.\", metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool 'Weather'\"}, ) ) yield\nhistory time.sleep(0.25) history.append( ChatMessage( role=\"assistant\",\ncontent=\"Now that the API succeeded I can complete my task.\", ) ) yield\nhistory time.sleep(0.25) history.append( ChatMessage( role=\"assistant\",\ncontent=\"It's a sunny day in San Francisco with a current temperature of 72\ndegrees Fahrenheit and a 20% chance of rain. Enjoy the weather!\", ) ) yield\nhistory def like(evt: gr.LikeData): print(\"User liked the response\")\nprint(evt.index, evt.liked, evt.value) with gr.Blocks() as demo: chatbot =\ngr.Chatbot(type=\"messages\", height=500, show_copy_button=True) button =\ngr.Button(\"Get San Francisco Weather\") button.click(generate_response,\nchatbot, chatbot) chatbot.like(like) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    from gradio import ChatMessage\n    import time\n    \n    def generate_response(history):\n        history.append(\n            ChatMessage(\n                role=\"user\", content=\"What is the weather in San Francisco right now?\"\n            )\n        )\n        yield history\n        time.sleep(0.25)\n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"In order to find the current weather in San Francisco, I will need to use my weather tool.\",\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"API Error when connecting to weather service.\",\n                metadata={\"title\": \"\ud83d\udca5 Error using tool 'Weather'\"},\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMes", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "r service.\",\n                metadata={\"title\": \"\ud83d\udca5 Error using tool 'Weather'\"},\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"I will try again\",\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"Weather 72 degrees Fahrenheit with 20% chance of rain.\",\n                metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool 'Weather'\"},\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"Now that the API succeeded I can complete my task.\",\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"It's a sunny day in San Francisco with a current temperature of 72 degrees Fahrenheit and a 20% chance of rain. Enjoy the weather!\",\n            )\n        )\n        yield history\n    \n    def like(evt: gr.LikeData):\n        print(\"User liked the response\")\n        print(evt.index, evt.liked, evt.value)\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(type=\"messages\", height=500, show_copy_button=True)\n        button = gr.Button(\"Get San Francisco Weather\")\n        button.click(generate_response, chatbot, chatbot)\n        chatbot.like(like)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import os import plotly.express as px import\nrandom Chatbot demo with multimodal input (text, markdown, LaTeX, code\nblocks, image, audio, video, & model3d). Plus shows support for streaming\ntext. txt = \"\"\" Absolutely! The mycorrhizal network, often referred to as the\n\"Wood Wide Web,\" is a symbiotic association between fungi ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "age, audio, video, & model3d). Plus shows support for streaming\ntext. txt = \"\"\" Absolutely! The mycorrhizal network, often referred to as the\n\"Wood Wide Web,\" is a symbiotic association between fungi and the roots of\nmost plant species. Here\u2019s a deeper dive into how it works and its\nimplications: How It Works 1\\. **Symbiosis**: Mycorrhizal fungi attach to\nplant roots, extending far into the soil. The plant provides the fungi with\ncarbohydrates produced via photosynthesis. In return, the fungi help the plant\nabsorb water and essential nutrients like phosphorus and nitrogen from the\nsoil. 2\\. **Network Formation**: The fungal hyphae (thread-like structures)\nconnect individual plants, creating an extensive underground network. This\nnetwork can link many plants together, sometimes spanning entire forests. 3\\.\n**Communication**: Trees and plants use this network to communicate and share\nresources. For example, a tree under attack by pests can send chemical signals\nthrough the mycorrhizal network to warn neighboring trees. These trees can\nthen produce defensive chemicals to prepare for the impending threat. \nBenefits and Functions 1\\. **Resource Sharing**: The network allows for the\nredistribution of resources among plants. For instance, a large, established\ntree might share excess nutrients and water with smaller, younger trees,\npromoting overall forest health. 2\\. **Defense Mechanism**: The ability to\nshare information about pests and diseases enhances the resilience of plant\ncommunities. This early warning system helps plants activate their defenses\nbefore they are directly affected. 3\\. **Support for Seedlings**: Young\nseedlings, which have limited root systems, benefit immensely from the\nmycorrhizal network. They receive nutrients and water from larger plants,\nincreasing their chances of survival and growth. Ecological Impact 1\\.\n**Biodiversity**: The mycorrhizal network supports biodiversity by fostering a\ncooperative environment. Plants of different spec", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "reasing their chances of survival and growth. Ecological Impact 1\\.\n**Biodiversity**: The mycorrhizal network supports biodiversity by fostering a\ncooperative environment. Plants of different species can coexist and thrive\nbecause of the shared resources and information. 2\\. **Forest Health**: The\nnetwork enhances the overall health of forests. By enabling efficient nutrient\ncycling and supporting plant defenses, it contributes to the stability and\nlongevity of forest ecosystems. 3\\. **Climate Change Mitigation**: Healthy\nforests act as significant carbon sinks, absorbing carbon dioxide from the\natmosphere. The mycorrhizal network plays a critical role in maintaining\nforest health and, consequently, in mitigating climate change. Research\nand Discoveries 1\\. **Suzanne Simard's Work**: Ecologist Suzanne Simard\u2019s\nresearch has been pivotal in uncovering the complexities of the mycorrhizal\nnetwork. She demonstrated that trees of different species can share resources\nand that \"mother trees\" (large, older trees) play a crucial role in nurturing\nyounger plants. 2\\. **Implications for Conservation**: Understanding the\nmycorrhizal network has significant implications for conservation efforts. It\nhighlights the importance of preserving not just individual trees but entire\necosystems, including the fungal networks that sustain them. Practical\nApplications 1\\. **Agriculture**: Farmers and horticulturists are exploring\nthe use of mycorrhizal fungi to improve crop yields and soil health. By\nincorporating these fungi into agricultural practices, they can reduce the\nneed for chemical fertilizers and enhance plant resilience. 2\\.\n**Reforestation**: In reforestation projects, introducing mycorrhizal fungi\ncan accelerate the recovery of degraded lands. The fungi help establish\nhealthy plant communities, ensuring the success of newly planted trees. The\n\"Wood Wide Web\" exemplifies the intricate and often hidden connections that\nsustain life on Earth. It\u2019s a reminder of the pr", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "healthy plant communities, ensuring the success of newly planted trees. The\n\"Wood Wide Web\" exemplifies the intricate and often hidden connections that\nsustain life on Earth. It\u2019s a reminder of the profound interdependence within\nnatural systems and the importance of preserving these delicate relationships.\n\"\"\" def random_plot(): df = px.data.iris() fig = px.scatter( df,\nx=\"sepal_width\", y=\"sepal_length\", color=\"species\", size=\"petal_length\",\nhover_data=[\"petal_width\"], ) return fig color_map = { \"harmful\": \"crimson\",\n\"neutral\": \"gray\", \"beneficial\": \"green\", } def html_src(harm_level): return\nf\"\"\" <div style=\"display: flex; gap: 5px;\"> <div style=\"background-color:\n{color_map[harm_level]}; padding: 2px; border-radius: 5px;\"> {harm_level}\n</div> </div> \"\"\" def print_like_dislike(x: gr.LikeData): print(x.index,\nx.value, x.liked) def random_bokeh_plot(): from bokeh.models import\nColumnDataSource, Whisker from bokeh.plotting import figure from\nbokeh.sampledata.autompg2 import autompg2 as df from bokeh.transform import\nfactor_cmap, jitter classes = sorted(df[\"class\"].unique()) p = figure(\nheight=400, x_range=classes, background_fill_color=\"efefef\", title=\"Car class\nvs HWY mpg with quintile ranges\", ) p.xgrid.grid_line_color = None g =\ndf.groupby(\"class\") upper = g.hwy.quantile(0.80) lower = g.hwy.quantile(0.20)\nsource = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\nerror = Whisker( base=\"base\", upper=\"upper\", lower=\"lower\", source=source,\nlevel=\"annotation\", line_width=2, ) error.upper_head.size = 20\nerror.lower_head.size = 20 p.add_layout(error) p.circle( jitter(\"class\", 0.3,\nrange=p.x_range), \"hwy\", source=df, alpha=0.5, size=13, line_color=\"white\",\ncolor=factor_cmap(\"class\", \"Light6\", classes), ) return p def\nrandom_matplotlib_plot(): import numpy as np import pandas as pd import\nmatplotlib.pyplot as plt countries = [\"USA\", \"Canada\", \"Mexico\", \"UK\"] months\n= [\"January\", \"February\", \"March\", \"April\", \"May\"] m = months.index(\"January\")\nr = 3.2 st", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "as np import pandas as pd import\nmatplotlib.pyplot as plt countries = [\"USA\", \"Canada\", \"Mexico\", \"UK\"] months\n= [\"January\", \"February\", \"March\", \"April\", \"May\"] m = months.index(\"January\")\nr = 3.2 start_day = 30 * m final_day = 30 * (m + 1) x = np.arange(start_day,\nfinal_day + 1) pop_count = {\"USA\": 350, \"Canada\": 40, \"Mexico\": 300, \"UK\":\n120} df = pd.DataFrame({\"day\": x}) for country in countries: df[country] = x\n** (r) * (pop_count[country] + 1) fig = plt.figure() plt.plot(df[\"day\"],\ndf[countries].to_numpy()) plt.title(\"Outbreak in \" + \"January\")\nplt.ylabel(\"Cases\") plt.xlabel(\"Days since Day 0\") plt.legend(countries)\nreturn fig def add_message(history, message): for x in message[\"files\"]:\nhistory.append({\"role\": \"user\", \"content\": {\"path\": x}}) if message[\"text\"] is\nnot None: history.append({\"role\": \"user\", \"content\": message[\"text\"]}) return\nhistory, gr.MultimodalTextbox(value=None, interactive=False) def bot(history,\nresponse_type): msg = {\"role\": \"assistant\", \"content\": \"\"} if response_type ==\n\"plot\": content = gr.Plot(random_plot()) elif response_type == \"bokeh_plot\":\ncontent = gr.Plot(random_bokeh_plot()) elif response_type ==\n\"matplotlib_plot\": content = gr.Plot(random_matplotlib_plot()) elif\nresponse_type == \"gallery\": content = gr.Gallery( [os.path.join(\"files\",\n\"avatar.png\"), os.path.join(\"files\", \"avatar.png\")] ) elif response_type ==\n\"dataframe\": content = gr.Dataframe( interactive=True, headers=[\"One\", \"Two\",\n\"Three\"], col_count=(3, \"fixed\"), row_count=(3, \"fixed\"), value=[[1, 2, 3],\n[4, 5, 6], [7, 8, 9]], label=\"Dataframe\", ) elif response_type == \"image\":\ncontent = gr.Image(os.path.join(\"files\", \"avatar.png\")) elif response_type ==\n\"video\": content = gr.Video(os.path.join(\"files\", \"world.mp4\")) elif\nresponse_type == \"audio\": content = gr.Audio(os.path.join(\"files\",\n\"audio.wav\")) elif response_type == \"audio_file\": content = {\"path\":\nos.path.join(\"files\", \"audio.wav\"), \"alt_text\": \"description\"} elif\nresponse_type == \"image_file\": content = {\"path\":", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "join(\"files\",\n\"audio.wav\")) elif response_type == \"audio_file\": content = {\"path\":\nos.path.join(\"files\", \"audio.wav\"), \"alt_text\": \"description\"} elif\nresponse_type == \"image_file\": content = {\"path\": os.path.join(\"files\",\n\"avatar.png\"), \"alt_text\": \"description\"} elif response_type == \"video_file\":\ncontent = {\"path\": os.path.join(\"files\", \"world.mp4\"), \"alt_text\":\n\"description\"} elif response_type == \"txt_file\": content = {\"path\":\nos.path.join(\"files\", \"sample.txt\"), \"alt_text\": \"description\"} elif\nresponse_type == \"model3d_file\": content = {\"path\": os.path.join(\"files\",\n\"Duck.glb\"), \"alt_text\": \"description\"} elif response_type == \"html\": content\n= gr.HTML( html_src(random.choice([\"harmful\", \"neutral\", \"beneficial\"])) )\nelif response_type == \"model3d\": content = gr.Model3D(os.path.join(\"files\",\n\"Duck.glb\")) else: content = txt msg[\"content\"] = content history.append(msg)\nreturn history fig = random_plot() with gr.Blocks(fill_height=True) as demo:\nchatbot = gr.Chatbot( elem_id=\"chatbot\", type=\"messages\",\nbubble_full_width=False, scale=1, show_copy_button=True, avatar_images=( None,\nos.path.join(\"files\", \"avatar.png\"), os.path.join(\"files\", \"avatar.png\"), ),\n) response_type = gr.Radio( [ \"audio_file\", \"image_file\", \"video_file\",\n\"txt_file\", \"model3d_file\", \"plot\", \"matplotlib_plot\", \"bokeh_plot\", \"image\",\n\"text\", \"gallery\", \"dataframe\", \"video\", \"audio\", \"html\", \"model3d\", ],\nvalue=\"text\", label=\"Response Type\", ) chat_input = gr.MultimodalTextbox(\ninteractive=True, placeholder=\"Enter message or upload file...\",\nshow_label=False, ) chat_msg = chat_input.submit( add_message, [chatbot,\nchat_input], [chatbot, chat_input] ) bot_msg = chat_msg.then( bot, [chatbot,\nresponse_type], chatbot, api_name=\"bot_response\" ) bot_msg.then(lambda:\ngr.MultimodalTextbox(interactive=True), None, [chat_input])\nchatbot.like(print_like_dislike, None, None) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    import os\n    import plotly.express as px\n    import ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": ", None, [chat_input])\nchatbot.like(print_like_dislike, None, None) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    import os\n    import plotly.express as px\n    import random\n    \n    Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, video, & model3d). Plus shows support for streaming text.\n    \n    txt = \"\"\"\n    Absolutely! The mycorrhizal network, often referred to as the \"Wood Wide Web,\" is a symbiotic association between fungi and the roots of most plant species. Here\u2019s a deeper dive into how it works and its implications:\n    \n    How It Works\n    \n    1. **Symbiosis**: Mycorrhizal fungi attach to plant roots, extending far into the soil. The plant provides the fungi with carbohydrates produced via photosynthesis. In return, the fungi help the plant absorb water and essential nutrients like phosphorus and nitrogen from the soil.\n    \n    2. **Network Formation**: The fungal hyphae (thread-like structures) connect individual plants, creating an extensive underground network. This network can link many plants together, sometimes spanning entire forests.\n    \n    3. **Communication**: Trees and plants use this network to communicate and share resources. For example, a tree under attack by pests can send chemical signals through the mycorrhizal network to warn neighboring trees. These trees can then produce defensive chemicals to prepare for the impending threat.\n    \n    Benefits and Functions\n    \n    1. **Resource Sharing**: The network allows for the redistribution of resources among plants. For instance, a large, established tree might share excess nutrients and water with smaller, younger trees, promoting overall forest health.\n    \n    2. **Defense Mechanism**: The ability to share information about pests and diseases enhances the resilience of plant communities. This early warning system helps plants activate their defenses before they are directly affected.\n    \n    3. **Support ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "ormation about pests and diseases enhances the resilience of plant communities. This early warning system helps plants activate their defenses before they are directly affected.\n    \n    3. **Support for Seedlings**: Young seedlings, which have limited root systems, benefit immensely from the mycorrhizal network. They receive nutrients and water from larger plants, increasing their chances of survival and growth.\n    \n    Ecological Impact\n    \n    1. **Biodiversity**: The mycorrhizal network supports biodiversity by fostering a cooperative environment. Plants of different species can coexist and thrive because of the shared resources and information.\n    \n    2. **Forest Health**: The network enhances the overall health of forests. By enabling efficient nutrient cycling and supporting plant defenses, it contributes to the stability and longevity of forest ecosystems.\n    \n    3. **Climate Change Mitigation**: Healthy forests act as significant carbon sinks, absorbing carbon dioxide from the atmosphere. The mycorrhizal network plays a critical role in maintaining forest health and, consequently, in mitigating climate change.\n    \n    Research and Discoveries\n    \n    1. **Suzanne Simard's Work**: Ecologist Suzanne Simard\u2019s research has been pivotal in uncovering the complexities of the mycorrhizal network. She demonstrated that trees of different species can share resources and that \"mother trees\" (large, older trees) play a crucial role in nurturing younger plants.\n    \n    2. **Implications for Conservation**: Understanding the mycorrhizal network has significant implications for conservation efforts. It highlights the importance of preserving not just individual trees but entire ecosystems, including the fungal networks that sustain them.\n    \n    Practical Applications\n    \n    1. **Agriculture**: Farmers and horticulturists are exploring the use of mycorrhizal fungi to improve crop yields and soil health. By incorporating these fungi into agricultur", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "al Applications\n    \n    1. **Agriculture**: Farmers and horticulturists are exploring the use of mycorrhizal fungi to improve crop yields and soil health. By incorporating these fungi into agricultural practices, they can reduce the need for chemical fertilizers and enhance plant resilience.\n    \n    2. **Reforestation**: In reforestation projects, introducing mycorrhizal fungi can accelerate the recovery of degraded lands. The fungi help establish healthy plant communities, ensuring the success of newly planted trees.\n    \n    The \"Wood Wide Web\" exemplifies the intricate and often hidden connections that sustain life on Earth. It\u2019s a reminder of the profound interdependence within natural systems and the importance of preserving these delicate relationships.\n    \"\"\"\n    \n    def random_plot():\n        df = px.data.iris()\n        fig = px.scatter(\n            df,\n            x=\"sepal_width\",\n            y=\"sepal_length\",\n            color=\"species\",\n            size=\"petal_length\",\n            hover_data=[\"petal_width\"],\n        )\n        return fig\n    \n    color_map = {\n        \"harmful\": \"crimson\",\n        \"neutral\": \"gray\",\n        \"beneficial\": \"green\",\n    }\n    \n    def html_src(harm_level):\n        return f\"\"\"\n    \n    \n    \n      \n    \n    \n      {harm_level}\n      \n    \n    \n    \n    \n    \n    \"\"\"\n    \n    def print_like_dislike(x: gr.LikeData):\n        print(x.index, x.value, x.liked)\n    \n    def random_bokeh_plot():\n        from bokeh.models import ColumnDataSource, Whisker\n        from bokeh.plotting import figure\n        from bokeh.sampledata.autompg2 import autompg2 as df\n        from bokeh.transform import factor_cmap, jitter\n    \n        classes = sorted(df[\"class\"].unique())\n    \n        p = figure(\n            height=400,\n            x_range=classes,\n            background_fill_color=\"efefef\",\n            title=\"Car class vs HWY mpg with quintile ranges\",\n        )\n        p.xgrid.grid_line_color = None\n    \n        g = df.groupby(\"class\")\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "s,\n            background_fill_color=\"efefef\",\n            title=\"Car class vs HWY mpg with quintile ranges\",\n        )\n        p.xgrid.grid_line_color = None\n    \n        g = df.groupby(\"class\")\n        upper = g.hwy.quantile(0.80)\n        lower = g.hwy.quantile(0.20)\n        source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n    \n        error = Whisker(\n            base=\"base\",\n            upper=\"upper\",\n            lower=\"lower\",\n            source=source,\n            level=\"annotation\",\n            line_width=2,\n        )\n        error.upper_head.size = 20\n        error.lower_head.size = 20\n        p.add_layout(error)\n    \n        p.circle(\n            jitter(\"class\", 0.3, range=p.x_range),\n            \"hwy\",\n            source=df,\n            alpha=0.5,\n            size=13,\n            line_color=\"white\",\n            color=factor_cmap(\"class\", \"Light6\", classes),\n        )\n        return p\n    \n    def random_matplotlib_plot():\n        import numpy as np\n        import pandas as pd\n        import matplotlib.pyplot as plt\n    \n        countries = [\"USA\", \"Canada\", \"Mexico\", \"UK\"]\n        months = [\"January\", \"February\", \"March\", \"April\", \"May\"]\n        m = months.index(\"January\")\n        r = 3.2\n        start_day = 30 * m\n        final_day = 30 * (m + 1)\n        x = np.arange(start_day, final_day + 1)\n        pop_count = {\"USA\": 350, \"Canada\": 40, \"Mexico\": 300, \"UK\": 120}\n        df = pd.DataFrame({\"day\": x})\n        for country in countries:\n            df[country] = x ** (r) * (pop_count[country] + 1)\n    \n        fig = plt.figure()\n        plt.plot(df[\"day\"], df[countries].to_numpy())\n        plt.title(\"Outbreak in \" + \"January\")\n        plt.ylabel(\"Cases\")\n        plt.xlabel(\"Days since Day 0\")\n        plt.legend(countries)\n        return fig\n    \n    def add_message(history, message):\n        for x in message[\"files\"]:\n            history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n        if message[\"text\"] is not None:\n  ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "eturn fig\n    \n    def add_message(history, message):\n        for x in message[\"files\"]:\n            history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n        if message[\"text\"] is not None:\n            history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n        return history, gr.MultimodalTextbox(value=None, interactive=False)\n    \n    def bot(history, response_type):\n        msg = {\"role\": \"assistant\", \"content\": \"\"}\n        if response_type == \"plot\":\n            content = gr.Plot(random_plot())\n        elif response_type == \"bokeh_plot\":\n            content = gr.Plot(random_bokeh_plot())\n        elif response_type == \"matplotlib_plot\":\n            content =  gr.Plot(random_matplotlib_plot())\n        elif response_type == \"gallery\":\n            content = gr.Gallery(\n                [os.path.join(\"files\", \"avatar.png\"), os.path.join(\"files\", \"avatar.png\")]\n            )\n        elif response_type == \"dataframe\":\n            content = gr.Dataframe(\n                interactive=True,\n                headers=[\"One\", \"Two\", \"Three\"],\n                col_count=(3, \"fixed\"),\n                row_count=(3, \"fixed\"),\n                value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n                label=\"Dataframe\",\n            )\n        elif response_type == \"image\":\n           content = gr.Image(os.path.join(\"files\", \"avatar.png\"))\n        elif response_type == \"video\":\n           content = gr.Video(os.path.join(\"files\", \"world.mp4\"))\n        elif response_type == \"audio\":\n            content = gr.Audio(os.path.join(\"files\", \"audio.wav\"))\n        elif response_type == \"audio_file\":\n            content = {\"path\": os.path.join(\"files\", \"audio.wav\"), \"alt_text\": \"description\"}\n        elif response_type == \"image_file\":\n            content = {\"path\": os.path.join(\"files\", \"avatar.png\"), \"alt_text\": \"description\"}\n        elif response_type == \"video_file\":\n            content = {\"path\": os.path.join(\"files\", \"world.mp4\"), \"alt_text\": \"description\"}\n        elif respo", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": ", \"avatar.png\"), \"alt_text\": \"description\"}\n        elif response_type == \"video_file\":\n            content = {\"path\": os.path.join(\"files\", \"world.mp4\"), \"alt_text\": \"description\"}\n        elif response_type == \"txt_file\":\n            content = {\"path\": os.path.join(\"files\", \"sample.txt\"), \"alt_text\": \"description\"}\n        elif response_type == \"model3d_file\":\n            content = {\"path\": os.path.join(\"files\", \"Duck.glb\"), \"alt_text\": \"description\"}\n        elif response_type == \"html\":\n            content = gr.HTML(\n                html_src(random.choice([\"harmful\", \"neutral\", \"beneficial\"]))\n            )\n        elif response_type == \"model3d\":\n            content = gr.Model3D(os.path.join(\"files\", \"Duck.glb\"))\n        else:\n            content = txt\n        msg[\"content\"] = content \n        history.append(msg)\n        return history\n    \n    fig = random_plot()\n    \n    with gr.Blocks(fill_height=True) as demo:\n        chatbot = gr.Chatbot(\n            elem_id=\"chatbot\",\n            type=\"messages\",\n            bubble_full_width=False,\n            scale=1,\n            show_copy_button=True,\n            avatar_images=(\n                None,  os.path.join(\"files\", \"avatar.png\"),\n                os.path.join(\"files\", \"avatar.png\"),\n            ),\n        )\n        response_type = gr.Radio(\n            [\n                \"audio_file\",\n                \"image_file\",\n                \"video_file\",\n                \"txt_file\",\n                \"model3d_file\",\n                \"plot\",\n                \"matplotlib_plot\",\n                \"bokeh_plot\",\n                \"image\",\n                \"text\",\n                \"gallery\",\n                \"dataframe\",\n                \"video\",\n                \"audio\",\n                \"html\",\n                \"model3d\",\n            ],\n            value=\"text\",\n            label=\"Response Type\",\n        )\n    \n        chat_input = gr.MultimodalTextbox(\n            interactive=True,\n            placeholder=\"Enter message or upload file...\",\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "    value=\"text\",\n            label=\"Response Type\",\n        )\n    \n        chat_input = gr.MultimodalTextbox(\n            interactive=True,\n            placeholder=\"Enter message or upload file...\",\n            show_label=False,\n        )\n    \n        chat_msg = chat_input.submit(\n            add_message, [chatbot, chat_input], [chatbot, chat_input]\n        )\n        bot_msg = chat_msg.then(\n            bot, [chatbot, response_type], chatbot, api_name=\"bot_response\"\n        )\n        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n    \n        chatbot.like(print_like_dislike, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Chatbot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Chatbot.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Chatbot changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Chatbot.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Chatbot. Uses event data gradio.SelectData to carry `value` referring to the label of the Chatbot, and `selected` to refer to state of the Chatbot. See EventData documentation on how to use this event data  \n`Chatbot.like(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user likes/dislikes from within the Chatbot. This event has EventData of type gradio.LikeData that carries information, accessible through LikeData.index and LikeData.value. See EventData documentation on how to use this event data.  \n`Chatbot.retry(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clicks the retry button in the chatbot message.  \n`Chatbot.undo(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clicks the undo button in the chatbot message.  \n`Chatbot.example_select(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clicks on an example from within the Chatbot. This event has SelectData of type gradio.SelectData that carries information, accessible through SelectData.index and SelectData.value. See SelectData documentation on how to use this event data.  \n`Chatbot.option_select(fn, \u00b7\u00b7\u00b7)` | This listener is trigge", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": " carries information, accessible through SelectData.index and SelectData.value. See SelectData documentation on how to use this event data.  \n`Chatbot.option_select(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clicks on an option from within the Chatbot. This event has SelectData of type gradio.SelectData that carries information, accessible through SelectData.index and SelectData.value. See SelectData documentation on how to use this event data.  \n`Chatbot.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Chatbot using the clear button for the component.  \n`Chatbot.copy(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user copies content from the Chatbot. Uses event data gradio.CopyData to carry information about the copied content. See EventData documentation on how to use this event data  \n`Chatbot.edit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user edits the Chatbot (e.g. image) using the built-in editor.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "nents to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "nding, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "gradio.ChatMessage(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass that represents a message in the Chatbot component (with\ntype=\"messages\"). The only required field is `content`. The value of\n`gr.Chatbot` is a list of these dataclasses.\n\nParameters \u25bc\n\n\n    \n    \n    content: str | FileData | Component | FileDataDict | tuple | list\n\nThe content of the message. Can be a string or a Gradio component.\n\n\n    \n    \n    role: Literal['user', 'assistant', 'system']\n\ndefault `= \"assistant\"`\n\nThe role of the message, which determines the alignment of the message in the\nchatbot. Can be \"user\", \"assistant\", or \"system\". Defaults to \"assistant\".\n\n\n    \n    \n    metadata: MetadataDict\n\ndefault `= _HAS_DEFAULT_FACTORY_CLASS()`\n\nThe metadata of the message, which is used to display intermediate thoughts /\ntool usage. Should be a dictionary with the following keys: \"title\" (required\nto display the thought), and optionally: \"id\" and \"parent_id\" (to nest\nthoughts), \"duration\" (to display the duration of the thought), \"status\" (to\ndisplay the status of the thought).\n\n\n    \n    \n    options: list[OptionDict]\n\ndefault `= _HAS_DEFAULT_FACTORY_CLASS()`\n\nThe options of the message. A list of Option objects, which are dictionaries\nwith the following keys: \"label\" (the text to display in the option), and\noptionally \"value\" (the value to return when the option is selected if\ndifferent from the label).\n\n", "heading1": "ChatMessage", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "A typed dictionary to represent metadata for a message in the Chatbot\ncomponent. An instance of this dictionary is used for the `metadata` field in\na ChatMessage when the chat message should be displayed as a thought.\n\nKeys \u25bc\n\n\n    \n    \n    title: str\n\nThe title of the 'thought' message. Only required field.\n\n\n    \n    \n    id: int | str\n\nThe ID of the message. Only used for nested thoughts. Nested thoughts can be\nnested by setting the parent_id to the id of the parent thought.\n\n\n    \n    \n    parent_id: int | str\n\nThe ID of the parent message. Only used for nested thoughts.\n\n\n    \n    \n    log: str\n\nA string message to display next to the thought title in a subdued font.\n\n\n    \n    \n    duration: float\n\nThe duration of the message in seconds. Appears next to the thought title in a\nsubdued font inside a parentheses.\n\n\n    \n    \n    status: Literal['pending', 'done']\n\nif set to `'pending'`, a spinner appears next to the thought title and the\naccordion is initialized open. If `status` is `'done'`, the thought accordion\nis initialized closed. If `status` is not provided, the thought accordion is\ninitialized open and no spinner is displayed.\n\n", "heading1": "MetadataDict", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "A typed dictionary to represent an option in a ChatMessage. A list of these\ndictionaries is used for the `options` field in a ChatMessage.\n\nKeys \u25bc\n\n\n    \n    \n    value: str\n\nThe value to return when the option is selected.\n\n\n    \n    \n    label: str\n\nThe text to display in the option, if different from the value.\n\n", "heading1": "OptionDict", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "Gradio includes pre-built components that can be used as inputs or outputs\nin your Interface or Blocks with a single line of code. Components include\npreprocessing steps that convert user data submitted through browser to\nsomething that be can used by a Python function, and postprocessing steps to\nconvert values returned by a Python function into something that can be\ndisplayed in a browser.\n\nConsider an example with three inputs (Textbox, Number, and Image) and two\noutputs (Number and Gallery), below is a diagram of what our preprocessing\nwill send to the function and what our postprocessing will require from it.\n\n![](/_app/immutable/assets/dataflow.FNuRzshD.svg)\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": "Components also come with certain events that they support. These are\nmethods that are triggered with user actions. Below is a table showing which\nevents are supported for each component. All events are also listed (with\nparameters) in the component\u2019s docs.\n\n| retry| stop| undo| preview_open| change| submit| release| double_click| end|\nedit| example_select| stream| input| pause_recording| like| select|\noption_select| delete| download| preview_close| blur| click| upload|\nstop_recording| copy| pause| clear| start_recording| key_up| focus| play|\ntick| apply  \n---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---  \n[AnnotatedImage](annotatedimage) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Audio](audio) | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2713 | \u2713 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715  \n[BrowserState](browserstate) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Button](button) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Chatbot](chatbot) | \u2713 | \u2715 | \u2713 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Checkbox](checkbox) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[CheckboxGroup](checkboxgroup) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[ClearButton](clearbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Code](code) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": " | \u2715  \n[ClearButton](clearbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Code](code) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[ColorPicker](colorpicker) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[Dataframe](dataframe) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Dataset](dataset) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[DateTime](datetime) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[DeepLinkButton](deeplinkbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Dialogue](dialogue) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[DownloadButton](downloadbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Dropdown](dropdown) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715  \n[DuplicateButton](duplicatebutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[File](file) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[FileExplorer](fileexplorer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | ", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": " \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[FileExplorer](fileexplorer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Gallery](gallery) | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[HighlightedText](highlightedtext) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[HTML](html) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[ImageEditor](imageeditor) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713  \n[Image](image) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[ImageSlider](imageslider) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[JSON](json) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Label](label) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[LoginButton](loginbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Markdown](markdown) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Model3D](model3d) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Textbox](textbox) | \u2715 | \u2713 | \u2715 ", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": "\u2715 | \u2715 | \u2715 | \u2715  \n[Model3D](model3d) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Textbox](textbox) | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[MultimodalTextbox](multimodaltextbox) | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[BarPlot](barplot) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[LinePlot](lineplot) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[ScatterPlot](scatterplot) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Number](number) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[ParamViewer](paramviewer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Plot](plot) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Radio](radio) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Slider](slider) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[State](state) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Timer](timer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 |", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": "\u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Timer](timer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715  \n[UploadButton](uploadbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Video](video) | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2713 | \u2713 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715  \n[SimpleImage](simpleimage) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715\n\n", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": "The gr.KeyUpData class is a subclass of gr.EventData that specifically\ncarries information about the `.key_up()` event. When gr.KeyUpData is added as\na type hint to an argument of an event listener method, a gr.KeyUpData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/keyupdata", "source_page_title": "Gradio - Keyupdata Docs"}, {"text": "import gradio as gr\n    \n    def test(value, key_up_data: gr.KeyUpData):\n        return {\n            \"component value\": value,\n            \"input value\": key_up_data.input_value,\n            \"key\": key_up_data.key\n        }\n    \n    with gr.Blocks() as demo:\n        d = gr.Dropdown([\"abc\", \"def\"], allow_custom_value=True)\n        t = gr.JSON()\n        d.key_up(test, d, t)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/keyupdata", "source_page_title": "Gradio - Keyupdata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    key: str\n\nThe key that was pressed.\n\n\n    \n    \n    input_value: str\n\nThe displayed value in the input textbox after the key was pressed. This may\nbe different than the `value` attribute of the component itself, as the\n`value` attribute of some components (e.g. Dropdown) are not updated until the\nuser presses Enter.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/keyupdata", "source_page_title": "Gradio - Keyupdata Docs"}, {"text": "dropdown_key_up\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def test(value, key_up_data: gr.KeyUpData):\nreturn { \"component value\": value, \"input value\": key_up_data.input_value,\n\"key\": key_up_data.key } with gr.Blocks() as demo: d = gr.Dropdown([\"abc\",\n\"def\"], allow_custom_value=True) t = gr.JSON() d.key_up(test, d, t) if\n__name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def test(value, key_up_data: gr.KeyUpData):\n        return {\n            \"component value\": value,\n            \"input value\": key_up_data.input_value,\n            \"key\": key_up_data.key\n        }\n    \n    with gr.Blocks() as demo:\n        d = gr.Dropdown([\"abc\", \"def\"], allow_custom_value=True)\n        t = gr.JSON()\n        d.key_up(test, d, t)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/keyupdata", "source_page_title": "Gradio - Keyupdata Docs"}, {"text": "**Stream From a Gradio app in 5 lines**\n\n  \n\nUse the `submit` method to get a job you can iterate over.\n\n  \n\nIn python:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"gradio/llm_stream\")\n    \n    for result in client.submit(\"What's the best UI framework in Python?\"):\n        print(result)\n\n  \n\nIn typescript:\n\n    \n    \n    import { Client } from \"@gradio/client\";\n    \n    const client = await Client.connect(\"gradio/llm_stream\")\n    const job = client.submit(\"/predict\", {\"text\": \"What's the best UI framework in Python?\"})\n    \n    for await (const msg of job) console.log(msg.data)\n\n  \n\n**Use the same keyword arguments as the app**\n\n  \nIn the examples below, the upstream app has a function with parameters called\n`message`, `system_prompt`, and `tokens`. We can see that the client `predict`\ncall uses the same arguments.\n\nIn python:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"http://127.0.0.1:7860/\")\n    result = client.predict(\n    \t\tmessage=\"Hello!!\",\n    \t\tsystem_prompt=\"You are helpful AI.\",\n    \t\ttokens=10,\n    \t\tapi_name=\"/chat\"\n    )\n    print(result)\n\nIn typescript:\n\n    \n    \n    import { Client } from \"@gradio/client\";\n    \n    const client = await Client.connect(\"http://127.0.0.1:7860/\");\n    const result = await client.predict(\"/chat\", { \t\t\n    \t\tmessage: \"Hello!!\", \t\t\n    \t\tsystem_prompt: \"Hello!!\", \t\t\n    \t\ttokens: 10, \n    });\n    \n    console.log(result.data);\n\n  \n\n**Better Error Messages**\n\n  \nIf something goes wrong in the upstream app, the client will raise the same\nexception as the app provided that `show_error=True` in the original app's\n`launch()` function, or it's a `gr.Error` exception.\n\n", "heading1": "Ergonomic API \ud83d\udc86", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "Anything you can do in the UI, you can do with the client:\n\n  * \ud83d\udd10Authentication\n  * \ud83d\uded1 Job Cancelling\n  * \u2139\ufe0f Access Queue Position and API\n  * \ud83d\udcd5 View the API information\n\n  \nHere's an example showing how to display the queue position of a pending job:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"gradio/diffusion_model\")\n    \n    job = client.submit(\"A cute cat\")\n    while not job.done():\n        status = job.status()\n        print(f\"Current in position {status.rank} out of {status.queue_size}\")\n\n", "heading1": "Transparent Design \ud83e\ude9f", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "The client can run from pretty much any python and javascript environment\n(node, deno, the browser, Service Workers).  \nHere's an example using the client from a Flask server using gevent:\n\n    \n    \n    from gevent import monkey\n    monkey.patch_all()\n    \n    from gradio_client import Client\n    from flask import Flask, send_file\n    import time\n    \n    app = Flask(__name__)\n    \n    imageclient = Client(\"gradio/diffusion_model\")\n    \n    @app.route(\"/gen\")\n    def gen():\n          result = imageclient.predict(\n                    \"A cute cat\",\n                    api_name=\"/predict\"\n                  )\n          return send_file(result)\n    \n    if __name__ == \"__main__\":\n          app.run(host=\"0.0.0.0\", port=5000)\n\n", "heading1": "Portable Design \u26fa\ufe0f", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "Changes\n\n  \n\n**Python**\n\n  * The `serialize` argument of the `Client` class was removed and has no effect.\n  * The `upload_files` argument of the `Client` was removed.\n  * All filepaths must be wrapped in the `handle_file` method. For example, `caption = client.predict(handle_file('./dog.jpg'))`.\n  * The `output_dir` argument was removed. It is not specified in the `download_files` argument.\n\n  \n\n**Javascript**\n\n  \nThe client has been redesigned entirely. It was refactored from a function\ninto a class. An instance can now be constructed by awaiting the `connect`\nmethod.\n\n    \n    \n    const app = await Client.connect(\"gradio/whisper\")\n\nThe app variable has the same methods as the python class (`submit`,\n`predict`, `view_api`, `duplicate`).\n\n", "heading1": "v1.0 Migration Guide and Breaking", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "ZeroGPU\n\nZeroGPU spaces are rate-limited to ensure that a single user does not hog all\nof the available GPUs. The limit is controlled by a special token that the\nHugging Face Hub infrastructure adds to all incoming requests to Spaces. This\ntoken is a request header called `X-IP-Token` and its value changes depending\non the user who makes a request to the ZeroGPU space.\n\n  \n\nLet\u2019s say you want to create a space (Space A) that uses a ZeroGPU space\n(Space B) programmatically. Simply calling Space B from Space A with the\npython client will quickly exhaust your rate limit, as all the requests to the\nZeroGPU space will have the same token. So in order to avoid this, we need to\nextract the token of the user using Space A before we call Space B\nprogrammatically.\n\n  \n\nHow to do this will be explained in the following section.\n\n", "heading1": "Explaining Rate Limits for", "source_page_url": "https://gradio.app/docs/python-client/using-zero-gpu-spaces", "source_page_title": "Python Client - Using Zero Gpu Spaces Docs"}, {"text": "When a user presses enter in the textbox, we will extract their token from the\n`X-IP-Token` header of the incoming request. We will use this header when\nconstructing the gradio client. The following hypothetical text-to-image\napplication shows how this is done.\n\n  \n\n    \n    \n    import gradio as gr\n    from gradio_client import Client\n    \n    def text_to_image(prompt, request: gr.Request):\n        x_ip_token = request.headers['x-ip-token']\n        client = Client(\"hysts/SDXL\", headers={\"x-ip-token\": x_ip_token})\n        img = client.predict(prompt, api_name=\"/predict\")\n        return img\n    \n    \n    with gr.Blocks() as demo:\n        image = gr.Image()\n        prompt = gr.Textbox(max_lines=1)\n        prompt.submit(text_to_image, [prompt], [image])\n    \n    demo.launch()\n\n", "heading1": "Avoiding Rate Limits", "source_page_url": "https://gradio.app/docs/python-client/using-zero-gpu-spaces", "source_page_title": "Python Client - Using Zero Gpu Spaces Docs"}, {"text": "A Job is a wrapper over the Future class that represents a prediction call\nthat has been submitted by the Gradio client. This class is not meant to be\ninstantiated directly, but rather is created by the Client.submit() method.  \nA Job object includes methods to get the status of the prediction call, as\nwell to get the outputs of the prediction call. Job objects are also iterable,\nand can be used in a loop to get the outputs of prediction calls as they\nbecome available for generator endpoints.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    future: Future\n\nThe future object that represents the prediction call, created by the\nClient.submit() method\n\n\n    \n    \n    communicator: Communicator | None\n\ndefault `= None`\n\nThe communicator object that is used to communicate between the client and the\nbackground thread running the job\n\n\n    \n    \n    verbose: bool\n\ndefault `= True`\n\nWhether to print any status-related messages to the console\n\n\n    \n    \n    space_id: str | None\n\ndefault `= None`\n\nThe space ID corresponding to the Client object that created this Job object\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Job component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`Job.result(fn, \u00b7\u00b7\u00b7)` | Return the result of the call that the future represents. Raises CancelledError: If the future was cancelled, TimeoutError: If the future didn't finish executing before the given timeout, and Exception: If the call raised then that exception will be raised. <br>  \n`Job.outputs(fn, \u00b7\u00b7\u00b7)` | Returns a list containing the latest outputs from the Job. <br> If the endpoint has multiple output components, the list will contain a tuple of results. Otherwise, it will contain the results without storing them in tuples. <br> For endpoints that are queued, this list will contain the final job output even if that endpoint does not use a generator function. <br>  \n`Job.status(fn, \u00b7\u00b7\u00b7)` | Returns the latest status update from the Job in the form of a StatusUpdate object, which contains the following fields: code, rank, queue_size, success, time, eta, and progress_data. <br> progress_data is a list of updates emitted by the gr.Progress() tracker of the event handler. Each element of the list has the following fields: index, length, unit, progress, desc. If the event handler does not have a gr.Progress() tracker, the progress_data field will be None. <br>  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    timeout: float | None\n\ndefault `= None`\n\nThe number of seconds to wait for the result if the future isn't done. If\nNone, then there is no limit on the wait time.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "If you already have a recent version of `gradio`, then the `gradio_client` is\nincluded as a dependency. But note that this documentation reflects the latest\nversion of the `gradio_client`, so upgrade if you\u2019re not sure!\n\nThe lightweight `gradio_client` package can be installed from pip (or pip3)\nand is tested to work with **Python versions 3.9 or higher** :\n\n    \n    \n    $ pip install --upgrade gradio_client\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Spaces\n\nStart by connecting instantiating a `Client` object and connecting it to a\nGradio app that is running on Hugging Face Spaces.\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/en2fr\")  a Space that translates from English to French\n\nYou can also connect to private Spaces by passing in your HF token with the\n`hf_token` parameter. You can get your HF token here:\n<https://huggingface.co/settings/tokens>\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/my-private-space\", hf_token=\"...\")\n\n", "heading1": "Connecting to a Gradio App on Hugging Face", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "use\n\nWhile you can use any public Space as an API, you may get rate limited by\nHugging Face if you make too many requests. For unlimited usage of a Space,\nsimply duplicate the Space to create a private Space, and then use it to make\nas many requests as you\u2019d like!\n\nThe `gradio_client` includes a class method: `Client.duplicate()` to make this\nprocess simple (you\u2019ll need to pass in your [Hugging Face\ntoken](https://huggingface.co/settings/tokens) or be logged in using the\nHugging Face CLI):\n\n    \n    \n    import os\n    from gradio_client import Client, file\n    \n    HF_TOKEN = os.environ.get(\"HF_TOKEN\")\n    \n    client = Client.duplicate(\"abidlabs/whisper\", hf_token=HF_TOKEN)\n    client.predict(file(\"audio_sample.wav\"))\n    \n    >> \"This is a test of the whisper speech recognition model.\"\n\nIf you have previously duplicated a Space, re-running `duplicate()` will _not_\ncreate a new Space. Instead, the Client will attach to the previously-created\nSpace. So it is safe to re-run the `Client.duplicate()` method multiple times.\n\n**Note:** if the original Space uses GPUs, your private Space will as well,\nand your Hugging Face account will get billed based on the price of the GPU.\nTo minimize charges, your Space will automatically go to sleep after 1 hour of\ninactivity. You can also set the hardware using the `hardware` parameter of\n`duplicate()`.\n\n", "heading1": "Duplicating a Space for private", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "app\n\nIf your app is running somewhere else, just provide the full URL instead,\nincluding the \u201chttp://\u201d or \u201chttps://\u201c. Here\u2019s an example of making predictions\nto a Gradio app that is running on a share URL:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"https://bec81a83-5b5c-471e.gradio.live\")\n\n", "heading1": "Connecting a general Gradio", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are\navailable to you by calling the `Client.view_api()` method. For the Whisper\nSpace, we see the following:\n\n    \n    \n    Client.predict() Usage Info\n    ---------------------------\n    Named API endpoints: 1\n    \n     - predict(audio, api_name=\"/predict\") -> output\n        Parameters:\n         - [Audio] audio: filepath (required)  \n        Returns:\n         - [Textbox] output: str \n\nWe see that we have 1 API endpoint in this space, and shows us how to use the\nAPI endpoint to make a prediction: we should call the `.predict()` method\n(which we will explore below), providing a parameter `input_audio` of type\n`str`, which is a `filepath or URL`.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()`\nmethod. Although this isn\u2019t necessary if a Gradio app has only 1 named\nendpoint, it does allow us to call different endpoints in a single app if they\nare available.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the\n\u201cUse via API\u201d link in the footer of the Gradio app, which shows us the same\ninformation, along with example usage.\n\n![](https://huggingface.co/datasets/huggingface/documentation-\nimages/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \u201cAPI Recorder\u201d that lets you interact with\nthe Gradio UI normally and converts your interactions into the corresponding\ncode to run with the Python Client.\n\n", "heading1": "The \u201cView API\u201d Page", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()`\nfunction with the appropriate arguments:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/en2fr\", api_name='/predict')\n    client.predict(\"Hello\")\n    \n    >> Bonjour\n\nIf there are multiple parameters, then you should pass them as separate\narguments to `.predict()`, like this:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"gradio/calculator\")\n    client.predict(4, \"add\", 5)\n    \n    >> 9.0\n\nIt is recommended to provide key-word arguments instead of positional\narguments:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"gradio/calculator\")\n    client.predict(num1=4, operation=\"add\", num2=5)\n    \n    >> 9.0\n\nThis allows you to take advantage of default arguments. For example, this\nSpace includes the default value for the Slider component so you do not need\nto provide it when accessing it with the client.\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/image_generator\")\n    client.predict(text=\"an astronaut riding a camel\")\n\nThe default value is the initial value of the corresponding Gradio component.\nIf the component does not have an initial value, but if the corresponding\nargument in the predict function has a default value of `None`, then that\nparameter is also optional in the client. Of course, if you\u2019d like to override\nit, you can include it as well:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/image_generator\")\n    client.predict(text=\"an astronaut riding a camel\", steps=25)\n\nFor providing files or URLs as inputs, you should pass in the filepath or URL\nto the file enclosed within `gradio_client.file()`. This takes care of\nuploading the file to the Gradio server and ensures that the file is\npreprocessed correctly:\n\n    \n    \n    from gradio_client import Client, file\n    \n    client = Client(\"abidlabs/whisper\")\n    client.predict(\n      ", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": " to the Gradio server and ensures that the file is\npreprocessed correctly:\n\n    \n    \n    from gradio_client import Client, file\n    \n    client = Client(\"abidlabs/whisper\")\n    client.predict(\n        audio=file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\")\n    )\n    \n    >> \"My thought I have nobody by a beauty and will as you poured. Mr. Rochester is serve in that so don't find simpus, and devoted abode, to at might in a r\u2014\"\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Oe should note that `.predict()` is a _blocking_ operation as it waits for the\noperation to complete before returning the prediction.\n\nIn many cases, you may be better off letting the job run in the background\nuntil you need the results of the prediction. You can do this by creating a\n`Job` instance using the `.submit()` method, and then later calling\n`.result()` on the job to get the result. For example:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(space=\"abidlabs/en2fr\")\n    job = client.submit(\"Hello\", api_name=\"/predict\")  This is not blocking\n    \n    Do something else\n    \n    job.result()  This is blocking\n    \n    >> Bonjour\n\n", "heading1": "Running jobs asynchronously", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Alternatively, one can add one or more callbacks to perform actions after the\njob has completed running, like this:\n\n    \n    \n    from gradio_client import Client\n    \n    def print_result(x):\n        print(\"The translated result is: {x}\")\n    \n    client = Client(space=\"abidlabs/en2fr\")\n    \n    job = client.submit(\"Hello\", api_name=\"/predict\", result_callbacks=[print_result])\n    \n    Do something else\n    \n    >> The translated result is: Bonjour\n    \n\n", "heading1": "Adding callbacks", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The `Job` object also allows you to get the status of the running job by\ncalling the `.status()` method. This returns a `StatusUpdate` object with the\nfollowing attributes: `code` (the status code, one of a set of defined strings\nrepresenting the status. See the `utils.Status` class), `rank` (the current\nposition of this job in the queue), `queue_size` (the total queue size), `eta`\n(estimated time this job will complete), `success` (a boolean representing\nwhether the job completed successfully), and `time` (the time that the status\nwas generated).\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(src=\"gradio/calculator\")\n    job = client.submit(5, \"add\", 4, api_name=\"/predict\")\n    job.status()\n    \n    >> <Status.STARTING: 'STARTING'>\n\n_Note_ : The `Job` class also has a `.done()` instance method which returns a\nboolean indicating whether the job has completed.\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The `Job` class also has a `.cancel()` instance method that cancels jobs that\nhave been queued but not started. For example, if you run:\n\n    \n    \n    client = Client(\"abidlabs/whisper\")\n    job1 = client.submit(file(\"audio_sample1.wav\"))\n    job2 = client.submit(file(\"audio_sample2.wav\"))\n    job1.cancel()  will return False, assuming the job has started\n    job2.cancel()  will return True, indicating that the job has been canceled\n\nIf the first job has started processing, then it will not be canceled. If the\nsecond job has not yet started, it will be successfully canceled and removed\nfrom the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a\nseries of values. You can get the series of values that have been returned at\nany time from such a generator endpoint by running `job.outputs()`:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(src=\"gradio/count_generator\")\n    job = client.submit(3, api_name=\"/count\")\n    while not job.done():\n        time.sleep(0.1)\n    job.outputs()\n    \n    >> ['0', '1', '2']\n\nNote that running `job.result()` on a generator endpoint only gives you the\n_first_ value returned by the endpoint.\n\nThe `Job` object is also iterable, which means you can use it to display the\nresults of a generator function as they are returned from the endpoint. Here\u2019s\nthe equivalent example using the `Job` as a generator:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(src=\"gradio/count_generator\")\n    job = client.submit(3, api_name=\"/count\")\n    \n    for o in job:\n        print(o)\n    \n    >> 0\n    >> 1\n    >> 2\n\nYou can also cancel jobs that that have iterative outputs, in which case the\njob will finish as soon as the current iteration finishes running.\n\n    \n    \n    from gradio_client import Client\n    import time\n    \n    client = Client(\"abidlabs/test-yield\")\n    job = client.submit(\"abcdef\")\n    time.sleep(3)\n    job.cancel()  job cancels after 2 iterations\n\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Gradio demos can include [session state](https://www.gradio.app/guides/state-\nin-blocks), which provides a way for demos to persist information from user\ninteractions within a page session.\n\nFor example, consider the following demo, which maintains a list of words that\na user has submitted in a `gr.State` component. When a user submits a new\nword, it is added to the state, and the number of previous occurrences of that\nword is displayed:\n\n    \n    \n    import gradio as gr\n    \n    def count(word, list_of_words):\n        return list_of_words.count(word), list_of_words + [word]\n    \n    with gr.Blocks() as demo:\n        words = gr.State([])\n        textbox = gr.Textbox()\n        number = gr.Number()\n        textbox.submit(count, inputs=[textbox, words], outputs=[number, words])\n        \n    demo.launch()\n\nIf you were to connect this this Gradio app using the Python Client, you would\nnotice that the API information only shows a single input and output:\n\n    \n    \n    Client.predict() Usage Info\n    ---------------------------\n    Named API endpoints: 1\n    \n     - predict(word, api_name=\"/count\") -> value_31\n        Parameters:\n         - [Textbox] word: str (required)  \n        Returns:\n         - [Number] value_31: float \n\nThat is because the Python client handles state automatically for you \u2014 as you\nmake a series of requests, the returned state from one request is stored\ninternally and automatically supplied for the subsequent request. If you\u2019d\nlike to reset the state, you can do that by calling `Client.reset_session()`.\n\n", "heading1": "Demos with Session State", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The main Client class for the Python client. This class is used to connect\nto a remote Gradio app and call its API endpoints.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "from gradio_client import Client\n    \n    client = Client(\"abidlabs/whisper-large-v2\")  connecting to a Hugging Face Space\n    client.predict(\"test.mp4\", api_name=\"/predict\")\n    >> What a nice recording! returns the result of the remote API call\n    \n    client = Client(\"https://bec81a83-5b5c-471e.gradio.live\")  connecting to a temporary Gradio share URL\n    job = client.submit(\"hello\", api_name=\"/predict\")  runs the prediction in a background thread\n    job.result()\n    >> 49 returns the result of the remote API call (blocking call)\n\n", "heading1": "Example usage", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    src: str\n\neither the name of the Hugging Face Space to load, (e.g. \"abidlabs/whisper-\nlarge-v2\") or the full URL (including \"http\" or \"https\") of the hosted Gradio\napp to load (e.g. \"http://mydomain.com/app\" or\n\"https://bec81a83-5b5c-471e.gradio.live/\").\n\n\n    \n    \n    hf_token: str | Literal[False] | None\n\ndefault `= False`\n\noptional Hugging Face token to use to access private Spaces. By default, no\ntoken is sent to the server. Set `hf_token=None` to use the locally saved\ntoken if there is one (warning: only provide a token if you are loading a\ntrusted private Space as the token can be read by the Space you are loading).\nFind your tokens here: https://huggingface.co/settings/tokens.\n\n\n    \n    \n    max_workers: int\n\ndefault `= 40`\n\nmaximum number of thread workers that can be used to make requests to the\nremote Gradio app simultaneously.\n\n\n    \n    \n    verbose: bool\n\ndefault `= True`\n\nwhether the client should print statements to the console.\n\n\n    \n    \n    auth: tuple[str, str] | None\n\ndefault `= None`\n\n\n    \n    \n    httpx_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nadditional keyword arguments to pass to `httpx.Client`, `httpx.stream`,\n`httpx.get` and `httpx.post`. This can be used to set timeouts, proxies, http\nauth, etc.\n\n\n    \n    \n    headers: dict[str, str] | None\n\ndefault `= None`\n\nadditional headers to send to the remote Gradio app on every request. By\ndefault only the HF authorization and user-agent headers are sent. This\nparameter will override the default headers if they have the same keys.\n\n\n    \n    \n    download_files: str | Path | Literal[False]\n\ndefault `= \"/tmp/gradio\"`\n\ndirectory where the client should download output files on the local machine\nfrom the remote API. By default, uses the value of the GRADIO_TEMP_DIR\nenvironment variable which, if not set by the user, is a temporary directory\non your machine. If False, the client does not download files and returns a\nFileData dataclass object with the filepath ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "IR\nenvironment variable which, if not set by the user, is a temporary directory\non your machine. If False, the client does not download files and returns a\nFileData dataclass object with the filepath on the remote machine instead.\n\n\n    \n    \n    ssl_verify: bool\n\ndefault `= True`\n\nif False, skips certificate validation which allows the client to connect to\nGradio apps that are using self-signed certificates.\n\n\n    \n    \n    analytics_enabled: bool\n\ndefault `= True`\n\nWhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable or default to True.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Client component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Client.predict(fn, \u00b7\u00b7\u00b7)` | Calls the Gradio API and returns the result (this is a blocking call). Arguments can be provided as positional arguments or as keyword arguments (latter is recommended). <br>  \n`Client.submit(fn, \u00b7\u00b7\u00b7)` | Creates and returns a Job object which calls the Gradio API in a background thread. The job can be used to retrieve the status and result of the remote API call. Arguments can be provided as positional arguments or as keyword arguments (latter is recommended). <br>  \n`Client.view_api(fn, \u00b7\u00b7\u00b7)` | Prints the usage info for the API. If the Gradio app has multiple API endpoints, the usage info for each endpoint will be printed separately. If return_format=\"dict\" the info is returned in dictionary format, as shown in the example below. <br>  \n`Client.duplicate(fn, \u00b7\u00b7\u00b7)` | Duplicates a Hugging Face Space under your account and returns a Client object for the new Space. No duplication is created if the Space already exists in your account (to override this, provide a new name for the new Space using `to_id`). To use this method, you must provide an `hf_token` or be logged in via the Hugging Face Hub CLI. <br> The new Space will be private by default and use the same hardware as the original Space. This can be changed by using the `private` and `hardware` parameters. For hardware upgrades (beyond the basic CPU tier), you may be required to provide billing information on Hugging Face: https://huggingface.co/settings/billing <br>  \n`Client.deploy_discord(fn, \u00b7\u00b7\u00b7)` | Deploy", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "dware upgrades (beyond the basic CPU tier), you may be required to provide billing information on Hugging Face: https://huggingface.co/settings/billing <br>  \n`Client.deploy_discord(fn, \u00b7\u00b7\u00b7)` | Deploy the upstream app as a discord bot. Currently only supports gr.ChatInterface.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    args: <class 'inspect._empty'>\n\nThe positional arguments to pass to the remote API endpoint. The order of the\narguments must match the order of the inputs in the Gradio app.\n\n\n    \n    \n    api_name: str | None\n\ndefault `= None`\n\nThe name of the API endpoint to call starting with a leading slash, e.g.\n\"/predict\". Does not need to be provided if the Gradio app has only one named\nAPI endpoint.\n\n\n    \n    \n    fn_index: int | None\n\ndefault `= None`\n\nAs an alternative to api_name, this parameter takes the index of the API\nendpoint to call, e.g. 0. Both api_name and fn_index can be provided, but if\nthey conflict, api_name will take precedence.\n\n\n    \n    \n    headers: dict[str, str] | None\n\ndefault `= None`\n\nAdditional headers to send to the remote Gradio app on this request. This\nparameter will overrides the headers provided in the Client constructor if\nthey have the same keys.\n\n\n    \n    \n    kwargs: <class 'inspect._empty'>\n\nThe keyword arguments to pass to the remote API endpoint.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "`gradio-rs` is a Gradio Client in Rust built by\n[@JacobLinCool](https://github.com/JacobLinCool). You can find the repo\n[here](https://github.com/JacobLinCool/gradio-rs), and more in depth API\ndocumentation [here](https://docs.rs/gradio/latest/gradio/).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/docs/third-party-clients/rust-client", "source_page_title": "Third Party Clients - Rust Client Docs"}, {"text": "Here is an example of using BS-RoFormer model to separate vocals and\nbackground music from an audio file.\n\n    \n    \n    use gradio::{PredictionInput, Client, ClientOptions};\n    \n    [tokio::main]\n    async fn main() {\n        if std::env::args().len() < 2 {\n            println!(\"Please provide an audio file path as an argument\");\n            std::process::exit(1);\n        }\n        let args: Vec<String> = std::env::args().collect();\n        let file_path = &args[1];\n        println!(\"File: {}\", file_path);\n    \n        let client = Client::new(\"JacobLinCool/vocal-separation\", ClientOptions::default())\n            .await\n            .unwrap();\n    \n        let output = client\n            .predict(\n                \"/separate\",\n                vec![\n                    PredictionInput::from_file(file_path),\n                    PredictionInput::from_value(\"BS-RoFormer\"),\n                ],\n            )\n            .await\n            .unwrap();\n        println!(\n            \"Vocals: {}\",\n            output[0].clone().as_file().unwrap().url.unwrap()\n        );\n        println!(\n            \"Background: {}\",\n            output[1].clone().as_file().unwrap().url.unwrap()\n        );\n    }\n\nYou can find more examples [here](https://github.com/JacobLinCool/gradio-\nrs/tree/main/examples).\n\n", "heading1": "Usage", "source_page_url": "https://gradio.app/docs/third-party-clients/rust-client", "source_page_title": "Third Party Clients - Rust Client Docs"}, {"text": "cargo install gradio\n    gr --help\n\nTake [stabilityai/stable-\ndiffusion-3-medium](https://huggingface.co/spaces/stabilityai/stable-\ndiffusion-3-medium) HF Space as an example:\n\n    \n    \n    > gr list stabilityai/stable-diffusion-3-medium\n    API Spec for stabilityai/stable-diffusion-3-medium:\n            /infer\n                    Parameters:\n                            prompt               ( str      ) \n                            negative_prompt      ( str      ) \n                            seed                 ( float    ) numeric value between 0 and 2147483647\n                            randomize_seed       ( bool     ) \n                            width                ( float    ) numeric value between 256 and 1344\n                            height               ( float    ) numeric value between 256 and 1344\n                            guidance_scale       ( float    ) numeric value between 0.0 and 10.0\n                            num_inference_steps  ( float    ) numeric value between 1 and 50\n                    Returns:\n                            Result               ( filepath ) \n                            Seed                 ( float    ) numeric value between 0 and 2147483647\n    \n    > gr run stabilityai/stable-diffusion-3-medium infer 'Rusty text \"AI & CLI\" on the snow.' '' 0 true 1024 1024 5 28\n    Result: https://stabilityai-stable-diffusion-3-medium.hf.space/file=/tmp/gradio/5735ca7775e05f8d56d929d8f57b099a675c0a01/image.webp\n    Seed: 486085626\n\nFor file input, simply use the file path as the argument:\n\n    \n    \n    gr run hf-audio/whisper-large-v3 predict 'test-audio.wav' 'transcribe'\n    output: \" Did you know you can try the coolest model on your command line?\"\n\n", "heading1": "Command Line Interface", "source_page_url": "https://gradio.app/docs/third-party-clients/rust-client", "source_page_title": "Third Party Clients - Rust Client Docs"}, {"text": "Gradio applications support programmatic requests from many environments:\n\n  * The [Python Client](/docs/python-client): `gradio-client` allows you to make requests from Python environments.\n  * The [JavaScript Client](/docs/js-client): `@gradio/client` allows you to make requests in TypeScript from the browser or server-side.\n  * You can also query gradio apps [directly from cURL](/guides/querying-gradio-apps-with-curl).\n\n", "heading1": "Gradio Clients", "source_page_url": "https://gradio.app/docs/third-party-clients/introduction", "source_page_title": "Third Party Clients - Introduction Docs"}, {"text": "We also encourage the development and use of third party clients built by\nthe community:\n\n  * [Rust Client](/docs/third-party-clients/rust-client): `gradio-rs` built by [@JacobLinCool](https://github.com/JacobLinCool) allows you to make requests in Rust.\n  * [Powershell Client](https://github.com/rrg92/powershai): `powershai` built by [@rrg92](https://github.com/rrg92) allows you to make requests to Gradio apps directly from Powershell. See [here for documentation](https://github.com/rrg92/powershai/blob/main/docs/en-US/providers/HUGGING-FACE.md)\n\n", "heading1": "Community Clients", "source_page_url": "https://gradio.app/docs/third-party-clients/introduction", "source_page_title": "Third Party Clients - Introduction Docs"}, {"text": "The frontend code should have, at minimum, three files:\n\n* `Index.svelte`: This is the main export and where your component's layout and logic should live.\n* `Example.svelte`: This is where the example view of the component is defined.\n\nFeel free to add additional files and subdirectories. \nIf you want to export any additional modules, remember to modify the `package.json` file\n\n```json\n\"exports\": {\n    \".\": \"./Index.svelte\",\n    \"./example\": \"./Example.svelte\",\n    \"./package.json\": \"./package.json\"\n},\n```\n\n", "heading1": "The directory structure", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "Your component should expose the following props that will be passed down from the parent Gradio application.\n\n```typescript\nimport type { LoadingStatus } from \"@gradio/statustracker\";\nimport type { Gradio } from \"@gradio/utils\";\n\nexport let gradio: Gradio<{\n    event_1: never;\n    event_2: never;\n}>;\n\nexport let elem_id = \"\";\nexport let elem_classes: string[] = [];\nexport let scale: number | null = null;\nexport let min_width: number | undefined = undefined;\nexport let loading_status: LoadingStatus | undefined = undefined;\nexport let mode: \"static\" | \"interactive\";\n```\n\n* `elem_id` and `elem_classes` allow Gradio app developers to target your component with custom CSS and JavaScript from the Python `Blocks` class.\n\n* `scale` and `min_width` allow Gradio app developers to control how much space your component takes up in the UI.\n\n* `loading_status` is used to display a loading status over the component when it is the output of an event.\n\n* `mode` is how the parent Gradio app tells your component whether the `interactive` or `static` version should be displayed.\n\n* `gradio`: The `gradio` object is created by the parent Gradio app. It stores some application-level configuration that will be useful in your component, like internationalization. You must use it to dispatch events from your component.\n\nA minimal `Index.svelte` file would look like:\n\n```svelte\n<script lang=\"ts\">\n\timport type { LoadingStatus } from \"@gradio/statustracker\";\n    import { Block } from \"@gradio/atoms\";\n\timport { StatusTracker } from \"@gradio/statustracker\";\n\timport type { Gradio } from \"@gradio/utils\";\n\n\texport let gradio: Gradio<{\n\t\tevent_1: never;\n\t\tevent_2: never;\n\t}>;\n\n    export let value = \"\";\n\texport let elem_id = \"\";\n\texport let elem_classes: string[] = [];\n\texport let scale: number | null = null;\n\texport let min_width: number | undefined = undefined;\n\texport let loading_status: LoadingStatus | undefined = undefined;\n    export let mode: \"static\" | \"interactive\";\n</script>\n\n<Block\n\tvisib", "heading1": "The Index.svelte file", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": " null;\n\texport let min_width: number | undefined = undefined;\n\texport let loading_status: LoadingStatus | undefined = undefined;\n    export let mode: \"static\" | \"interactive\";\n</script>\n\n<Block\n\tvisible={true}\n\t{elem_id}\n\t{elem_classes}\n\t{scale}\n\t{min_width}\n\tallow_overflow={false}\n\tpadding={true}\n>\n\t{if loading_status}\n\t\t<StatusTracker\n\t\t\tautoscroll={gradio.autoscroll}\n\t\t\ti18n={gradio.i18n}\n\t\t\t{...loading_status}\n\t\t/>\n\t{/if}\n    <p>{value}</p>\n</Block>\n```\n\n", "heading1": "The Index.svelte file", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "The `Example.svelte` file should expose the following props:\n\n```typescript\n    export let value: string;\n    export let type: \"gallery\" | \"table\";\n    export let selected = false;\n    export let index: number;\n```\n\n* `value`: The example value that should be displayed.\n\n* `type`: This is a variable that can be either `\"gallery\"` or `\"table\"` depending on how the examples are displayed. The `\"gallery\"` form is used when the examples correspond to a single input component, while the `\"table\"` form is used when a user has multiple input components, and the examples need to populate all of them. \n\n* `selected`: You can also adjust how the examples are displayed if a user \"selects\" a particular example by using the selected variable.\n\n* `index`: The current index of the selected value.\n\n* Any additional props your \"non-example\" component takes!\n\nThis is the `Example.svelte` file for the code `Radio` component:\n\n```svelte\n<script lang=\"ts\">\n\texport let value: string;\n\texport let type: \"gallery\" | \"table\";\n\texport let selected = false;\n</script>\n\n<div\n\tclass:table={type === \"table\"}\n\tclass:gallery={type === \"gallery\"}\n\tclass:selected\n>\n\t{value}\n</div>\n\n<style>\n\t.gallery {\n\t\tpadding: var(--size-1) var(--size-2);\n\t}\n</style>\n```\n\n", "heading1": "The Example.svelte file", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "If your component deals with files, these files **should** be uploaded to the backend server. \nThe `@gradio/client` npm package provides the `upload` and `prepare_files` utility functions to help you do this.\n\nThe `prepare_files` function will convert the browser's `File` datatype to gradio's internal `FileData` type.\nYou should use the `FileData` data in your component to keep track of uploaded files.\n\nThe `upload` function will upload an array of `FileData` values to the server.\n\nHere's an example of loading files from an `<input>` element when its value changes.\n\n\n```svelte\n<script lang=\"ts\">\n    import { upload, prepare_files, type FileData } from \"@gradio/client\";\n    export let root;\n    export let value;\n    let uploaded_files;\n\n    async function handle_upload(file_data: FileData[]): Promise<void> {\n        await tick();\n        uploaded_files = await upload(file_data, root);\n    }\n\n    async function loadFiles(files: FileList): Promise<void> {\n        let _files: File[] = Array.from(files);\n        if (!files.length) {\n            return;\n        }\n        if (file_count === \"single\") {\n            _files = [files[0]];\n        }\n        let file_data = await prepare_files(_files);\n        await handle_upload(file_data);\n    }\n\n    async function loadFilesFromUpload(e: Event): Promise<void> {\n\t\tconst target = e.target;\n\n\t\tif (!target.files) return;\n\t\tawait loadFiles(target.files);\n\t}\n</script>\n\n<input\n    type=\"file\"\n    on:change={loadFilesFromUpload}\n    multiple={true}\n/>\n```\n\nThe component exposes a prop named `root`. \nThis is passed down by the parent gradio app and it represents the base url that the files will be uploaded to and fetched from.\n\nFor WASM support, you should get the upload function from the `Context` and pass that as the third parameter of the `upload` function.\n\n```typescript\n<script lang=\"ts\">\n    import { getContext } from \"svelte\";\n    const upload_fn = getContext<typeof upload_files>(\"upload_files\");\n\n    async function handle_uploa", "heading1": "Handling Files", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "he `upload` function.\n\n```typescript\n<script lang=\"ts\">\n    import { getContext } from \"svelte\";\n    const upload_fn = getContext<typeof upload_files>(\"upload_files\");\n\n    async function handle_upload(file_data: FileData[]): Promise<void> {\n        await tick();\n        await upload(file_data, root, upload_fn);\n    }\n</script>\n```\n\n", "heading1": "Handling Files", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "Most of Gradio's frontend components are published on [npm](https://www.npmjs.com/), the javascript package repository.\nThis means that you can use them to save yourself time while incorporating common patterns in your component, like uploading files.\nFor example, the `@gradio/upload` package has `Upload` and `ModifyUpload` components for properly uploading files to the Gradio server. \nHere is how you can use them to create a user interface to upload and display PDF files.\n\n```svelte\n<script>\n\timport { type FileData, Upload, ModifyUpload } from \"@gradio/upload\";\n\timport { Empty, UploadText, BlockLabel } from \"@gradio/atoms\";\n</script>\n\n<BlockLabel Icon={File} label={label || \"PDF\"} />\n{if value === null && interactive}\n    <Upload\n        filetype=\"application/pdf\"\n        on:load={handle_load}\n        {root}\n        >\n        <UploadText type=\"file\" i18n={gradio.i18n} />\n    </Upload>\n{:else if value !== null}\n    {if interactive}\n        <ModifyUpload i18n={gradio.i18n} on:clear={handle_clear}/>\n    {/if}\n    <iframe title={value.orig_name || \"PDF\"} src={value.data} height=\"{height}px\" width=\"100%\"></iframe>\n{:else}\n    <Empty size=\"large\"> <File/> </Empty>\t\n{/if}\n```\n\nYou can also combine existing Gradio components to create entirely unique experiences.\nLike rendering a gallery of chatbot conversations. \nThe possibilities are endless, please read the documentation on our javascript packages [here](https://gradio.app/main/docs/js).\nWe'll be adding more packages and documentation over the coming weeks!\n\n", "heading1": "Leveraging Existing Gradio Components", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "You can explore our component library via Storybook. You'll be able to interact with our components and see them in their various states.\n\nFor those interested in design customization, we provide the CSS variables consisting of our color palette, radii, spacing, and the icons we use - so you can easily match up your custom component with the style of our core components. This Storybook will be regularly updated with any new additions or changes.\n\n[Storybook Link](https://gradio.app/main/docs/js/storybook)\n\n", "heading1": "Matching Gradio Core's Design System", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "If you want to make use of the vast vite ecosystem, you can use the `gradio.config.js` file to configure your component's build process. This allows you to make use of tools like tailwindcss, mdsvex, and more.\n\nCurrently, it is possible to configure the following:\n\nVite options:\n- `plugins`: A list of vite plugins to use.\n\nSvelte options:\n- `preprocess`: A list of svelte preprocessors to use.\n- `extensions`: A list of file extensions to compile to `.svelte` files.\n- `build.target`: The target to build for, this may be necessary to support newer javascript features. See the [esbuild docs](https://esbuild.github.io/api/target) for more information.\n\nThe `gradio.config.js` file should be placed in the root of your component's `frontend` directory. A default config file is created for you when you create a new component. But you can also create your own config file, if one doesn't exist, and use it to customize your component's build process.\n\nExample for a Vite plugin\n\nCustom components can use Vite plugins to customize the build process. Check out the [Vite Docs](https://vitejs.dev/guide/using-plugins.html) for more information. \n\nHere we configure [TailwindCSS](https://tailwindcss.com), a utility-first CSS framework. Setup is easiest using the version 4 prerelease. \n\n```\nnpm install tailwindcss@next @tailwindcss/vite@next\n```\n\nIn `gradio.config.js`:\n\n```typescript\nimport tailwindcss from \"@tailwindcss/vite\";\nexport default {\n    plugins: [tailwindcss()]\n};\n```\n\nThen create a `style.css` file with the following content:\n\n```css\n@import \"tailwindcss\";\n```\n\nImport this file into `Index.svelte`. Note, that you need to import the css file containing `@import` and cannot just use a `<style>` tag and use `@import` there. \n\n```svelte\n<script lang=\"ts\">\n[...]\nimport \"./style.css\";\n[...]\n</script>\n```\n\nExample for Svelte options\n\nIn `gradio.config.js` you can also specify a some Svelte options to apply to the Svelte compilation. In this example we will add support for", "heading1": "Custom configuration", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": ".css\";\n[...]\n</script>\n```\n\nExample for Svelte options\n\nIn `gradio.config.js` you can also specify a some Svelte options to apply to the Svelte compilation. In this example we will add support for [`mdsvex`](https://mdsvex.pngwn.io), a Markdown preprocessor for Svelte. \n\nIn order to do this we will need to add a [Svelte Preprocessor](https://svelte.dev/docs/svelte-compilerpreprocess) to the `svelte` object in `gradio.config.js` and configure the [`extensions`](https://github.com/sveltejs/vite-plugin-svelte/blob/HEAD/docs/config.mdconfig-file) field. Other options are not currently supported.\n\nFirst, install the `mdsvex` plugin:\n\n```bash\nnpm install mdsvex\n```\n\nThen add the following to `gradio.config.js`:\n\n```typescript\nimport { mdsvex } from \"mdsvex\";\n\nexport default {\n    svelte: {\n        preprocess: [\n            mdsvex()\n        ],\n        extensions: [\".svelte\", \".svx\"]\n    }\n};\n```\n\nNow we can create `mdsvex` documents in our component's `frontend` directory and they will be compiled to `.svelte` files.\n\n```md\n<!-- HelloWorld.svx -->\n\n<script lang=\"ts\">\n    import { Block } from \"@gradio/atoms\";\n\n    export let title = \"Hello World\";\n</script>\n\n<Block label=\"Hello World\">\n\n{title}\n\nThis is a markdown file.\n\n</Block>\n```\n\nWe can then use the `HelloWorld.svx` file in our components:\n\n```svelte\n<script lang=\"ts\">\n    import HelloWorld from \"./HelloWorld.svx\";\n</script>\n\n<HelloWorld />\n```\n\n", "heading1": "Custom configuration", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "You now know how to create delightful frontends for your components!\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "Every component in Gradio comes in a `static` variant, and most come in an `interactive` version as well.\nThe `static` version is used when a component is displaying a value, and the user can **NOT** change that value by interacting with it. \nThe `interactive` version is used when the user is able to change the value by interacting with the Gradio UI.\n\nLet's see some examples:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n   gr.Textbox(value=\"Hello\", interactive=True)\n   gr.Textbox(value=\"Hello\", interactive=False)\n\ndemo.launch()\n\n```\nThis will display two textboxes.\nThe only difference: you'll be able to edit the value of the Gradio component on top, and you won't be able to edit the variant on the bottom (i.e. the textbox will be disabled).\n\nPerhaps a more interesting example is with the `Image` component:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n   gr.Image(interactive=True)\n   gr.Image(interactive=False)\n\ndemo.launch()\n```\n\nThe interactive version of the component is much more complex -- you can upload images or snap a picture from your webcam -- while the static version can only be used to display images.\n\nNot every component has a distinct interactive version. For example, the `gr.AnnotatedImage` only appears as a static version since there's no way to interactively change the value of the annotations or the image.\n\nWhat you need to remember\n\n* Gradio will use the interactive version (if available) of a component if that component is used as the **input** to any event; otherwise, the static version will be used.\n\n* When you design custom components, you **must** accept the boolean interactive keyword in the constructor of your Python class. In the frontend, you **may** accept the `interactive` property, a `bool` which represents whether the component should be static or interactive. If you do not use this property in the frontend, the component will appear the same in interactive or static mode.\n\n", "heading1": "Interactive vs Static", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": "The most important attribute of a component is its `value`.\nEvery component has a `value`.\nThe value that is typically set by the user in the frontend (if the component is interactive) or displayed to the user (if it is static). \nIt is also this value that is sent to the backend function when a user triggers an event, or returned by the user's function e.g. at the end of a prediction.\n\nSo this value is passed around quite a bit, but sometimes the format of the value needs to change between the frontend and backend. \nTake a look at this example:\n\n```python\nimport numpy as np\nimport gradio as gr\n\ndef sepia(input_img):\n    sepia_filter = np.array([\n        [0.393, 0.769, 0.189], \n        [0.349, 0.686, 0.168], \n        [0.272, 0.534, 0.131]\n    ])\n    sepia_img = input_img.dot(sepia_filter.T)\n    sepia_img /= sepia_img.max()\n    return sepia_img\n\ndemo = gr.Interface(sepia, gr.Image(width=200, height=200), \"image\")\ndemo.launch()\n```\n\nThis will create a Gradio app which has an `Image` component as the input and the output. \nIn the frontend, the Image component will actually **upload** the file to the server and send the **filepath** but this is converted to a `numpy` array before it is sent to a user's function. \nConversely, when the user returns a `numpy` array from their function, the numpy array is converted to a file so that it can be sent to the frontend and displayed by the `Image` component.\n\nTip: By default, the `Image` component sends numpy arrays to the python function because it is a common choice for machine learning engineers, though the Image component also supports other formats using the `type` parameter.  Read the `Image` docs [here](https://www.gradio.app/docs/image) to learn more.\n\nEach component does two conversions:\n\n1. `preprocess`: Converts the `value` from the format sent by the frontend to the format expected by the python function. This usually involves going from a web-friendly **JSON** structure to a **python-native** data structure, like a `n", "heading1": "The value and how it is preprocessed/postprocessed", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": " from the format sent by the frontend to the format expected by the python function. This usually involves going from a web-friendly **JSON** structure to a **python-native** data structure, like a `numpy` array or `PIL` image. The `Audio`, `Image` components are good examples of `preprocess` methods.\n\n2. `postprocess`: Converts the value returned by the python function to the format expected by the frontend. This usually involves going from a **python-native** data-structure, like a `PIL` image to a **JSON** structure.\n\nWhat you need to remember\n\n* Every component must implement `preprocess` and `postprocess` methods. In the rare event that no conversion needs to happen, simply return the value as-is. `Textbox` and `Number` are examples of this. \n\n* As a component author, **YOU** control the format of the data displayed in the frontend as well as the format of the data someone using your component will receive. Think of an ergonomic data-structure a **python** developer will find intuitive, and control the conversion from a **Web-friendly JSON** data structure (and vice-versa) with `preprocess` and `postprocess.`\n\n", "heading1": "The value and how it is preprocessed/postprocessed", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": "Gradio apps support providing example inputs -- and these are very useful in helping users get started using your Gradio app. \nIn `gr.Interface`, you can provide examples using the `examples` keyword, and in `Blocks`, you can provide examples using the special `gr.Examples` component.\n\nAt the bottom of this screenshot, we show a miniature example image of a cheetah that, when clicked, will populate the same image in the input Image component:\n\n![img](https://user-images.githubusercontent.com/1778297/277548211-a3cb2133-2ffc-4cdf-9a83-3e8363b57ea6.png)\n\n\nTo enable the example view, you must have the following two files in the top of the `frontend` directory:\n\n* `Example.svelte`: this corresponds to the \"example version\" of your component\n* `Index.svelte`: this corresponds to the \"regular version\"\n\nIn the backend, you typically don't need to do anything. The user-provided example `value` is processed using the same `.postprocess()` method described earlier. If you'd like to do process the data differently (for example, if the `.postprocess()` method is computationally expensive), then you can write your own `.process_example()` method for your custom component, which will be used instead. \n\nThe `Example.svelte` file and `process_example()` method will be covered in greater depth in the dedicated [frontend](./frontend) and [backend](./backend) guides respectively.\n\nWhat you need to remember\n\n* If you expect your component to be used as input, it is important to define an \"Example\" view.\n* If you don't, Gradio will use a default one but it won't be as informative as it can be!\n\n", "heading1": "The \"Example Version\" of a Component", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": "Now that you know the most important pieces to remember about Gradio components, you can start to design and build your own!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": "You will need to have:\n\n* Python 3.10+ (<a href=\"https://www.python.org/downloads/\" target=\"_blank\">install here</a>)\n* pip 21.3+ (`python -m pip install --upgrade pip`)\n* Node.js 20+ (<a href=\"https://nodejs.dev/en/download/package-manager/\" target=\"_blank\">install here</a>)\n* npm 9+ (<a href=\"https://docs.npmjs.com/downloading-and-installing-node-js-and-npm/\" target=\"_blank\">install here</a>)\n* Gradio 5+ (`pip install --upgrade gradio`)\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "The Custom Components workflow consists of 4 steps: create, dev, build, and publish.\n\n1. create: creates a template for you to start developing a custom component.\n2. dev: launches a development server with a sample app & hot reloading allowing you to easily develop your custom component\n3. build: builds a python package containing to your custom component's Python and JavaScript code -- this makes things official!\n4. publish: uploads your package to [PyPi](https://pypi.org/) and/or a sample app to [HuggingFace Spaces](https://hf.co/spaces).\n\nEach of these steps is done via the Custom Component CLI. You can invoke it with `gradio cc` or `gradio component`\n\nTip: Run `gradio cc --help` to get a help menu of all available commands. There are some commands that are not covered in this guide. You can also append `--help` to any command name to bring up a help page for that command, e.g. `gradio cc create --help`.\n\n", "heading1": "The Workflow", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Bootstrap a new template by running the following in any working directory:\n\n```bash\ngradio cc create MyComponent --template SimpleTextbox\n```\n\nInstead of `MyComponent`, give your component any name.\n\nInstead of `SimpleTextbox`, you can use any Gradio component as a template. `SimpleTextbox` is actually a special component that a stripped-down version of the `Textbox` component that makes it particularly useful when creating your first custom component.\nSome other components that are good if you are starting out: `SimpleDropdown`, `SimpleImage`, or `File`.\n\nTip: Run `gradio cc show` to get a list of available component templates.\n\nThe `create` command will:\n\n1. Create a directory with your component's name in lowercase with the following structure:\n```directory\n- backend/ <- The python code for your custom component\n- frontend/ <- The javascript code for your custom component\n- demo/ <- A sample app using your custom component. Modify this to develop your component!\n- pyproject.toml <- Used to build the package and specify package metadata.\n```\n\n2. Install the component in development mode\n\nEach of the directories will have the code you need to get started developing!\n\n", "heading1": "1. create", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Once you have created your new component, you can start a development server by `entering the directory` and running\n\n```bash\ngradio cc dev\n```\n\nYou'll see several lines that are printed to the console.\nThe most important one is the one that says:\n\n> Frontend Server (Go here): http://localhost:7861/\n\nThe port number might be different for you.\nClick on that link to launch the demo app in hot reload mode.\nNow, you can start making changes to the backend and frontend you'll see the results reflected live in the sample app!\nWe'll go through a real example in a later guide.\n\nTip: You don't have to run dev mode from your custom component directory. The first argument to `dev` mode is the path to the directory. By default it uses the current directory.\n\n", "heading1": "2. dev", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Once you are satisfied with your custom component's implementation, you can `build` it to use it outside of the development server.\n\nFrom your component directory, run:\n\n```bash\ngradio cc build\n```\n\nThis will create a `tar.gz` and `.whl` file in a `dist/` subdirectory.\nIf you or anyone installs that `.whl` file (`pip install <path-to-whl>`) they will be able to use your custom component in any gradio app!\n\nThe `build` command will also generate documentation for your custom component. This takes the form of an interactive space and a static `README.md`. You can disable this by passing `--no-generate-docs`. You can read more about the documentation generator in [the dedicated guide](https://gradio.app/guides/documenting-custom-components).\n\n", "heading1": "3. build", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Right now, your package is only available on a `.whl` file on your computer.\nYou can share that file with the world with the `publish` command!\n\nSimply run the following command from your component directory:\n\n```bash\ngradio cc publish\n```\n\nThis will guide you through the following process:\n\n1. Upload your distribution files to PyPi. This makes it easier to upload the demo to Hugging Face spaces. Otherwise your package must be at a publicly available url. If you decide to upload to PyPi, you will need a PyPI username and password. You can get one [here](https://pypi.org/account/register/).\n2. Upload a demo of your component to hugging face spaces. This is also optional.\n\n\nHere is an example of what publishing looks like:\n\n<video autoplay muted loop>\n  <source src=\"https://gradio-builds.s3.amazonaws.com/assets/text_with_attachments_publish.mov\" type=\"video/mp4\" />\n</video>\n\n\n", "heading1": "4. publish", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Now that you know the high-level workflow of creating custom components, you can go in depth in the next guides!\nAfter reading the guides, check out this [collection](https://huggingface.co/collections/gradio/custom-components-65497a761c5192d981710b12) of custom components on the HuggingFace Hub so you can learn from other's code.\n\nTip: If you want to start off from someone else's custom component see this [guide](./frequently-asked-questionsdo-i-always-need-to-start-my-component-from-scratch).\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Make sure you have gradio 5.0 or higher installed as well as node 20+.\nAs of the time of publication, the latest release is 4.1.1.\nAlso, please read the [Five Minute Tour](./custom-components-in-five-minutes) of custom components and the [Key Concepts](./key-component-concepts) guide before starting.\n\n\n", "heading1": "Step 0: Prerequisites", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "Navigate to a directory of your choosing and run the following command:\n\n```bash\ngradio cc create PDF\n```\n\n\nTip: You should change the name of the component.\nSome of the screenshots assume the component is called `PDF` but the concepts are the same!\n\nThis will create a subdirectory called `pdf` in your current working directory.\nThere are three main subdirectories in `pdf`: `frontend`, `backend`, and `demo`.\nIf you open `pdf` in your code editor, it will look like this:\n\n![directory structure](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/CodeStructure.png)\n\nTip: For this demo we are not templating off a current gradio component. But you can see the list of available templates with `gradio cc show` and then pass the template name to the `--template` option, e.g. `gradio cc create <Name> --template <foo>`\n\n", "heading1": "Step 1: Creating the custom component", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "We're going to use the [pdfjs](https://mozilla.github.io/pdf.js/) javascript library to display the pdfs in the frontend. \nLet's start off by adding it to our frontend project's dependencies, as well as adding a couple of other projects we'll need.\n\nFrom within the `frontend` directory, run `npm install @gradio/client @gradio/upload @gradio/icons @gradio/button` and `npm install --save-dev pdfjs-dist@3.11.174`.\nAlso, let's uninstall the `@zerodevx/svelte-json-view` dependency by running `npm uninstall @zerodevx/svelte-json-view`.\n\nThe complete `package.json` should look like this:\n\n```json\n{\n  \"name\": \"gradio_pdf\",\n  \"version\": \"0.2.0\",\n  \"description\": \"Gradio component for displaying PDFs\",\n  \"type\": \"module\",\n  \"author\": \"\",\n  \"license\": \"ISC\",\n  \"private\": false,\n  \"main_changeset\": true,\n  \"exports\": {\n    \".\": \"./Index.svelte\",\n    \"./example\": \"./Example.svelte\",\n    \"./package.json\": \"./package.json\"\n  },\n  \"devDependencies\": {\n    \"pdfjs-dist\": \"3.11.174\"\n  },\n  \"dependencies\": {\n    \"@gradio/atoms\": \"0.2.0\",\n    \"@gradio/statustracker\": \"0.3.0\",\n    \"@gradio/utils\": \"0.2.0\",\n    \"@gradio/client\": \"0.7.1\",\n    \"@gradio/upload\": \"0.3.2\",\n    \"@gradio/icons\": \"0.2.0\",\n    \"@gradio/button\": \"0.2.3\",\n    \"pdfjs-dist\": \"3.11.174\"\n  }\n}\n```\n\n\nTip: Running `npm install` will install the latest version of the package available. You can install a specific version with `npm install package@<version>`.  You can find all of the gradio javascript package documentation [here](https://www.gradio.app/main/docs/js). It is recommended you use the same versions as me as the API can change.\n\nNavigate to `Index.svelte` and delete mentions of `JSONView`\n\n```ts\nimport { JsonView } from \"@zerodevx/svelte-json-view\";\n```\n\n```svelte\n<JsonView json={value} />\n```\n\n", "heading1": "Step 2: Frontend - modify javascript dependencies", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "Run the `dev` command to launch the development server.\nThis will open the demo in `demo/app.py` in an environment where changes to the `frontend` and `backend` directories will reflect instantaneously in the launched app.\n\nAfter launching the dev server, you should see a link printed to your console that says `Frontend Server (Go here): ... `.\n \n![](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/dev_server_terminal.png)\n\nYou should see the following:\n\n![](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/frontend_start.png)\n\n\nIts not impressive yet but we're ready to start coding!\n\n", "heading1": "Step 3: Frontend - Launching the Dev Server", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "We're going to start off by first writing the skeleton of our frontend and then adding the pdf rendering logic.\nAdd the following imports and expose the following properties to the top of your file in the `<script>` tag.\nYou may get some warnings from your code editor that some props are not used.\nThat's ok.\n\n```ts\n    import { tick } from \"svelte\";\n    import type { Gradio } from \"@gradio/utils\";\n    import { Block, BlockLabel } from \"@gradio/atoms\";\n    import { File } from \"@gradio/icons\";\n    import { StatusTracker } from \"@gradio/statustracker\";\n    import type { LoadingStatus } from \"@gradio/statustracker\";\n    import type { FileData } from \"@gradio/client\";\n    import { Upload, ModifyUpload } from \"@gradio/upload\";\n\n\texport let elem_id = \"\";\n\texport let elem_classes: string[] = [];\n\texport let visible = true;\n\texport let value: FileData | null = null;\n\texport let container = true;\n\texport let scale: number | null = null;\n\texport let root: string;\n\texport let height: number | null = 500;\n\texport let label: string;\n\texport let proxy_url: string;\n\texport let min_width: number | undefined = undefined;\n\texport let loading_status: LoadingStatus;\n\texport let gradio: Gradio<{\n\t\tchange: never;\n\t\tupload: never;\n\t}>;\n\n    let _value = value;\n    let old_value = _value;\n```\n\n\nTip: The `gradio`` object passed in here contains some metadata about the application as well as some utility methods. One of these utilities is a dispatch method. We want to dispatch change and upload events whenever our PDF is changed or updated. This line provides type hints that these are the only events we will be dispatching.\n\nWe want our frontend component to let users upload a PDF document if there isn't one already loaded.\nIf it is loaded, we want to display it underneath a \"clear\" button that lets our users upload a new document. \nWe're going to use the `Upload` and `ModifyUpload` components that come with the `@gradio/upload` package to do this.\nUnderneath the `</script>` tag, delete all ", "heading1": "Step 4: Frontend - The basic skeleton", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "lets our users upload a new document. \nWe're going to use the `Upload` and `ModifyUpload` components that come with the `@gradio/upload` package to do this.\nUnderneath the `</script>` tag, delete all the current code and add the following:\n\n```svelte\n<Block {visible} {elem_id} {elem_classes} {container} {scale} {min_width}>\n    {if loading_status}\n        <StatusTracker\n            autoscroll={gradio.autoscroll}\n            i18n={gradio.i18n}\n            {...loading_status}\n        />\n    {/if}\n    <BlockLabel\n        show_label={label !== null}\n        Icon={File}\n        float={value === null}\n        label={label || \"File\"}\n    />\n    {if _value}\n        <ModifyUpload i18n={gradio.i18n} absolute />\n    {:else}\n        <Upload\n            filetype={\"application/pdf\"}\n            file_count=\"single\"\n            {root}\n        >\n            Upload your PDF\n        </Upload>\n    {/if}\n</Block>\n```\n\nYou should see the following when you navigate to your app after saving your current changes:\n\n![](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/frontend_1.png)\n\n", "heading1": "Step 4: Frontend - The basic skeleton", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "The `Upload your PDF` text looks a bit small and barebones. \nLets customize it!\n\nCreate a new file called `PdfUploadText.svelte` and copy the following code.\nIts creating a new div to display our \"upload text\" with some custom styling.\n\nTip: Notice that we're leveraging Gradio core's existing css variables here: `var(--size-60)` and `var(--body-text-color-subdued)`. This allows our component to work nicely in light mode and dark mode, as well as with Gradio's built-in themes.\n\n\n```svelte\n<script lang=\"ts\">\n\timport { Upload as UploadIcon } from \"@gradio/icons\";\n\texport let hovered = false;\n\n</script>\n\n<div class=\"wrap\">\n\t<span class=\"icon-wrap\" class:hovered><UploadIcon /> </span>\n    Drop PDF\n    <span class=\"or\">- or -</span>\n    Click to Upload\n</div>\n\n<style>\n\t.wrap {\n\t\tdisplay: flex;\n\t\tflex-direction: column;\n\t\tjustify-content: center;\n\t\talign-items: center;\n\t\tmin-height: var(--size-60);\n\t\tcolor: var(--block-label-text-color);\n\t\tline-height: var(--line-md);\n\t\theight: 100%;\n\t\tpadding-top: var(--size-3);\n\t}\n\n\t.or {\n\t\tcolor: var(--body-text-color-subdued);\n\t\tdisplay: flex;\n\t}\n\n\t.icon-wrap {\n\t\twidth: 30px;\n\t\tmargin-bottom: var(--spacing-lg);\n\t}\n\n\t@media (--screen-md) {\n\t\t.wrap {\n\t\t\tfont-size: var(--text-lg);\n\t\t}\n\t}\n\n\t.hovered {\n\t\tcolor: var(--color-accent);\n\t}\n</style>\n```\n\nNow import `PdfUploadText.svelte` in your `<script>` and pass it to the `Upload` component!\n\n```svelte\n\timport PdfUploadText from \"./PdfUploadText.svelte\";\n\n...\n\n    <Upload\n        filetype={\"application/pdf\"}\n        file_count=\"single\"\n        {root}\n    >\n        <PdfUploadText />\n    </Upload>\n```\n\nAfter saving your code, the frontend should now look like this:\n\n![](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/better_upload.png)\n\n", "heading1": "Step 5: Frontend - Nicer Upload Text", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "This is the most advanced javascript part.\nIt took me a while to figure it out!\nDo not worry if you have trouble, the important thing is to not be discouraged \ud83d\udcaa\nAsk for help in the gradio [discord](https://discord.gg/hugging-face-879548962464493619) if you need and ask for help.\n\nWith that out of the way, let's start off by importing `pdfjs` and loading the code of the pdf worker from the mozilla cdn.\n\n```ts\n\timport pdfjsLib from \"pdfjs-dist\";\n    ...\n    pdfjsLib.GlobalWorkerOptions.workerSrc =  \"https://cdn.bootcss.com/pdf.js/3.11.174/pdf.worker.js\";\n```\n\nAlso create the following variables:\n\n```ts\n    let pdfDoc;\n    let numPages = 1;\n    let currentPage = 1;\n    let canvasRef;\n```\n\nNow, we will use `pdfjs` to render a given page of the PDF onto an `html` document.\nAdd the following code to `Index.svelte`:\n\n```ts\n    async function get_doc(value: FileData) {\n        const loadingTask = pdfjsLib.getDocument(value.url);\n        pdfDoc = await loadingTask.promise;\n        numPages = pdfDoc.numPages;\n        render_page();\n    }\n\n    function render_page() {\n    // Render a specific page of the PDF onto the canvas\n        pdfDoc.getPage(currentPage).then(page => {\n            const ctx  = canvasRef.getContext('2d')\n            ctx.clearRect(0, 0, canvasRef.width, canvasRef.height);\n            let viewport = page.getViewport({ scale: 1 });\n            let scale = height / viewport.height;\n            viewport = page.getViewport({ scale: scale });\n\n            const renderContext = {\n                canvasContext: ctx,\n                viewport,\n            };\n            canvasRef.width = viewport.width;\n            canvasRef.height = viewport.height;\n            page.render(renderContext);\n        });\n    }\n\n    // If the value changes, render the PDF of the currentPage\n    $: if(JSON.stringify(old_value) != JSON.stringify(_value)) {\n        if (_value){\n            get_doc(_value);\n        }\n        old_value = _value;\n        gradio.dispatch(\"change\");\n    }\n```\n\n\n", "heading1": "Step 6: PDF Rendering logic", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "    $: if(JSON.stringify(old_value) != JSON.stringify(_value)) {\n        if (_value){\n            get_doc(_value);\n        }\n        old_value = _value;\n        gradio.dispatch(\"change\");\n    }\n```\n\n\nTip: The `$:` syntax in svelte is how you declare statements to be reactive. Whenever any of the inputs of the statement change, svelte will automatically re-run that statement.\n\nNow place the `canvas` underneath the `ModifyUpload` component:\n\n```svelte\n<div class=\"pdf-canvas\" style=\"height: {height}px\">\n    <canvas bind:this={canvasRef}></canvas>\n</div>\n```\n\nAnd add the following styles to the `<style>` tag:\n\n```svelte\n<style>\n    .pdf-canvas {\n        display: flex;\n        justify-content: center;\n        align-items: center;\n    }\n</style>\n```\n\n", "heading1": "Step 6: PDF Rendering logic", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "Now for the fun part - actually rendering the PDF when the file is uploaded!\nAdd the following functions to the `<script>` tag:\n\n```ts\n    async function handle_clear() {\n        _value = null;\n        await tick();\n        gradio.dispatch(\"change\");\n    }\n\n    async function handle_upload({detail}: CustomEvent<FileData>): Promise<void> {\n        value = detail;\n        await tick();\n        gradio.dispatch(\"change\");\n        gradio.dispatch(\"upload\");\n    }\n```\n\n\nTip: The `gradio.dispatch` method is actually what is triggering the `change` or `upload` events in the backend. For every event defined in the component's backend, we will explain how to do this in Step 9, there must be at least one `gradio.dispatch(\"<event-name>\")` call. These are called `gradio` events and they can be listended from the entire Gradio application. You can dispatch a built-in `svelte` event with the `dispatch` function. These events can only be listened to from the component's direct parent. Learn about svelte events from the [official documentation](https://learn.svelte.dev/tutorial/component-events).\n\nNow we will run these functions whenever the `Upload` component uploads a file and whenever the `ModifyUpload` component clears the current file. The `<Upload>` component dispatches a `load` event with a payload of type `FileData` corresponding to the uploaded file. The `on:load` syntax tells `Svelte` to automatically run this function in response to the event.\n\n```svelte\n    <ModifyUpload i18n={gradio.i18n} on:clear={handle_clear} absolute />\n    \n    ...\n    \n    <Upload\n        on:load={handle_upload}\n        filetype={\"application/pdf\"}\n        file_count=\"single\"\n        {root}\n    >\n        <PdfUploadText/>\n    </Upload>\n```\n\nCongratulations! You have a working pdf uploader!\n\n![upload-gif](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/pdf_component_gif_docs.gif)\n\n", "heading1": "Step 7: Handling The File Upload And Clear", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "If a user uploads a PDF document with multiple pages, they will only be able to see the first one.\nLet's add some buttons to help them navigate the page.\nWe will use the `BaseButton` from `@gradio/button` so that they look like regular Gradio buttons.\n\nImport the `BaseButton` and add the following functions that will render the next and previous page of the PDF.\n\n```ts\n    import { BaseButton } from \"@gradio/button\";\n\n    ...\n\n    function next_page() {\n        if (currentPage >= numPages) {\n            return;\n        }\n        currentPage++;\n        render_page();\n    }\n\n    function prev_page() {\n        if (currentPage == 1) {\n            return;\n        }\n        currentPage--;\n        render_page();\n    }\n```\n\nNow we will add them underneath the canvas in a separate `<div>`\n\n```svelte\n    ...\n\n    <ModifyUpload i18n={gradio.i18n} on:clear={handle_clear} absolute />\n    <div class=\"pdf-canvas\" style=\"height: {height}px\">\n        <canvas bind:this={canvasRef}></canvas>\n    </div>\n    <div class=\"button-row\">\n        <BaseButton on:click={prev_page}>\n            \u2b05\ufe0f\n        </BaseButton>\n        <span class=\"page-count\"> {currentPage} / {numPages} </span>\n        <BaseButton on:click={next_page}>\n            \u27a1\ufe0f\n        </BaseButton>\n    </div>\n    \n    ...\n\n<style>\n    .button-row {\n        display: flex;\n        flex-direction: row;\n        width: 100%;\n        justify-content: center;\n        align-items: center;\n    }\n\n    .page-count {\n        margin: 0 10px;\n        font-family: var(--font-mono);\n    }\n```\n\nCongratulations! The frontend is almost complete \ud83c\udf89\n\n![multipage-pdf-gif](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/pdf_multipage.gif)\n\n", "heading1": "Step 8: Adding buttons to navigate pages", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "We're going to want users of our component to get a preview of the PDF if its used as an `example` in a `gr.Interface` or `gr.Examples`.\n\nTo do so, we're going to add some of the pdf rendering logic in `Index.svelte` to `Example.svelte`.\n\n\n```svelte\n<script lang=\"ts\">\n\texport let value: string;\n\texport let type: \"gallery\" | \"table\";\n\texport let selected = false;\n\timport pdfjsLib from \"pdfjs-dist\";\n\tpdfjsLib.GlobalWorkerOptions.workerSrc =  \"https://cdn.bootcss.com/pdf.js/3.11.174/pdf.worker.js\";\n\t\n\tlet pdfDoc;\n\tlet canvasRef;\n\n\tasync function get_doc(url: string) {\n\t\tconst loadingTask = pdfjsLib.getDocument(url);\n\t\tpdfDoc = await loadingTask.promise;\n\t\trenderPage();\n\t\t}\n\n\tfunction renderPage() {\n\t\t// Render a specific page of the PDF onto the canvas\n\t\t\tpdfDoc.getPage(1).then(page => {\n\t\t\t\tconst ctx  = canvasRef.getContext('2d')\n\t\t\t\tctx.clearRect(0, 0, canvasRef.width, canvasRef.height);\n\t\t\t\t\n\t\t\t\tconst viewport = page.getViewport({ scale: 0.2 });\n\t\t\t\t\n\t\t\t\tconst renderContext = {\n\t\t\t\t\tcanvasContext: ctx,\n\t\t\t\t\tviewport\n\t\t\t\t};\n\t\t\t\tcanvasRef.width = viewport.width;\n\t\t\t\tcanvasRef.height = viewport.height;\n\t\t\t\tpage.render(renderContext);\n\t\t\t});\n\t\t}\n\t\n\t$: get_doc(value);\n</script>\n\n<div\n\tclass:table={type === \"table\"}\n\tclass:gallery={type === \"gallery\"}\n\tclass:selected\n\tstyle=\"justify-content: center; align-items: center; display: flex; flex-direction: column;\"\n>\n\t<canvas bind:this={canvasRef}></canvas>\n</div>\n\n<style>\n\t.gallery {\n\t\tpadding: var(--size-1) var(--size-2);\n\t}\n</style>\n```\n\n\nTip: Exercise for the reader - reduce the code duplication between `Index.svelte` and `Example.svelte` \ud83d\ude0a\n\n\nYou will not be able to render examples until we make some changes to the backend code in the next step!\n\n", "heading1": "Step 8.5: The Example view", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "The backend changes needed are smaller.\nWe're almost done!\n\nWhat we're going to do is:\n* Add `change` and `upload` events to our component.\n* Add a `height` property to let users control the height of the PDF.\n* Set the `data_model` of our component to be `FileData`. This is so that Gradio can automatically cache and safely serve any files that are processed by our component.\n* Modify the `preprocess` method to return a string corresponding to the path of our uploaded PDF.\n* Modify the `postprocess` to turn a path to a PDF created in an event handler to a `FileData`.\n\nWhen all is said an done, your component's backend code should look like this:\n\n```python\nfrom __future__ import annotations\nfrom typing import Any, Callable, TYPE_CHECKING\n\nfrom gradio.components.base import Component\nfrom gradio.data_classes import FileData\nfrom gradio import processing_utils\nif TYPE_CHECKING:\n    from gradio.components import Timer\n\nclass PDF(Component):\n\n    EVENTS = [\"change\", \"upload\"]\n\n    data_model = FileData\n\n    def __init__(self, value: Any = None, *,\n                 height: int | None = None,\n                 label: str | I18nData | None = None,\n                 info: str | I18nData | None = None,\n                 show_label: bool | None = None,\n                 container: bool = True,\n                 scale: int | None = None,\n                 min_width: int | None = None,\n                 interactive: bool | None = None,\n                 visible: bool = True,\n                 elem_id: str | None = None,\n                 elem_classes: list[str] | str | None = None,\n                 render: bool = True,\n                 load_fn: Callable[..., Any] | None = None,\n                 every: Timer | float | None = None):\n        super().__init__(value, label=label, info=info,\n                         show_label=show_label, container=container,\n                         scale=scale, min_width=min_width,\n                         interactive=interactive, visible=visible,\n          ", "heading1": "Step 9: The backend", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "                    show_label=show_label, container=container,\n                         scale=scale, min_width=min_width,\n                         interactive=interactive, visible=visible,\n                         elem_id=elem_id, elem_classes=elem_classes,\n                         render=render, load_fn=load_fn, every=every)\n        self.height = height\n\n    def preprocess(self, payload: FileData) -> str:\n        return payload.path\n\n    def postprocess(self, value: str | None) -> FileData:\n        if not value:\n            return None\n        return FileData(path=value)\n\n    def example_payload(self):\n        return \"https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/fw9.pdf\"\n\n    def example_value(self):\n        return \"https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/fw9.pdf\"\n```\n\n", "heading1": "Step 9: The backend", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "To test our backend code, let's add a more complex demo that performs Document Question and Answering with huggingface transformers.\n\nIn our `demo` directory, create a `requirements.txt` file with the following packages\n\n```\ntorch\ntransformers\npdf2image\npytesseract\n```\n\n\nTip: Remember to install these yourself and restart the dev server! You may need to install extra non-python dependencies for `pdf2image`. See [here](https://pypi.org/project/pdf2image/). Feel free to write your own demo if you have trouble.\n\n\n```python\nimport gradio as gr\nfrom gradio_pdf import PDF\nfrom pdf2image import convert_from_path\nfrom transformers import pipeline\nfrom pathlib import Path\n\ndir_ = Path(__file__).parent\n\np = pipeline(\n    \"document-question-answering\",\n    model=\"impira/layoutlm-document-qa\",\n)\n\ndef qa(question: str, doc: str) -> str:\n    img = convert_from_path(doc)[0]\n    output = p(img, question)\n    return sorted(output, key=lambda x: x[\"score\"], reverse=True)[0]['answer']\n\n\ndemo = gr.Interface(\n    qa,\n    [gr.Textbox(label=\"Question\"), PDF(label=\"Document\")],\n    gr.Textbox(),\n)\n\ndemo.launch()\n```\n\nSee our demo in action below!\n\n<video autoplay muted loop>\n  <source src=\"https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/PDFDemo.mov\" type=\"video/mp4\" />\n</video>\n\nFinally lets build our component with `gradio cc build` and publish it with the `gradio cc publish` command!\nThis will guide you through the process of uploading your component to [PyPi](https://pypi.org/) and [HuggingFace Spaces](https://huggingface.co/spaces).\n\n\nTip: You may need to add the following lines to the `Dockerfile` of your HuggingFace Space.\n\n```Dockerfile\nRUN mkdir -p /tmp/cache/\nRUN chmod a+rwx -R /tmp/cache/\nRUN apt-get update && apt-get install -y poppler-utils tesseract-ocr\n\nENV TRANSFORMERS_CACHE=/tmp/cache/\n```\n\n", "heading1": "Step 10: Add a demo and publish!", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "In order to use our new component in **any** gradio 4.0 app, simply install it with pip, e.g. `pip install gradio-pdf`. Then you can use it like the built-in `gr.File()` component (except that it will only accept and display PDF files).\n\nHere is a simple demo with the Blocks api:\n\n```python\nimport gradio as gr\nfrom gradio_pdf import PDF\n\nwith gr.Blocks() as demo:\n    pdf = PDF(label=\"Upload a PDF\", interactive=True)\n    name = gr.Textbox()\n    pdf.upload(lambda f: f, pdf, name)\n\ndemo.launch()\n```\n\n\nI hope you enjoyed this tutorial!\nThe complete source code for our component is [here](https://huggingface.co/spaces/freddyaboulton/gradio_pdf/tree/main/src).\nPlease don't hesitate to reach out to the gradio community on the [HuggingFace Discord](https://discord.gg/hugging-face-879548962464493619) if you get stuck.\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "The documentation will be generated when running `gradio cc build`. You can pass the `--no-generate-docs` argument to turn off this behaviour.\n\nThere is also a standalone `docs` command that allows for greater customisation. If you are running this command manually it should be run _after_ the `version` in your `pyproject.toml` has been bumped but before building the component.\n\nAll arguments are optional.\n\n```bash\ngradio cc docs\n  path The directory of the custom component.\n  --demo-dir Path to the demo directory.\n  --demo-name Name of the demo file\n  --space-url URL of the Hugging Face Space to link to\n  --generate-space create a documentation space.\n  --no-generate-space do not create a documentation space\n  --readme-path Path to the README.md file.\n  --generate-readme create a REAMDE.md file\n  --no-generate-readme do not create a README.md file\n  --suppress-demo-check suppress validation checks and warnings\n```\n\n", "heading1": "How do I use it?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "The `gradio cc docs` command will generate an interactive Gradio app and a static README file with various features. You can see an example here:\n\n- [Gradio app deployed on Hugging Face Spaces]()\n- [README.md rendered by GitHub]()\n\nThe README.md and space both have the following features:\n\n- A description.\n- Installation instructions.\n- A fully functioning code snippet.\n- Optional links to PyPi, GitHub, and Hugging Face Spaces.\n- API documentation including:\n  - An argument table for component initialisation showing types, defaults, and descriptions.\n  - A description of how the component affects the user's predict function.\n  - A table of events and their descriptions.\n  - Any additional interfaces or classes that may be used during initialisation or in the pre- or post- processors.\n\nAdditionally, the Gradio includes:\n\n- A live demo.\n- A richer, interactive version of the parameter tables.\n- Nicer styling!\n\n", "heading1": "What gets generated?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "The documentation generator uses existing standards to extract the necessary information, namely Type Hints and Docstrings. There are no Gradio-specific APIs for documentation, so following best practices will generally yield the best results.\n\nIf you already use type hints and docstrings in your component source code, you don't need to do much to benefit from this feature, but there are some details that you should be aware of.\n\nPython version\n\nTo get the best documentation experience, you need to use Python `3.10` or greater when generating documentation. This is because some introspection features used to generate the documentation were only added in `3.10`.\n\nType hints\n\nPython type hints are used extensively to provide helpful information for users. \n\n<details> \n<summary> What are type hints?</summary>\n\n\nIf you need to become more familiar with type hints in Python, they are a simple way to express what Python types are expected for arguments and return values of functions and methods. They provide a helpful in-editor experience, aid in maintenance, and integrate with various other tools. These types can be simple primitives, like `list` `str` `bool`; they could be more compound types like `list[str]`, `str | None` or `tuple[str, float | int]`; or they can be more complex types using utility classed like [`TypedDict`](https://peps.python.org/pep-0589/abstract).\n\n[Read more about type hints in Python.](https://realpython.com/lessons/type-hinting/)\n\n\n</details>\n\nWhat do I need to add hints to?\n\nYou do not need to add type hints to every part of your code. For the documentation to work correctly, you will need to add type hints to the following component methods:\n\n- `__init__` parameters should be typed.\n- `postprocess` parameters and return value should be typed.\n- `preprocess` parameters and return value should be typed.\n\nIf you are using `gradio cc create`, these types should already exist, but you may need to tweak them based on any changes you ma", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "be typed.\n- `preprocess` parameters and return value should be typed.\n\nIf you are using `gradio cc create`, these types should already exist, but you may need to tweak them based on any changes you make.\n\n`__init__`\n\nHere, you only need to type the parameters. If you have cloned a template with `gradio` cc create`, these should already be in place. You will only need to add new hints for anything you have added or changed:\n\n```py\ndef __init__(\n  self,\n  value: str | None = None,\n  *,\n  sources: Literal[\"upload\", \"microphone\"] = \"upload,\n  every: Timer | float | None = None,\n  ...\n):\n  ...\n```\n\n`preprocess` and `postprocess`\n\nThe `preprocess` and `postprocess` methods determine the value passed to the user function and the value that needs to be returned.\n\nEven if the design of your component is primarily as an input or an output, it is worth adding type hints to both the input parameters and the return values because Gradio has no way of limiting how components can be used.\n\nIn this case, we specifically care about:\n\n- The return type of `preprocess`.\n- The input type of `postprocess`.\n\n```py\ndef preprocess(\n  self, payload: FileData | None input is optional\n) -> tuple[int, str] | str | None:\n\nuser function input  is the preprocess return \u25b2\nuser function output is the postprocess input \u25bc\n\ndef postprocess(\n  self, value: tuple[int, str] | None\n) -> FileData | bytes | None: return is optional\n  ...\n```\n\nDocstrings\n\nDocstrings are also used extensively to extract more meaningful, human-readable descriptions of certain parts of the API.\n\n<details> \n<summary> What are docstrings?</summary>\n\n\nIf you need to become more familiar with docstrings in Python, they are a way to annotate parts of your code with human-readable decisions and explanations. They offer a rich in-editor experience like type hints, but unlike type hints, they don't have any specific syntax requirements. They are simple strings and can take almost any form. The only requirement i", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "offer a rich in-editor experience like type hints, but unlike type hints, they don't have any specific syntax requirements. They are simple strings and can take almost any form. The only requirement is where they appear. Docstrings should be \"a string literal that occurs as the first statement in a module, function, class, or method definition\".\n\n[Read more about Python docstrings.](https://peps.python.org/pep-0257/what-is-a-docstring)\n\n</details>\n\nWhile docstrings don't have any syntax requirements, we need a particular structure for documentation purposes.\n\nAs with type hint, the specific information we care about is as follows:\n\n- `__init__` parameter docstrings.\n- `preprocess` return docstrings.\n- `postprocess` input parameter docstrings.\n\nEverything else is optional.\n\nDocstrings should always take this format to be picked up by the documentation generator:\n\nClasses\n\n```py\n\"\"\"\nA description of the class.\n\nThis can span multiple lines and can _contain_ *markdown*.\n\"\"\"\n```\n\nMethods and functions \n\nMarkdown in these descriptions will not be converted into formatted text.\n\n```py\n\"\"\"\nParameters:\n    param_one: A description for this parameter.\n    param_two: A description for this parameter.\nReturns:\n    A description for this return value.\n\"\"\"\n```\n\nEvents\n\nIn custom components, events are expressed as a list stored on the `events` field of the component class. While we do not need types for events, we _do_ need a human-readable description so users can understand the behaviour of the event.\n\nTo facilitate this, we must create the event in a specific way.\n\nThere are two ways to add events to a custom component.\n\nBuilt-in events\n\nGradio comes with a variety of built-in events that may be enough for your component. If you are using built-in events, you do not need to do anything as they already have descriptions we can extract:\n\n```py\nfrom gradio.events import Events\n\nclass ParamViewer(Component):\n  ...\n\n  EVENTS = [\n    Events.change,\n    Events.up", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "do not need to do anything as they already have descriptions we can extract:\n\n```py\nfrom gradio.events import Events\n\nclass ParamViewer(Component):\n  ...\n\n  EVENTS = [\n    Events.change,\n    Events.upload,\n  ]\n```\n\nCustom events\n\nYou can define a custom event if the built-in events are unsuitable for your use case. This is a straightforward process, but you must create the event in this way for docstrings to work correctly:\n\n```py\nfrom gradio.events import Events, EventListener\n\nclass ParamViewer(Component):\n  ...\n\n  EVENTS = [\n    Events.change,\n    EventListener(\n        \"bingbong\",\n        doc=\"This listener is triggered when the user does a bingbong.\"\n      )\n  ]\n```\n\nDemo\n\nThe `demo/app.py`, often used for developing the component, generates the live demo and code snippet. The only strict rule here is that the `demo.launch()` command must be contained with a `__name__ == \"__main__\"` conditional as below:\n\n```py\nif __name__ == \"__main__\":\n  demo.launch()\n```\n\nThe documentation generator will scan for such a clause and error if absent. If you are _not_ launching the demo inside the `demo/app.py`, then you can pass `--suppress-demo-check` to turn off this check.\n\nDemo recommendations\n\nAlthough there are no additional rules, there are some best practices you should bear in mind to get the best experience from the documentation generator.\n\nThese are only guidelines, and every situation is unique, but they are sound principles to remember.\n\nKeep the demo compact\n\nCompact demos look better and make it easier for users to understand what the demo does. Try to remove as many extraneous UI elements as possible to focus the users' attention on the core use case. \n\nSometimes, it might make sense to have a `demo/app.py` just for the docs and an additional, more complex app for your testing purposes. You can also create other spaces, showcasing more complex examples and linking to them from the main class docstring or the `pyproject.toml` description.\n\n", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "ore complex app for your testing purposes. You can also create other spaces, showcasing more complex examples and linking to them from the main class docstring or the `pyproject.toml` description.\n\nKeep the code concise\n\nThe 'getting started' snippet utilises the demo code, which should be as short as possible to keep users engaged and avoid confusion.\n\nIt isn't the job of the sample snippet to demonstrate the whole API; this snippet should be the shortest path to success for a new user. It should be easy to type or copy-paste and easy to understand. Explanatory comments should be brief and to the point.\n\nAvoid external dependencies\n\nAs mentioned above, users should be able to copy-paste a snippet and have a fully working app. Try to avoid third-party library dependencies to facilitate this.\n\nYou should carefully consider any examples; avoiding examples that require additional files or that make assumptions about the environment is generally a good idea.\n\nEnsure the `demo` directory is self-contained\n\nOnly the `demo` directory will be uploaded to Hugging Face spaces in certain instances, as the component will be installed via PyPi if possible. It is essential that this directory is self-contained and any files needed for the correct running of the demo are present.\n\nAdditional URLs\n\nThe documentation generator will generate a few buttons, providing helpful information and links to users. They are obtained automatically in some cases, but some need to be explicitly included in the `pyproject.yaml`. \n\n- PyPi Version and link - This is generated automatically.\n- GitHub Repository - This is populated via the `pyproject.toml`'s `project.urls.repository`.\n- Hugging Face Space - This is populated via the `pyproject.toml`'s `project.urls.space`.\n\nAn example `pyproject.toml` urls section might look like this:\n\n```toml\n[project.urls]\nrepository = \"https://github.com/user/repo-name\"\nspace = \"https://huggingface.co/spaces/user/space-name\"\n```", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "pyproject.toml` urls section might look like this:\n\n```toml\n[project.urls]\nrepository = \"https://github.com/user/repo-name\"\nspace = \"https://huggingface.co/spaces/user/space-name\"\n```", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "By default, all custom component packages are called `gradio_<component-name>` where `component-name` is the name of the component's python class in lowercase.\n\nAs an example, let's walkthrough changing the name of a component from `gradio_mytextbox` to `supertextbox`. \n\n1. Modify the `name` in the `pyproject.toml` file. \n\n```bash\n[project]\nname = \"supertextbox\"\n```\n\n2. Change all occurrences of `gradio_<component-name>` in `pyproject.toml` to `<component-name>`\n\n```bash\n[tool.hatch.build]\nartifacts = [\"/backend/supertextbox/templates\", \"*.pyi\"]\n\n[tool.hatch.build.targets.wheel]\npackages = [\"/backend/supertextbox\"]\n```\n\n3. Rename the `gradio_<component-name>` directory in `backend/` to `<component-name>`\n\n```bash\nmv backend/gradio_mytextbox backend/supertextbox\n```\n\n\nTip: Remember to change the import statement in `demo/app.py`!\n\n", "heading1": "The Package Name", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "By default, only the custom component python class is a top level export. \nThis means that when users type `from gradio_<component-name> import ...`, the only class that will be available is the custom component class.\nTo add more classes as top level exports, modify the `__all__` property in `__init__.py`\n\n```python\nfrom .mytextbox import MyTextbox\nfrom .mytextbox import AdditionalClass, additional_function\n\n__all__ = ['MyTextbox', 'AdditionalClass', 'additional_function']\n```\n\n", "heading1": "Top Level Python Exports", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "You can add python dependencies by modifying the `dependencies` key in `pyproject.toml`\n\n```bash\ndependencies = [\"gradio\", \"numpy\", \"PIL\"]\n```\n\n\nTip: Remember to run `gradio cc install` when you add dependencies!\n\n", "heading1": "Python Dependencies", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "You can add JavaScript dependencies by modifying the `\"dependencies\"` key in `frontend/package.json`\n\n```json\n\"dependencies\": {\n    \"@gradio/atoms\": \"0.2.0-beta.4\",\n    \"@gradio/statustracker\": \"0.3.0-beta.6\",\n    \"@gradio/utils\": \"0.2.0-beta.4\",\n    \"your-npm-package\": \"<version>\"\n}\n```\n\n", "heading1": "Javascript Dependencies", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "By default, the CLI will place the Python code in `backend` and the JavaScript code in `frontend`.\nIt is not recommended to change this structure since it makes it easy for a potential contributor to look at your source code and know where everything is.\nHowever, if you did want to this is what you would have to do:\n\n1. Place the Python code in the subdirectory of your choosing. Remember to modify the `[tool.hatch.build]` `[tool.hatch.build.targets.wheel]` in the `pyproject.toml` to match!\n\n2. Place the JavaScript code in the subdirectory of your choosing.\n\n2. Add the `FRONTEND_DIR` property on the component python class. It must be the relative path from the file where the class is defined to the location of the JavaScript directory.\n\n```python\nclass SuperTextbox(Component):\n    FRONTEND_DIR = \"../../frontend/\"\n```\n\nThe JavaScript and Python directories must be under the same common directory!\n\n", "heading1": "Directory Structure", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "Sticking to the defaults will make it easy for others to understand and contribute to your custom component.\nAfter all, the beauty of open source is that anyone can help improve your code!\nBut if you ever need to deviate from the defaults, you know how!", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "For this demo we will be tweaking the existing Gradio `Chatbot` component to display text and media files in the same message.\nLet's create a new custom component directory by templating off of the `Chatbot` component source code.\n\n```bash\ngradio cc create MultimodalChatbot --template Chatbot\n```\n\nAnd we're ready to go!\n\nTip: Make sure to modify the `Author` key in the `pyproject.toml` file.\n\n", "heading1": "Part 1 - Creating our project", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "Open up the `multimodalchatbot.py` file in your favorite code editor and let's get started modifying the backend of our component.\n\nThe first thing we will do is create the `data_model` of our component.\nThe `data_model` is the data format that your python component will receive and send to the javascript client running the UI.\nYou can read more about the `data_model` in the [backend guide](./backend).\n\nFor our component, each chatbot message will consist of two keys: a `text` key that displays the text message and an optional list of media files that can be displayed underneath the text.\n\nImport the `FileData` and `GradioModel` classes from `gradio.data_classes` and modify the existing `ChatbotData` class to look like the following:\n\n```python\nclass FileMessage(GradioModel):\n    file: FileData\n    alt_text: Optional[str] = None\n\n\nclass MultimodalMessage(GradioModel):\n    text: Optional[str] = None\n    files: Optional[List[FileMessage]] = None\n\n\nclass ChatbotData(GradioRootModel):\n    root: List[Tuple[Optional[MultimodalMessage], Optional[MultimodalMessage]]]\n\n\nclass MultimodalChatbot(Component):\n    ...\n    data_model = ChatbotData\n```\n\n\nTip: The `data_model`s are implemented using `Pydantic V2`. Read the documentation [here](https://docs.pydantic.dev/latest/).\n\nWe've done the hardest part already!\n\n", "heading1": "Part 2a - The backend data_model", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "For the `preprocess` method, we will keep it simple and pass a list of `MultimodalMessage`s to the python functions that use this component as input. \nThis will let users of our component access the chatbot data with `.text` and `.files` attributes.\nThis is a design choice that you can modify in your implementation!\nWe can return the list of messages with the `root` property of the `ChatbotData` like so:\n\n```python\ndef preprocess(\n    self,\n    payload: ChatbotData | None,\n) -> List[MultimodalMessage] | None:\n    if payload is None:\n        return payload\n    return payload.root\n```\n\n\nTip: Learn about the reasoning behind the `preprocess` and `postprocess` methods in the [key concepts guide](./key-component-concepts)\n\nIn the `postprocess` method we will coerce each message returned by the python function to be a `MultimodalMessage` class. \nWe will also clean up any indentation in the `text` field so that it can be properly displayed as markdown in the frontend.\n\nWe can leave the `postprocess` method as is and modify the `_postprocess_chat_messages`\n\n```python\ndef _postprocess_chat_messages(\n    self, chat_message: MultimodalMessage | dict | None\n) -> MultimodalMessage | None:\n    if chat_message is None:\n        return None\n    if isinstance(chat_message, dict):\n        chat_message = MultimodalMessage(**chat_message)\n    chat_message.text = inspect.cleandoc(chat_message.text or \"\")\n    for file_ in chat_message.files:\n        file_.file.mime_type = client_utils.get_mimetype(file_.file.path)\n    return chat_message\n```\n\nBefore we wrap up with the backend code, let's modify the `example_value` and `example_payload` method to return a valid dictionary representation of the `ChatbotData`:\n\n```python\ndef example_value(self) -> Any:\n    return [[{\"text\": \"Hello!\", \"files\": []}, None]]\n\ndef example_payload(self) -> Any:\n    return [[{\"text\": \"Hello!\", \"files\": []}, None]]\n```\n\nCongrats - the backend is complete!\n\n", "heading1": "Part 2b - The pre and postprocess methods", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "The frontend for the `Chatbot` component is divided into two parts - the `Index.svelte` file and the `shared/Chatbot.svelte` file.\nThe `Index.svelte` file applies some processing to the data received from the server and then delegates the rendering of the conversation to the `shared/Chatbot.svelte` file.\nFirst we will modify the `Index.svelte` file to apply processing to the new data type the backend will return.\n\nLet's begin by porting our custom types  from our python `data_model` to typescript.\nOpen `frontend/shared/utils.ts` and add the following type definitions at the top of the file:\n\n```ts\nexport type FileMessage = {\n\tfile: FileData;\n\talt_text?: string;\n};\n\n\nexport type MultimodalMessage = {\n\ttext: string;\n\tfiles?: FileMessage[];\n}\n```\n\nNow let's import them in `Index.svelte` and modify the type annotations for `value` and `_value`.\n\n```ts\nimport type { FileMessage, MultimodalMessage } from \"./shared/utils\";\n\nexport let value: [\n    MultimodalMessage | null,\n    MultimodalMessage | null\n][] = [];\n\nlet _value: [\n    MultimodalMessage | null,\n    MultimodalMessage | null\n][];\n```\n\nWe need to normalize each message to make sure each file has a proper URL to fetch its contents from.\nWe also need to format any embedded file links in the `text` key.\nLet's add a `process_message` utility function and apply it whenever the `value` changes.\n\n```ts\nfunction process_message(msg: MultimodalMessage | null): MultimodalMessage | null {\n    if (msg === null) {\n        return msg;\n    }\n    msg.text = redirect_src_url(msg.text);\n    msg.files = msg.files.map(normalize_messages);\n    return msg;\n}\n\n$: _value = value\n    ? value.map(([user_msg, bot_msg]) => [\n            process_message(user_msg),\n            process_message(bot_msg)\n        ])\n    : [];\n```\n\n", "heading1": "Part 3a - The Index.svelte file", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "Let's begin similarly to the `Index.svelte` file and let's first modify the type annotations.\nImport `Mulimodal` message at the top of the `<script>` section and use it to type the `value` and `old_value` variables.\n\n```ts\nimport type { MultimodalMessage } from \"./utils\";\n\nexport let value:\n    | [\n            MultimodalMessage | null,\n            MultimodalMessage | null\n        ][]\n    | null;\nlet old_value:\n    | [\n            MultimodalMessage | null,\n            MultimodalMessage | null\n        ][]\n    | null = null;\n```\n\nWe also need to modify the `handle_select` and `handle_like` functions:\n\n```ts\nfunction handle_select(\n    i: number,\n    j: number,\n    message: MultimodalMessage | null\n): void {\n    dispatch(\"select\", {\n        index: [i, j],\n        value: message\n    });\n}\n\nfunction handle_like(\n    i: number,\n    j: number,\n    message: MultimodalMessage | null,\n    liked: boolean\n): void {\n    dispatch(\"like\", {\n        index: [i, j],\n        value: message,\n        liked: liked\n    });\n}\n```\n\nNow for the fun part, actually rendering the text and files in the same message!\n\nYou should see some code like the following that determines whether a file or a markdown message should be displayed depending on the type of the message:\n\n```svelte\n{if typeof message === \"string\"}\n    <Markdown\n        {message}\n        {latex_delimiters}\n        {sanitize_html}\n        {render_markdown}\n        {line_breaks}\n        on:load={scroll}\n    />\n{:else if message !== null && message.file?.mime_type?.includes(\"audio\")}\n    <audio\n        data-testid=\"chatbot-audio\"\n        controls\n        preload=\"metadata\"\n        ...\n```\n\nWe will modify this code to always display the text message and then loop through the files and display all of them that are present:\n\n```svelte\n<Markdown\n    message={message.text}\n    {latex_delimiters}\n    {sanitize_html}\n    {render_markdown}\n    {line_breaks}\n    on:load={scroll}\n/>\n{each message.files as file, k}\n    {if file !== null && fil", "heading1": "Part 3b - the Chatbot.svelte file", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "own\n    message={message.text}\n    {latex_delimiters}\n    {sanitize_html}\n    {render_markdown}\n    {line_breaks}\n    on:load={scroll}\n/>\n{each message.files as file, k}\n    {if file !== null && file.file.mime_type?.includes(\"audio\")}\n        <audio\n            data-testid=\"chatbot-audio\"\n            controls\n            preload=\"metadata\"\n            src={file.file?.url}\n            title={file.alt_text}\n            on:play\n            on:pause\n            on:ended\n        />\n    {:else if message !== null && file.file?.mime_type?.includes(\"video\")}\n        <video\n            data-testid=\"chatbot-video\"\n            controls\n            src={file.file?.url}\n            title={file.alt_text}\n            preload=\"auto\"\n            on:play\n            on:pause\n            on:ended\n        >\n            <track kind=\"captions\" />\n        </video>\n    {:else if message !== null && file.file?.mime_type?.includes(\"image\")}\n        <img\n            data-testid=\"chatbot-image\"\n            src={file.file?.url}\n            alt={file.alt_text}\n        />\n    {:else if message !== null && file.file?.url !== null}\n        <a\n            data-testid=\"chatbot-file\"\n            href={file.file?.url}\n            target=\"_blank\"\n            download={window.__is_colab__\n                ? null\n                : file.file?.orig_name || file.file?.path}\n        >\n            {file.file?.orig_name || file.file?.path}\n        </a>\n    {:else if pending_message && j === 1}\n        <Pending {layout} />\n    {/if}\n{/each}\n```\n\nWe did it! \ud83c\udf89\n\n", "heading1": "Part 3b - the Chatbot.svelte file", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "For this tutorial, let's keep the demo simple and just display a static conversation between a hypothetical user and a bot.\nThis demo will show how both the user and the bot can send files. \nIn part 2 of this tutorial series we will build a fully functional chatbot demo!\n\nThe demo code will look like the following:\n\n```python\nimport gradio as gr\nfrom gradio_multimodalchatbot import MultimodalChatbot\nfrom gradio.data_classes import FileData\n\nuser_msg1 = {\"text\": \"Hello, what is in this image?\",\n             \"files\": [{\"file\": FileData(path=\"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\")}]\n             }\nbot_msg1 = {\"text\": \"It is a very cute dog\",\n            \"files\": []}\n\nuser_msg2 = {\"text\": \"Describe this audio clip please.\",\n             \"files\": [{\"file\": FileData(path=\"cantina.wav\")}]}\nbot_msg2 = {\"text\": \"It is the cantina song from Star Wars\",\n            \"files\": []}\n\nuser_msg3 = {\"text\": \"Give me a video clip please.\",\n             \"files\": []}\nbot_msg3 = {\"text\": \"Here is a video clip of the world\",\n            \"files\": [{\"file\": FileData(path=\"world.mp4\")},\n                      {\"file\": FileData(path=\"cantina.wav\")}]}\n\nconversation = [[user_msg1, bot_msg1], [user_msg2, bot_msg2], [user_msg3, bot_msg3]]\n\nwith gr.Blocks() as demo:\n    MultimodalChatbot(value=conversation, height=800)\n\n\ndemo.launch()\n```\n\n\nTip: Change the filepaths so that they correspond to files on your machine. Also, if you are running in development mode, make sure the files are located in the top level of your custom component directory.\n\n", "heading1": "Part 4 - The demo", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "Let's build and deploy our demo with `gradio cc build` and `gradio cc deploy`!\n\nYou can check out our component deployed to [HuggingFace Spaces](https://huggingface.co/spaces/freddyaboulton/gradio_multimodalchatbot) and all of the source code is available [here](https://huggingface.co/spaces/freddyaboulton/gradio_multimodalchatbot/tree/main/src).\n\nSee you in the next installment of this series!", "heading1": "Part 5 - Deploying and Conclusion", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "Before using Custom Components, make sure you have Python 3.10+, Node.js v18+, npm 9+, and Gradio 4.0+ (preferably Gradio 5.0+) installed.\n\n", "heading1": "What do I need to install before using Custom Components?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "Custom components built with Gradio 5.0 should be compatible with Gradio 4.0. If you built your custom component in Gradio 4.0 you will have to rebuild your component to be compatible with Gradio 5.0. Simply follow these steps:\n1. Update the `@gradio/preview` package. `cd` into the `frontend` directory and run `npm update`.\n2.  Modify the `dependencies` key in `pyproject.toml` to pin the maximum allowed Gradio version at version 5, e.g. `dependencies = [\"gradio>=4.0,<6.0\"]`.\n3. Run the build and publish commands\n\n", "heading1": "Are custom components compatible between Gradio 4.0 and 5.0?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "Run `gradio cc show` to see the list of built-in templates.\nYou can also start off from other's custom components!\nSimply `git clone` their repository and make your modifications.\n\n", "heading1": "What templates can I use to create my custom component?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "When you run `gradio cc dev`, a development server will load and run a Gradio app of your choosing.\nThis is like when you run `python <app-file>.py`, however the `gradio` command will hot reload so you can instantly see your changes. \n\n", "heading1": "What is the development server?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "**1. Check your terminal and browser console**\n\nMake sure there are no syntax errors or other obvious problems in your code. Exceptions triggered from python will be displayed in the terminal. Exceptions from javascript will be displayed in the browser console and/or the terminal.\n\n**2. Are you developing on Windows?**\n\nChrome on Windows will block the local compiled svelte files for security reasons. We recommend developing your custom component in the windows subsystem for linux (WSL) while the team looks at this issue.\n\n**3. Inspect the window.__GRADIO_CC__ variable**\n\nIn the browser console, print the `window.__GRADIO__CC` variable (just type it into the console). If it is an empty object, that means\nthat the CLI could not find your custom component source code. Typically, this happens when the custom component is installed in a different virtual environment than the one used to run the dev command. Please use the `--python-path` and `gradio-path` CLI arguments to specify the path of the python and gradio executables for the environment your component is installed in. For example, if you are using a virtualenv located at `/Users/mary/venv`, pass in `/Users/mary/bin/python` and `/Users/mary/bin/gradio` respectively.\n\nIf the `window.__GRADIO__CC` variable is not empty (see below for an example), then the dev server should be working correctly. \n\n![](https://gradio-builds.s3.amazonaws.com/demo-files/gradio_CC_DEV.png)\n\n**4. Make sure you are using a virtual environment**\nIt is highly recommended you use a virtual environment to prevent conflicts with other python dependencies installed in your system.\n\n\n", "heading1": "The development server didn't work for me", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "No! You can start off from an existing gradio component as a template, see the [five minute guide](./custom-components-in-five-minutes).\nYou can also start from an existing custom component if you'd like to tweak it further. Once you find the source code of a custom component you like, clone the code to your computer and run `gradio cc install`. Then you can run the development server to make changes.If you run into any issues, contact the author of the component by opening an issue in their repository. The [gallery](https://www.gradio.app/custom-components/gallery) is a good place to look for published components. For example, to start from the [PDF component](https://www.gradio.app/custom-components/gallery?id=freddyaboulton%2Fgradio_pdf), clone the space with `git clone https://huggingface.co/spaces/freddyaboulton/gradio_pdf`, `cd` into the `src` directory, and run `gradio cc install`.\n\n\n", "heading1": "Do I always need to start my component from scratch?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "You can develop and build your custom component without hosting or connecting to HuggingFace.\nIf you would like to share your component with the gradio community, it is recommended to publish your package to PyPi and host a demo on HuggingFace so that anyone can install it or try it out.\n\n", "heading1": "Do I need to host my custom component on HuggingFace Spaces?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "You must implement the `preprocess`, `postprocess`, `example_payload`, and `example_value` methods. If your component does not use a data model, you must also define the `api_info`, `flag`, and `read_from_flag` methods. Read more in the [backend guide](./backend).\n\n", "heading1": "What methods are mandatory for implementing a custom component in Gradio?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "A `data_model` defines the expected data format for your component, simplifying the component development process and self-documenting your code. It streamlines API usage and example caching.\n\n", "heading1": "What is the purpose of a `data_model` in Gradio custom components?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "Utilizing `FileData` is crucial for components that expect file uploads. It ensures secure file handling, automatic caching, and streamlined client library functionality.\n\n", "heading1": "Why is it important to use `FileData` for components dealing with file uploads?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "You can define event triggers in the `EVENTS` class attribute by listing the desired event names, which automatically adds corresponding methods to your component.\n\n", "heading1": "How can I add event triggers to my custom Gradio component?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "Yes, it is possible to create custom components without a `data_model`, but you are going to have to manually implement `api_info`, `flag`, and `read_from_flag` methods.\n\n", "heading1": "Can I implement a custom Gradio component without defining a `data_model`?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "We have prepared this [collection](https://huggingface.co/collections/gradio/custom-components-65497a761c5192d981710b12) of custom components on the HuggingFace Hub that you can use to get started!\n\n", "heading1": "Are there sample custom components I can learn from?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "We're working on creating a gallery to make it really easy to discover new custom components.\nIn the meantime, you can search for HuggingFace Spaces that are tagged as a `gradio-custom-component` [here](https://huggingface.co/search/full-text?q=gradio-custom-component&type=space)", "heading1": "How can I find custom components created by the Gradio community?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "All components inherit from one of three classes `Component`, `FormComponent`, or `BlockContext`.\nYou need to inherit from one so that your component behaves like all other gradio components.\nWhen you start from a template with `gradio cc create --template`, you don't need to worry about which one to choose since the template uses the correct one. \nFor completeness, and in the event that you need to make your own component from scratch, we explain what each class is for.\n\n* `FormComponent`: Use this when you want your component to be grouped together in the same `Form` layout with other `FormComponents`. The `Slider`, `Textbox`, and `Number` components are all `FormComponents`.\n* `BlockContext`: Use this when you want to place other components \"inside\" your component. This enabled `with MyComponent() as component:` syntax.\n* `Component`: Use this for all other cases.\n\nTip: If your component supports streaming output, inherit from the `StreamingOutput` class.\n\nTip: If you inherit from `BlockContext`, you also need to set the metaclass to be `ComponentMeta`. See example below.\n\n```python\nfrom gradio.blocks import BlockContext\nfrom gradio.component_meta import ComponentMeta\n\n\n\n\n@document()\nclass Row(BlockContext, metaclass=ComponentMeta):\n    pass\n```\n\n", "heading1": "Which Class to Inherit From", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "When you inherit from any of these classes, the following methods must be implemented.\nOtherwise the Python interpreter will raise an error when you instantiate your component!\n\n`preprocess` and `postprocess`\n\nExplained in the [Key Concepts](./key-component-conceptsthe-value-and-how-it-is-preprocessed-postprocessed) guide. \nThey handle the conversion from the data sent by the frontend to the format expected by the python function.\n\n```python\n    def preprocess(self, x: Any) -> Any:\n        \"\"\"\n        Convert from the web-friendly (typically JSON) value in the frontend to the format expected by the python function.\n        \"\"\"\n        return x\n\n    def postprocess(self, y):\n        \"\"\"\n        Convert from the data returned by the python function to the web-friendly (typically JSON) value expected by the frontend.\n        \"\"\"\n        return y\n```\n\n`process_example`\n\nTakes in the original Python value and returns the modified value that should be displayed in the examples preview in the app. \nIf not provided, the `.postprocess()` method is used instead. Let's look at the following example from the `SimpleDropdown` component.\n\n```python\ndef process_example(self, input_data):\n    return next((c[0] for c in self.choices if c[1] == input_data), None)\n```\n\nSince `self.choices` is a list of tuples corresponding to (`display_name`, `value`), this converts the value that a user provides to the display value (or if the value is not present in `self.choices`, it is converted to `None`).\n\n\n`api_info`\n\nA JSON-schema representation of the value that the `preprocess` expects. \nThis powers api usage via the gradio clients. \nYou do **not** need to implement this yourself if you components specifies a `data_model`. \nThe `data_model` in the following section.\n\n```python\ndef api_info(self) -> dict[str, list[str]]:\n    \"\"\"\n    A JSON-schema representation of the value that the `preprocess` expects and the `postprocess` returns.\n    \"\"\"\n    pass\n```\n\n`example_payload`\n\nA", "heading1": "The methods you need to implement", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "pi_info(self) -> dict[str, list[str]]:\n    \"\"\"\n    A JSON-schema representation of the value that the `preprocess` expects and the `postprocess` returns.\n    \"\"\"\n    pass\n```\n\n`example_payload`\n\nAn example payload for your component, e.g. something that can be passed into the `.preprocess()` method\nof your component. The example input is displayed in the `View API` page of a Gradio app that uses your custom component. \nMust be JSON-serializable. If your component expects a file, it is best to use a publicly accessible URL.\n\n```python\ndef example_payload(self) -> Any:\n    \"\"\"\n    The example inputs for this component for API usage. Must be JSON-serializable.\n    \"\"\"\n    pass\n```\n\n`example_value`\n\nAn example value for your component, e.g. something that can be passed into the `.postprocess()` method\nof your component. This is used as the example value in the default app that is created in custom component development.\n\n```python\ndef example_payload(self) -> Any:\n    \"\"\"\n    The example inputs for this component for API usage. Must be JSON-serializable.\n    \"\"\"\n    pass\n```\n\n`flag`\n\nWrite the component's value to a format that can be stored in the `csv` or `json` file used for flagging.\nYou do **not** need to implement this yourself if you components specifies a `data_model`. \nThe `data_model` in the following section.\n\n```python\ndef flag(self, x: Any | GradioDataModel, flag_dir: str | Path = \"\") -> str:\n    pass\n```\n\n`read_from_flag`\nConvert from the format stored in the `csv` or `json` file used for flagging to the component's python `value`.\nYou do **not** need to implement this yourself if you components specifies a `data_model`. \nThe `data_model` in the following section.\n\n```python\ndef read_from_flag(\n    self,\n    x: Any,\n) -> GradioDataModel | Any:\n    \"\"\"\n    Convert the data from the csv or jsonl file into the component state.\n    \"\"\"\n    return x\n```\n\n", "heading1": "The methods you need to implement", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "    \"\"\"\n    Convert the data from the csv or jsonl file into the component state.\n    \"\"\"\n    return x\n```\n\n", "heading1": "The methods you need to implement", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "The `data_model` is how you define the expected data format your component's value will be stored in the frontend.\nIt specifies the data format your `preprocess` method expects and the format the `postprocess` method returns.\nIt is not necessary to define a `data_model` for your component but it greatly simplifies the process of creating a custom component.\nIf you define a custom component you only need to implement four methods - `preprocess`, `postprocess`, `example_payload`, and `example_value`!\n\nYou define a `data_model` by defining a [pydantic model](https://docs.pydantic.dev/latest/concepts/models/basic-model-usage) that inherits from either `GradioModel` or `GradioRootModel`.\n\nThis is best explained with an example. Let's look at the core `Video` component, which stores the video data as a JSON object with two keys `video` and `subtitles` which point to separate files.\n\n```python\nfrom gradio.data_classes import FileData, GradioModel\n\nclass VideoData(GradioModel):\n    video: FileData\n    subtitles: Optional[FileData] = None\n\nclass Video(Component):\n    data_model = VideoData\n```\n\nBy adding these four lines of code, your component automatically implements the methods needed for API usage, the flagging methods, and example caching methods!\nIt also has the added benefit of self-documenting your code.\nAnyone who reads your component code will know exactly the data it expects.\n\nTip: If your component expects files to be uploaded from the frontend, your must use the `FileData` model! It will be explained in the following section. \n\nTip: Read the pydantic docs [here](https://docs.pydantic.dev/latest/concepts/models/basic-model-usage).\n\nThe difference between a `GradioModel` and a `GradioRootModel` is that the `RootModel` will not serialize the data to a dictionary.\nFor example, the `Names` model will serialize the data to `{'names': ['freddy', 'pete']}` whereas the `NamesRoot` model will serialize it to `['freddy', 'pete']`.\n\n```python\nfrom typing import List\n\nclas", "heading1": "The `data_model`", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": " example, the `Names` model will serialize the data to `{'names': ['freddy', 'pete']}` whereas the `NamesRoot` model will serialize it to `['freddy', 'pete']`.\n\n```python\nfrom typing import List\n\nclass Names(GradioModel):\n    names: List[str]\n\nclass NamesRoot(GradioRootModel):\n    root: List[str]\n```\n\nEven if your component does not expect a \"complex\" JSON data structure it can be beneficial to define a `GradioRootModel` so that you don't have to worry about implementing the API and flagging methods.\n\nTip: Use classes from the Python typing library to type your models. e.g. `List` instead of `list`.\n\n", "heading1": "The `data_model`", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "If your component expects uploaded files as input, or returns saved files to the frontend, you **MUST** use the `FileData` to type the files in your `data_model`.\n\nWhen you use the `FileData`:\n\n* Gradio knows that it should allow serving this file to the frontend. Gradio automatically blocks requests to serve arbitrary files in the computer running the server.\n\n* Gradio will automatically place the file in a cache so that duplicate copies of the file don't get saved.\n\n* The client libraries will automatically know that they should upload input files prior to sending the request. They will also automatically download files.\n\nIf you do not use the `FileData`, your component will not work as expected!\n\n\n", "heading1": "Handling Files", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "The events triggers for your component are defined in the `EVENTS` class attribute.\nThis is a list that contains the string names of the events.\nAdding an event to this list will automatically add a method with that same name to your component!\n\nYou can import the `Events` enum from `gradio.events` to access commonly used events in the core gradio components.\n\nFor example, the following code will define `text_submit`, `file_upload` and `change` methods in the `MyComponent` class.\n\n```python\nfrom gradio.events import Events\nfrom gradio.components import FormComponent\n\nclass MyComponent(FormComponent):\n\n    EVENTS = [\n        \"text_submit\",\n        \"file_upload\",\n        Events.change\n    ]\n```\n\n\nTip: Don't forget to also handle these events in the JavaScript code!\n\n", "heading1": "Adding Event Triggers To Your Component", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "Plots accept a pandas Dataframe as their value. The plot also takes `x` and `y` which represent the names of the columns that represent the x and y axes respectively. Here's a simple example:\n\n$code_plot_guide_line\n$demo_plot_guide_line\n\nAll plots have the same API, so you could swap this out with a `gr.ScatterPlot`:\n\n$code_plot_guide_scatter\n$demo_plot_guide_scatter\n\nThe y axis column in the dataframe should have a numeric type, but the x axis column can be anything from strings, numbers, categories, or datetimes.\n\n$code_plot_guide_scatter_nominal\n$demo_plot_guide_scatter_nominal\n\n", "heading1": "Creating a Plot with a pd.Dataframe", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can break out your plot into series using the `color` argument.\n\n$code_plot_guide_series_nominal\n$demo_plot_guide_series_nominal\n\nIf you wish to assign series specific colors, use the `color_map` arg, e.g. `gr.ScatterPlot(..., color_map={'white': 'FF9988', 'asian': '88EEAA', 'black': '333388'})`\n\nThe color column can be numeric type as well.\n\n$code_plot_guide_series_quantitative\n$demo_plot_guide_series_quantitative\n\n", "heading1": "Breaking out Series by Color", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can aggregate values into groups using the `x_bin` and `y_aggregate` arguments. If your x-axis is numeric, providing an `x_bin` will create a histogram-style binning:\n\n$code_plot_guide_aggregate_quantitative\n$demo_plot_guide_aggregate_quantitative\n\nIf your x-axis is a string type instead, they will act as the category bins automatically:\n\n$code_plot_guide_aggregate_nominal\n$demo_plot_guide_aggregate_nominal\n\n", "heading1": "Aggregating Values", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can use the `.select` listener to select regions of a plot. Click and drag on the plot below to select part of the plot.\n\n$code_plot_guide_selection\n$demo_plot_guide_selection\n\nYou can combine this and the `.double_click` listener to create some zoom in/out effects by changing `x_lim` which sets the bounds of the x-axis:\n\n$code_plot_guide_zoom\n$demo_plot_guide_zoom\n\nIf you had multiple plots with the same x column, your event listeners could target the x limits of all other plots so that the x-axes stay in sync.\n\n$code_plot_guide_zoom_sync\n$demo_plot_guide_zoom_sync\n\n", "heading1": "Selecting Regions", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "Take a look how you can have an interactive dashboard where the plots are functions of other Components.\n\n$code_plot_guide_interactive\n$demo_plot_guide_interactive\n\nIt's that simple to filter and control the data presented in your visualization!", "heading1": "Making an Interactive Dashboard", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "```python\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\nengine = create_engine('sqlite:///your_database.db')\n\nwith gr.Blocks() as demo:\n    gr.LinePlot(pd.read_sql_query(\"SELECT time, price from flight_info;\", engine), x=\"time\", y=\"price\")\n```\n\nLet's see a a more interactive plot involving filters that modify your SQL query:\n\n```python\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\nengine = create_engine('sqlite:///your_database.db')\n\nwith gr.Blocks() as demo:\n    origin = gr.Dropdown([\"DFW\", \"DAL\", \"HOU\"], value=\"DFW\", label=\"Origin\")\n\n    gr.LinePlot(lambda origin: pd.read_sql_query(f\"SELECT time, price from flight_info WHERE origin = {origin};\", engine), inputs=origin, x=\"time\", y=\"price\")\n```\n\n", "heading1": "SQLite", "source_page_url": "https://gradio.app/guides/connecting-to-a-database", "source_page_title": "Data Science And Plots - Connecting To A Database Guide"}, {"text": "If you're using a different database format, all you have to do is swap out the engine, e.g.\n\n```python\nengine = create_engine('postgresql://username:password@host:port/database_name')\n```\n\n```python\nengine = create_engine('mysql://username:password@host:port/database_name')\n```\n\n```python\nengine = create_engine('oracle://username:password@host:port/database_name')\n```", "heading1": "Postgres, mySQL, and other databases", "source_page_url": "https://gradio.app/guides/connecting-to-a-database", "source_page_title": "Data Science And Plots - Connecting To A Database Guide"}, {"text": "Use any of the standard Gradio form components to filter your data. You can do this via event listeners or function-as-value syntax. Let's look at the event listener approach first:\n\n$code_plot_guide_filters_events\n$demo_plot_guide_filters_events\n\nAnd this would be the function-as-value approach for the same demo.\n\n$code_plot_guide_filters\n\n", "heading1": "Filters", "source_page_url": "https://gradio.app/guides/filters-tables-and-stats", "source_page_title": "Data Science And Plots - Filters Tables And Stats Guide"}, {"text": "Add `gr.DataFrame` and `gr.Label` to your dashboard for some hard numbers.\n\n$code_plot_guide_tables_stats\n$demo_plot_guide_tables_stats\n", "heading1": "Tables and Stats", "source_page_url": "https://gradio.app/guides/filters-tables-and-stats", "source_page_title": "Data Science And Plots - Filters Tables And Stats Guide"}, {"text": "Time plots need a datetime column on the x-axis. Here's a simple example with some flight data:\n\n$code_plot_guide_temporal\n$demo_plot_guide_temporal\n\n", "heading1": "Creating a Plot with a pd.Dataframe", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "You may wish to bin data by time buckets. Use `x_bin` to do so, using a string suffix with \"s\", \"m\", \"h\" or \"d\", such as \"15m\" or \"1d\".\n\n$code_plot_guide_aggregate_temporal\n$demo_plot_guide_aggregate_temporal\n\n", "heading1": "Aggregating by Time", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "You can use `gr.DateTime` to accept input datetime data. This works well with plots for defining the x-axis range for the data.\n\n$code_plot_guide_datetime\n$demo_plot_guide_datetime\n\nNote how `gr.DateTime` can accept a full datetime string, or a shorthand using `now - [0-9]+[smhd]` format to refer to a past time.\n\nYou will often have many time plots in which case you'd like to keep the x-axes in sync. The `DateTimeRange` custom component keeps a set of datetime plots in sync, and also uses the `.select` listener of plots to allow you to zoom into plots while keeping plots in sync. \n\nBecause it is a custom component, you first need to `pip install gradio_datetimerange`. Then run the following:\n\n$code_plot_guide_datetimerange\n$demo_plot_guide_datetimerange\n\nTry zooming around in the plots and see how DateTimeRange updates. All the plots updates their `x_lim` in sync. You also have a \"Back\" link in the component to allow you to quickly zoom in and out.\n\n", "heading1": "DateTime Components", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "In many cases, you're working with live, realtime date, not a static dataframe. In this case, you'd update the plot regularly with a `gr.Timer()`. Assuming there's a `get_data` method that gets the latest dataframe:\n\n```python\nwith gr.Blocks() as demo:\n    timer = gr.Timer(5)\n    plot1 = gr.BarPlot(x=\"time\", y=\"price\")\n    plot2 = gr.BarPlot(x=\"time\", y=\"price\", color=\"origin\")\n\n    timer.tick(lambda: [get_data(), get_data()], outputs=[plot1, plot2])\n```\n\nYou can also use the `every` shorthand to attach a `Timer` to a component that has a function value:\n\n```python\nwith gr.Blocks() as demo:\n    timer = gr.Timer(5)\n    plot1 = gr.BarPlot(get_data, x=\"time\", y=\"price\", every=timer)\n    plot2 = gr.BarPlot(get_data, x=\"time\", y=\"price\", color=\"origin\", every=timer)\n```\n\n\n", "heading1": "RealTime Data", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "Let's start by using `llama-index` on top of `openai` to build a RAG chatbot on any text or PDF files that you can demo and share in less than 30 lines of code. You'll need to have an OpenAI key for this example (keep reading for the free, open-source equivalent!)\n\n$code_llm_llamaindex\n\n", "heading1": "Llama Index", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "Here's an example using `langchain` on top of `openai` to build a general-purpose chatbot. As before, you'll need to have an OpenAI key for this example.\n\n$code_llm_langchain\n\nTip: For quick prototyping, the community-maintained <a href='https://github.com/AK391/langchain-gradio'>langchain-gradio repo</a>  makes it even easier to build chatbots on top of LangChain.\n\n", "heading1": "LangChain", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "Of course, we could also use the `openai` library directy. Here a similar example to the LangChain , but this time with streaming as well:\n\nTip: For quick prototyping, the  <a href='https://github.com/gradio-app/openai-gradio'>openai-gradio library</a> makes it even easier to build chatbots on top of OpenAI models.\n\n\n", "heading1": "OpenAI", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "Of course, in many cases you want to run a chatbot locally. Here's the equivalent example using the SmolLM2-135M-Instruct model using the Hugging Face `transformers` library.\n\n$code_llm_hf_transformers\n\n", "heading1": "Hugging Face `transformers`", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "The SambaNova Cloud API provides access to full-precision open-source models, such as the Llama family. Here's an example of how to build a Gradio app around the SambaNova API\n\n$code_llm_sambanova\n\nTip: For quick prototyping, the  <a href='https://github.com/gradio-app/sambanova-gradio'>sambanova-gradio library</a> makes it even easier to build chatbots on top of SambaNova models.\n\n", "heading1": "SambaNova", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "The Hyperbolic AI API provides access to many open-source models, such as the Llama family. Here's an example of how to build a Gradio app around the Hyperbolic\n\n$code_llm_hyperbolic\n\nTip: For quick prototyping, the  <a href='https://github.com/HyperbolicLabs/hyperbolic-gradio'>hyperbolic-gradio library</a> makes it even easier to build chatbots on top of Hyperbolic models.\n\n\n", "heading1": "Hyperbolic", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "Anthropic's Claude model can also be used via API. Here's a simple 20 questions-style game built on top of the Anthropic API:\n\n$code_llm_claude\n\n\n", "heading1": "Anthropic's Claude", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "The Slack bot will listen to messages mentioning it in channels. When it receives a message (which can include text as well as files), it will send it to your Gradio app via Gradio's built-in API. Your bot will reply with the response it receives from the API. \n\nBecause Gradio's API is very flexible, you can create Slack bots that support text, images, audio, streaming, chat history, and a wide variety of other features very easily. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-19%20at%203.30.00%E2%80%AFPM.gif)\n\n", "heading1": "How does it work?", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "* Install the latest version of `gradio` and the `slack-bolt` library:\n\n```bash\npip install --upgrade gradio slack-bolt~=1.0\n```\n\n* Have a running Gradio app. This app can be running locally or on Hugging Face Spaces. In this example, we will be using the [Gradio Playground Space](https://huggingface.co/spaces/abidlabs/gradio-playground-bot), which takes in an image and/or text and generates the code to generate the corresponding Gradio app.\n\nNow, we are ready to get started!\n\n1. Create a Slack App\n\n1. Go to [api.slack.com/apps](https://api.slack.com/apps) and click \"Create New App\"\n2. Choose \"From scratch\" and give your app a name\n3. Select the workspace where you want to develop your app\n4. Under \"OAuth & Permissions\", scroll to \"Scopes\" and add these Bot Token Scopes:\n   - `app_mentions:read`\n   - `chat:write`\n   - `files:read`\n   - `files:write`\n5. In the same \"OAuth & Permissions\" page, scroll back up and click the button to install the app to your workspace.\n6. Note the \"Bot User OAuth Token\" (starts with `xoxb-`) that appears as we'll need it later\n7. Click on \"Socket Mode\" in the menu bar. When the page loads, click the toggle to \"Enable Socket Mode\"\n8. Give your token a name, such as `socket-token` and copy the token that is generated (starts with `xapp-`) as we'll need it later.\n9. Finally, go to the \"Event Subscription\" option in the menu bar. Click the toggle to \"Enable Events\" and subscribe to the `app_mention` bot event.\n\n2. Write a Slack bot\n\nLet's start by writing a very simple Slack bot, just to make sure that everything is working. Write the following Python code in a file called `bot.py`, pasting the two tokens from step 6 and step 8 in the previous section.\n\n```py\nfrom slack_bolt import App\nfrom slack_bolt.adapter.socket_mode import SocketModeHandler\n\nSLACK_BOT_TOKEN = PASTE YOUR SLACK BOT TOKEN HERE\nSLACK_APP_TOKEN = PASTE YOUR SLACK APP TOKEN HERE\n\napp = App(token=SLACK_BOT_TOKEN)\n\n@app.event(\"app_mention\")\ndef handle_app_mention_ev", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "eHandler\n\nSLACK_BOT_TOKEN = PASTE YOUR SLACK BOT TOKEN HERE\nSLACK_APP_TOKEN = PASTE YOUR SLACK APP TOKEN HERE\n\napp = App(token=SLACK_BOT_TOKEN)\n\n@app.event(\"app_mention\")\ndef handle_app_mention_events(body, say):\n    user_id = body[\"event\"][\"user\"]\n    say(f\"Hi <@{user_id}>! You mentioned me and said: {body['event']['text']}\")\n\nif __name__ == \"__main__\":\n    handler = SocketModeHandler(app, SLACK_APP_TOKEN)\n    handler.start()\n```\n\nIf that is working, we are ready to add Gradio-specific code. We will be using the [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) to query the Gradio Playground Space mentioned above. Here's the updated `bot.py` file:\n\n```python\nfrom slack_bolt import App\nfrom slack_bolt.adapter.socket_mode import SocketModeHandler\n\nSLACK_BOT_TOKEN = PASTE YOUR SLACK BOT TOKEN HERE\nSLACK_APP_TOKEN = PASTE YOUR SLACK APP TOKEN HERE\n\napp = App(token=SLACK_BOT_TOKEN)\ngradio_client = Client(\"abidlabs/gradio-playground-bot\")\n\ndef download_image(url, filename):\n    headers = {\"Authorization\": f\"Bearer {SLACK_BOT_TOKEN}\"}\n    response = httpx.get(url, headers=headers)\n    image_path = f\"./images/{filename}\"\n    os.makedirs(\"./images\", exist_ok=True)\n    with open(image_path, \"wb\") as f:\n        f.write(response.content)\n    return image_path\n\ndef slackify_message(message):   \n    Replace markdown links with slack format and remove code language specifier after triple backticks\n    pattern = r'\\[(.*?)\\]\\((.*?)\\)'\n    cleaned = re.sub(pattern, r'<\\2|\\1>', message)\n    cleaned = re.sub(r'```\\w+\\n', '```', cleaned)\n    return cleaned.strip()\n\n@app.event(\"app_mention\")\ndef handle_app_mention_events(body, say):\n    Extract the message content without the bot mention\n    text = body[\"event\"][\"text\"]\n    bot_user_id = body[\"authorizations\"][0][\"user_id\"]\n    clean_message = text.replace(f\"<@{bot_user_id}>\", \"\").strip()\n    \n    Handle images if present\n    files = []\n    if \"files\" in body[\"event\"]:\n        for", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "= body[\"authorizations\"][0][\"user_id\"]\n    clean_message = text.replace(f\"<@{bot_user_id}>\", \"\").strip()\n    \n    Handle images if present\n    files = []\n    if \"files\" in body[\"event\"]:\n        for file in body[\"event\"][\"files\"]:\n            if file[\"filetype\"] in [\"png\", \"jpg\", \"jpeg\", \"gif\", \"webp\"]:\n                image_path = download_image(file[\"url_private_download\"], file[\"name\"])\n                files.append(handle_file(image_path))\n                break\n    \n    Submit to Gradio and send responses back to Slack\n    for response in gradio_client.submit(\n        message={\"text\": clean_message, \"files\": files},\n    ):\n        cleaned_response = slackify_message(response[-1])\n        say(cleaned_response)\n\nif __name__ == \"__main__\":\n    handler = SocketModeHandler(app, SLACK_APP_TOKEN)\n    handler.start()\n```\n3. Add the bot to your Slack Workplace\n\nNow, create a new channel or navigate to an existing channel in your Slack workspace where you want to use the bot. Click the \"+\" button next to \"Channels\" in your Slack sidebar and follow the prompts to create a new channel.\n\nFinally, invite your bot to the channel:\n1. In your new channel, type `/invite @YourBotName`\n2. Select your bot from the dropdown\n3. Click \"Invite to Channel\"\n\n4. That's it!\n\nNow you can mention your bot in any channel it's in, optionally attach an image, and it will respond with generated Gradio app code!\n\nThe bot will:\n1. Listen for mentions\n2. Process any attached images\n3. Send the text and images to your Gradio app\n4. Stream the responses back to the Slack channel\n\nThis is just a basic example - you can extend it to handle more types of files, add error handling, or integrate with different Gradio apps!\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-19%20at%203.30.00%E2%80%AFPM.gif)\n\nIf you build a Slack bot from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gr", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "/main/gradio-guides/Screen%20Recording%202024-12-19%20at%203.30.00%E2%80%AFPM.gif)\n\nIf you build a Slack bot from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gradio), and we are happy to help you amplify!", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "Each message in Gradio's chatbot is a dataclass of type `ChatMessage` (this is assuming that chatbot's `type=\"message\"`, which is strongly recommended). The schema of `ChatMessage` is as follows:\n\n ```py\n@dataclass\nclass ChatMessage:\n    content: str | Component\n    role: Literal[\"user\", \"assistant\"]\n    metadata: MetadataDict = None\n    options: list[OptionDict] = None\n\nclass MetadataDict(TypedDict):\n    title: NotRequired[str]\n    id: NotRequired[int | str]\n    parent_id: NotRequired[int | str]\n    log: NotRequired[str]\n    duration: NotRequired[float]\n    status: NotRequired[Literal[\"pending\", \"done\"]]\n\nclass OptionDict(TypedDict):\n    label: NotRequired[str]\n    value: str\n ```\n\n\nFor our purposes, the most important key is the `metadata` key, which accepts a dictionary. If this dictionary includes a `title` for the message, it will be displayed in a collapsible accordion representing a thought. It's that simple! Take a look at this example:\n\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    chatbot = gr.Chatbot(\n        type=\"messages\",\n        value=[\n            gr.ChatMessage(\n                role=\"user\", \n                content=\"What is the weather in San Francisco?\"\n            ),\n            gr.ChatMessage(\n                role=\"assistant\", \n                content=\"I need to use the weather API tool?\",\n                metadata={\"title\":  \"\ud83e\udde0 Thinking\"}\n        ]\n    )\n\ndemo.launch()\n```\n\n\n\nIn addition to `title`, the dictionary provided to `metadata` can take several optional keys:\n\n* `log`: an optional string value to be displayed in a subdued font next to the thought title.\n* `duration`: an optional numeric value representing the duration of the thought/tool usage, in seconds. Displayed in a subdued font next inside parentheses next to the thought title.\n* `status`: if set to `\"pending\"`, a spinner appears next to the thought title and the accordion is initialized open.  If `status` is `\"done\"`, the thought accordion is initialized closed. I", "heading1": "The `ChatMessage` dataclass", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "ht title.\n* `status`: if set to `\"pending\"`, a spinner appears next to the thought title and the accordion is initialized open.  If `status` is `\"done\"`, the thought accordion is initialized closed. If `status` is not provided, the thought accordion is initialized open and no spinner is displayed.\n* `id` and `parent_id`: if these are provided, they can be used to nest thoughts inside other thoughts.\n\nBelow, we show several complete examples of using `gr.Chatbot` and `gr.ChatInterface` to display tool use or thinking UIs.\n\n", "heading1": "The `ChatMessage` dataclass", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "A real example using transformers.agents\n\nWe'll create a Gradio application simple agent that has access to a text-to-image tool.\n\nTip: Make sure you read the [smolagents documentation](https://huggingface.co/docs/smolagents/index) first\n\nWe'll start by importing the necessary classes from transformers and gradio. \n\n```python\nimport gradio as gr\nfrom gradio import ChatMessage\nfrom transformers import Tool, ReactCodeAgent  type: ignore\nfrom transformers.agents import stream_to_gradio, HfApiEngine  type: ignore\n\nImport tool from Hub\nimage_generation_tool = Tool.from_space(\n    space_id=\"black-forest-labs/FLUX.1-schnell\",\n    name=\"image_generator\",\n    description=\"Generates an image following your prompt. Returns a PIL Image.\",\n    api_name=\"/infer\",\n)\n\nllm_engine = HfApiEngine(\"Qwen/Qwen2.5-Coder-32B-Instruct\")\nInitialize the agent with both tools and engine\nagent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)\n```\n\nThen we'll build the UI:\n\n```python\ndef interact_with_agent(prompt, history):\n    messages = []\n    yield messages\n    for msg in stream_to_gradio(agent, prompt):\n        messages.append(asdict(msg))\n        yield messages\n    yield messages\n\n\ndemo = gr.ChatInterface(\n    interact_with_agent,\n    chatbot= gr.Chatbot(\n        label=\"Agent\",\n        type=\"messages\",\n        avatar_images=(\n            None,\n            \"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png\",\n        ),\n    ),\n    examples=[\n        [\"Generate an image of an astronaut riding an alligator\"],\n        [\"I am writing a children's book for my daughter. Can you help me with some illustrations?\"],\n    ],\n    type=\"messages\",\n)\n```\n\nYou can see the full demo code [here](https://huggingface.co/spaces/gradio/agent_chatbot/blob/main/app.py).\n\n\n![transformers_agent_code](https://github.com/freddyaboulton/freddyboulton/assets/41651716/c8d21336-e0e6-4878-88ea-e6fcfef3552d)\n\n\nA real example using langchain agents\n\nWe'll create a UI for l", "heading1": "Building with Agents", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "\n\n\n![transformers_agent_code](https://github.com/freddyaboulton/freddyboulton/assets/41651716/c8d21336-e0e6-4878-88ea-e6fcfef3552d)\n\n\nA real example using langchain agents\n\nWe'll create a UI for langchain agent that has access to a search engine.\n\nWe'll begin with imports and setting up the langchain agent. Note that you'll need an .env file with the following environment variables set - \n\n```\nSERPAPI_API_KEY=\nHF_TOKEN=\nOPENAI_API_KEY=\n```\n\n```python\nfrom langchain import hub\nfrom langchain.agents import AgentExecutor, create_openai_tools_agent, load_tools\nfrom langchain_openai import ChatOpenAI\nfrom gradio import ChatMessage\nimport gradio as gr\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nmodel = ChatOpenAI(temperature=0, streaming=True)\n\ntools = load_tools([\"serpapi\"])\n\nGet the prompt to use - you can modify this!\nprompt = hub.pull(\"hwchase17/openai-tools-agent\")\nagent = create_openai_tools_agent(\n    model.with_config({\"tags\": [\"agent_llm\"]}), tools, prompt\n)\nagent_executor = AgentExecutor(agent=agent, tools=tools).with_config(\n    {\"run_name\": \"Agent\"}\n)\n```\n\nThen we'll create the Gradio UI\n\n```python\nasync def interact_with_langchain_agent(prompt, messages):\n    messages.append(ChatMessage(role=\"user\", content=prompt))\n    yield messages\n    async for chunk in agent_executor.astream(\n        {\"input\": prompt}\n    ):\n        if \"steps\" in chunk:\n            for step in chunk[\"steps\"]:\n                messages.append(ChatMessage(role=\"assistant\", content=step.action.log,\n                                  metadata={\"title\": f\"\ud83d\udee0\ufe0f Used tool {step.action.tool}\"}))\n                yield messages\n        if \"output\" in chunk:\n            messages.append(ChatMessage(role=\"assistant\", content=chunk[\"output\"]))\n            yield messages\n\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with a LangChain Agent \ud83e\udd9c\u26d3\ufe0f and see its thoughts \ud83d\udcad\")\n    chatbot = gr.Chatbot(\n        type=\"messages\",\n        label=\"Agent\",\n        avatar_images=(\n            None,\n         ", "heading1": "Building with Agents", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "   gr.Markdown(\"Chat with a LangChain Agent \ud83e\udd9c\u26d3\ufe0f and see its thoughts \ud83d\udcad\")\n    chatbot = gr.Chatbot(\n        type=\"messages\",\n        label=\"Agent\",\n        avatar_images=(\n            None,\n            \"https://em-content.zobj.net/source/twitter/141/parrot_1f99c.png\",\n        ),\n    )\n    input = gr.Textbox(lines=1, label=\"Chat Message\")\n    input.submit(interact_with_langchain_agent, [input_2, chatbot_2], [chatbot_2])\n\ndemo.launch()\n```\n\n![langchain_agent_code](https://github.com/freddyaboulton/freddyboulton/assets/41651716/762283e5-3937-47e5-89e0-79657279ea67)\n\nThat's it! See our finished langchain demo [here](https://huggingface.co/spaces/gradio/langchain-agent).\n\n\n", "heading1": "Building with Agents", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "The Gradio Chatbot can natively display intermediate thoughts of a _thinking_ LLM. This makes it perfect for creating UIs that show how an AI model \"thinks\" while generating responses. Below guide will show you how to build a chatbot that displays Gemini AI's thought process in real-time.\n\n\nA real example using Gemini 2.0 Flash Thinking API\n\nLet's create a complete chatbot that shows its thoughts and responses in real-time. We'll use Google's Gemini API for accessing Gemini 2.0 Flash Thinking LLM and Gradio for the UI.\n\nWe'll begin with imports and setting up the gemini client. Note that you'll need to [acquire a Google Gemini API key](https://aistudio.google.com/apikey) first -\n\n```python\nimport gradio as gr\nfrom gradio import ChatMessage\nfrom typing import Iterator\nimport google.generativeai as genai\n\ngenai.configure(api_key=\"your-gemini-api-key\")\nmodel = genai.GenerativeModel(\"gemini-2.0-flash-thinking-exp-1219\")\n```\n\nFirst, let's set up our streaming function that handles the model's output:\n\n```python\ndef stream_gemini_response(user_message: str, messages: list) -> Iterator[list]:\n    \"\"\"\n    Streams both thoughts and responses from the Gemini model.\n    \"\"\"\n    Initialize response from Gemini\n    response = model.generate_content(user_message, stream=True)\n    \n    Initialize buffers\n    thought_buffer = \"\"\n    response_buffer = \"\"\n    thinking_complete = False\n    \n    Add initial thinking message\n    messages.append(\n        ChatMessage(\n            role=\"assistant\",\n            content=\"\",\n            metadata={\"title\": \"\u23f3Thinking: *The thoughts produced by the Gemini2.0 Flash model are experimental\"}\n        )\n    )\n    \n    for chunk in response:\n        parts = chunk.candidates[0].content.parts\n        current_chunk = parts[0].text\n        \n        if len(parts) == 2 and not thinking_complete:\n            Complete thought and start response\n            thought_buffer += current_chunk\n            messages[-1] = ChatMessage(\n                rol", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": " if len(parts) == 2 and not thinking_complete:\n            Complete thought and start response\n            thought_buffer += current_chunk\n            messages[-1] = ChatMessage(\n                role=\"assistant\",\n                content=thought_buffer,\n                metadata={\"title\": \"\u23f3Thinking: *The thoughts produced by the Gemini2.0 Flash model are experimental\"}\n            )\n            \n            Add response message\n            messages.append(\n                ChatMessage(\n                    role=\"assistant\",\n                    content=parts[1].text\n                )\n            )\n            thinking_complete = True\n            \n        elif thinking_complete:\n            Continue streaming response\n            response_buffer += current_chunk\n            messages[-1] = ChatMessage(\n                role=\"assistant\",\n                content=response_buffer\n            )\n            \n        else:\n            Continue streaming thoughts\n            thought_buffer += current_chunk\n            messages[-1] = ChatMessage(\n                role=\"assistant\",\n                content=thought_buffer,\n                metadata={\"title\": \"\u23f3Thinking: *The thoughts produced by the Gemini2.0 Flash model are experimental\"}\n            )\n        \n        yield messages\n```\n\nThen, let's create the Gradio interface:\n\n```python\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with Gemini 2.0 Flash and See its Thoughts \ud83d\udcad\")\n    \n    chatbot = gr.Chatbot(\n        type=\"messages\",\n        label=\"Gemini2.0 'Thinking' Chatbot\",\n        render_markdown=True,\n    )\n    \n    input_box = gr.Textbox(\n        lines=1,\n        label=\"Chat Message\",\n        placeholder=\"Type your message here and press Enter...\"\n    )\n    \n    Set up event handlers\n    msg_store = gr.State(\"\")  Store for preserving user message\n    \n    input_box.submit(\n        lambda msg: (msg, msg, \"\"),  Store message and clear input\n        inputs=[input_box],\n        outputs=[msg_store, input_box, inp", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "Store for preserving user message\n    \n    input_box.submit(\n        lambda msg: (msg, msg, \"\"),  Store message and clear input\n        inputs=[input_box],\n        outputs=[msg_store, input_box, input_box],\n        queue=False\n    ).then(\n        user_message,  Add user message to chat\n        inputs=[msg_store, chatbot],\n        outputs=[input_box, chatbot],\n        queue=False\n    ).then(\n        stream_gemini_response,  Generate and stream response\n        inputs=[msg_store, chatbot],\n        outputs=chatbot\n    )\n\ndemo.launch()\n```\n\nThis creates a chatbot that:\n\n- Displays the model's thoughts in a collapsible section\n- Streams the thoughts and final response in real-time\n- Maintains a clean chat history\n\n That's it! You now have a chatbot that not only responds to users but also shows its thinking process, creating a more transparent and engaging interaction. See our finished Gemini 2.0 Flash Thinking demo [here](https://huggingface.co/spaces/ysharma/Gemini2-Flash-Thinking).\n\n\n Building with Citations \n\nThe Gradio Chatbot can display citations from LLM responses, making it perfect for creating UIs that show source documentation and references. This guide will show you how to build a chatbot that displays Claude's citations in real-time.\n\nA real example using Anthropic's Citations API\nLet's create a complete chatbot that shows both responses and their supporting citations. We'll use Anthropic's Claude API with citations enabled and Gradio for the UI.\n\nWe'll begin with imports and setting up the Anthropic client. Note that you'll need an `ANTHROPIC_API_KEY` environment variable set:\n\n```python\nimport gradio as gr\nimport anthropic\nimport base64\nfrom typing import List, Dict, Any\n\nclient = anthropic.Anthropic()\n```\n\nFirst, let's set up our message formatting functions that handle document preparation:\n\n```python\ndef encode_pdf_to_base64(file_obj) -> str:\n    \"\"\"Convert uploaded PDF file to base64 string.\"\"\"\n    if file_obj is None:\n        return None\n", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "ng functions that handle document preparation:\n\n```python\ndef encode_pdf_to_base64(file_obj) -> str:\n    \"\"\"Convert uploaded PDF file to base64 string.\"\"\"\n    if file_obj is None:\n        return None\n    with open(file_obj.name, 'rb') as f:\n        return base64.b64encode(f.read()).decode('utf-8')\n\ndef format_message_history(\n    history: list, \n    enable_citations: bool,\n    doc_type: str,\n    text_input: str,\n    pdf_file: str\n) -> List[Dict]:\n    \"\"\"Convert Gradio chat history to Anthropic message format.\"\"\"\n    formatted_messages = []\n    \n    Add previous messages\n    for msg in history[:-1]:\n        if msg[\"role\"] == \"user\":\n            formatted_messages.append({\"role\": \"user\", \"content\": msg[\"content\"]})\n    \n    Prepare the latest message with document\n    latest_message = {\"role\": \"user\", \"content\": []}\n    \n    if enable_citations:\n        if doc_type == \"plain_text\":\n            latest_message[\"content\"].append({\n                \"type\": \"document\",\n                \"source\": {\n                    \"type\": \"text\",\n                    \"media_type\": \"text/plain\",\n                    \"data\": text_input.strip()\n                },\n                \"title\": \"Text Document\",\n                \"citations\": {\"enabled\": True}\n            })\n        elif doc_type == \"pdf\" and pdf_file:\n            pdf_data = encode_pdf_to_base64(pdf_file)\n            if pdf_data:\n                latest_message[\"content\"].append({\n                    \"type\": \"document\",\n                    \"source\": {\n                        \"type\": \"base64\",\n                        \"media_type\": \"application/pdf\",\n                        \"data\": pdf_data\n                    },\n                    \"title\": pdf_file.name,\n                    \"citations\": {\"enabled\": True}\n                })\n    \n    Add the user's question\n    latest_message[\"content\"].append({\"type\": \"text\", \"text\": history[-1][\"content\"]})\n    \n    formatted_messages.append(latest_message)\n    return formatted_messages\n```\n\nThen, ", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": " the user's question\n    latest_message[\"content\"].append({\"type\": \"text\", \"text\": history[-1][\"content\"]})\n    \n    formatted_messages.append(latest_message)\n    return formatted_messages\n```\n\nThen, let's create our bot response handler that processes citations:\n\n```python\ndef bot_response(\n    history: list,\n    enable_citations: bool,\n    doc_type: str,\n    text_input: str,\n    pdf_file: str\n) -> List[Dict[str, Any]]:\n    try:\n        messages = format_message_history(history, enable_citations, doc_type, text_input, pdf_file)\n        response = client.messages.create(model=\"claude-3-5-sonnet-20241022\", max_tokens=1024, messages=messages)\n        \n        Initialize main response and citations\n        main_response = \"\"\n        citations = []\n        \n        Process each content block\n        for block in response.content:\n            if block.type == \"text\":\n                main_response += block.text\n                if enable_citations and hasattr(block, 'citations') and block.citations:\n                    for citation in block.citations:\n                        if citation.cited_text not in citations:\n                            citations.append(citation.cited_text)\n        \n        Add main response\n        history.append({\"role\": \"assistant\", \"content\": main_response})\n        \n        Add citations in a collapsible section\n        if enable_citations and citations:\n            history.append({\n                \"role\": \"assistant\",\n                \"content\": \"\\n\".join([f\"\u2022 {cite}\" for cite in citations]),\n                \"metadata\": {\"title\": \"\ud83d\udcda Citations\"}\n            })\n        \n        return history\n            \n    except Exception as e:\n        history.append({\n            \"role\": \"assistant\",\n            \"content\": \"I apologize, but I encountered an error while processing your request.\"\n        })\n        return history\n```\n\nFinally, let's create the Gradio interface:\n\n```python\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with Citations\"", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "an error while processing your request.\"\n        })\n        return history\n```\n\nFinally, let's create the Gradio interface:\n\n```python\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with Citations\")\n    \n    with gr.Row(scale=1):\n        with gr.Column(scale=4):\n            chatbot = gr.Chatbot(type=\"messages\", bubble_full_width=False, show_label=False, scale=1)\n            msg = gr.Textbox(placeholder=\"Enter your message here...\", show_label=False, container=False)\n            \n        with gr.Column(scale=1):\n            enable_citations = gr.Checkbox(label=\"Enable Citations\", value=True, info=\"Toggle citation functionality\" )\n            doc_type_radio = gr.Radio( choices=[\"plain_text\", \"pdf\"], value=\"plain_text\", label=\"Document Type\", info=\"Choose the type of document to use\")\n            text_input = gr.Textbox(label=\"Document Content\", lines=10, info=\"Enter the text you want to reference\")\n            pdf_input = gr.File(label=\"Upload PDF\", file_types=[\".pdf\"], file_count=\"single\", visible=False)\n    \n    Handle message submission\n    msg.submit(\n        user_message,\n        [msg, chatbot, enable_citations, doc_type_radio, text_input, pdf_input],\n        [msg, chatbot]\n    ).then(\n        bot_response,\n        [chatbot, enable_citations, doc_type_radio, text_input, pdf_input],\n        chatbot\n    )\n\ndemo.launch()\n```\n\nThis creates a chatbot that:\n- Supports both plain text and PDF documents for Claude to cite from \n- Displays Citations in collapsible sections using our `metadata` feature\n- Shows source quotes directly from the given documents\n\nThe citations feature works particularly well with the Gradio Chatbot's `metadata` support, allowing us to create collapsible sections that keep the chat interface clean while still providing easy access to source documentation.\n\nThat's it! You now have a chatbot that not only responds to users but also shows its sources, creating a more transparent and trustworthy interaction. See our finished Citations demo [her", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "umentation.\n\nThat's it! You now have a chatbot that not only responds to users but also shows its sources, creating a more transparent and trustworthy interaction. See our finished Citations demo [here](https://huggingface.co/spaces/ysharma/anthropic-citations-with-gradio-metadata-key).\n\n", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "First, we'll build the UI without handling these events and build from there. \nWe'll use the Hugging Face InferenceClient in order to get started without setting up\nany API keys.\n\nThis is what the first draft of our application looks like:\n\n```python\nfrom huggingface_hub import InferenceClient\nimport gradio as gr\n\nclient = InferenceClient()\n\ndef respond(\n    prompt: str,\n    history,\n):\n    if not history:\n        history = [{\"role\": \"system\", \"content\": \"You are a friendly chatbot\"}]\n    history.append({\"role\": \"user\", \"content\": prompt})\n\n    yield history\n\n    response = {\"role\": \"assistant\", \"content\": \"\"}\n    for message in client.chat_completion(\n        history,\n        temperature=0.95,\n        top_p=0.9,\n        max_tokens=512,\n        stream=True,\n        model=\"HuggingFaceH4/zephyr-7b-beta\"\n    ):\n        response[\"content\"] += message.choices[0].delta.content or \"\"\n\n        yield history + [response]\n\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with Hugging Face Zephyr 7b \ud83e\udd17\")\n    chatbot = gr.Chatbot(\n        label=\"Agent\",\n        type=\"messages\",\n        avatar_images=(\n            None,\n            \"https://em-content.zobj.net/source/twitter/376/hugging-face_1f917.png\",\n        ),\n    )\n    prompt = gr.Textbox(max_lines=1, label=\"Chat Message\")\n    prompt.submit(respond, [prompt, chatbot], [chatbot])\n    prompt.submit(lambda: \"\", None, [prompt])\n\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\n", "heading1": "The UI", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "Our undo event will populate the textbox with the previous user message and also remove all subsequent assistant responses.\n\nIn order to know the index of the last user message, we can pass `gr.UndoData` to our event handler function like so:\n\n```python\ndef handle_undo(history, undo_data: gr.UndoData):\n    return history[:undo_data.index], history[undo_data.index]['content']\n```\n\nWe then pass this function to the `undo` event!\n\n```python\n    chatbot.undo(handle_undo, chatbot, [chatbot, prompt])\n```\n\nYou'll notice that every bot response will now have an \"undo icon\" you can use to undo the response - \n\n![undo_event](https://github.com/user-attachments/assets/180b5302-bc4a-4c3e-903c-f14ec2adcaa6)\n\nTip: You can also access the content of the user message with `undo_data.value`\n\n", "heading1": "The Undo Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "The retry event will work similarly. We'll use `gr.RetryData` to get the index of the previous user message and remove all the subsequent messages from the history. Then we'll use the `respond` function to generate a new response. We could also get the previous prompt via the `value` property of `gr.RetryData`.\n\n```python\ndef handle_retry(history, retry_data: gr.RetryData):\n    new_history = history[:retry_data.index]\n    previous_prompt = history[retry_data.index]['content']\n    yield from respond(previous_prompt, new_history)\n\n...\n\nchatbot.retry(handle_retry, chatbot, chatbot)\n```\n\nYou'll see that the bot messages have a \"retry\" icon now -\n\n![retry_event](https://github.com/user-attachments/assets/cec386a7-c4cd-4fb3-a2d7-78fd806ceac6)\n\nTip: The Hugging Face inference API caches responses, so in this demo, the retry button will not generate a new response.\n\n", "heading1": "The Retry Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "By now you should hopefully be seeing the pattern!\nTo let users like a message, we'll add a `.like` event to our chatbot.\nWe'll pass it a function that accepts a `gr.LikeData` object.\nIn this case, we'll just print the message that was either liked or disliked.\n\n```python\ndef handle_like(data: gr.LikeData):\n    if data.liked:\n        print(\"You upvoted this response: \", data.value)\n    else:\n        print(\"You downvoted this response: \", data.value)\n\n...\n\nchatbot.like(vote, None, None)\n```\n\n", "heading1": "The Like Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "Same idea with the edit listener! with `gr.Chatbot(editable=True)`, you can capture user edits. The `gr.EditData` object tells us the index of the message edited and the new text of the mssage. Below, we use this object to edit the history, and delete any subsequent messages. \n\n```python\ndef handle_edit(history, edit_data: gr.EditData):\n    new_history = history[:edit_data.index]\n    new_history[-1]['content'] = edit_data.value\n    return new_history\n\n...\n\nchatbot.edit(handle_edit, chatbot, chatbot)\n```\n\n", "heading1": "The Edit Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "As a bonus, we'll also cover the `.clear()` event, which is triggered when the user clicks the clear icon to clear all messages. As a developer, you can attach additional events that should happen when this icon is clicked, e.g. to handle clearing of additional chatbot state:\n\n```python\nfrom uuid import uuid4\nimport gradio as gr\n\n\ndef clear():\n    print(\"Cleared uuid\")\n    return uuid4()\n\n\ndef chat_fn(user_input, history, uuid):\n    return f\"{user_input} with uuid {uuid}\"\n\n\nwith gr.Blocks() as demo:\n    uuid_state = gr.State(\n        uuid4\n    )\n    chatbot = gr.Chatbot(type=\"messages\")\n    chatbot.clear(clear, outputs=[uuid_state])\n\n    gr.ChatInterface(\n        chat_fn,\n        additional_inputs=[uuid_state],\n        chatbot=chatbot,\n        type=\"messages\"\n    )\n\ndemo.launch()\n```\n\nIn this example, the `clear` function, bound to the `chatbot.clear` event, returns a new UUID into our session state, when the chat history is cleared via the trash icon. This can be seen in the `chat_fn` function, which references the UUID saved in our session state.\n\nThis example also shows that you can use these events with `gr.ChatInterface` by passing in a custom `gr.Chatbot` object.\n\n", "heading1": "The Clear Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "That's it! You now know how you can implement the retry, undo, like, and clear events for the Chatbot.\n\n\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "Chatbots are a popular application of large language models (LLMs). Using Gradio, you can easily build a chat application and share that with your users, or try it yourself using an intuitive UI.\n\nThis tutorial uses `gr.ChatInterface()`, which is a high-level abstraction that allows you to create your chatbot UI fast, often with a _few lines of Python_. It can be easily adapted to support multimodal chatbots, or chatbots that require further customization.\n\n**Prerequisites**: please make sure you are using the latest version of Gradio:\n\n```bash\n$ pip install --upgrade gradio\n```\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "If you have a chat server serving an OpenAI-API compatible endpoint (such as Ollama), you can spin up a ChatInterface in a single line of Python. First, also run `pip install openai`. Then, with your own URL, model, and optional token:\n\n```python\nimport gradio as gr\n\ngr.load_chat(\"http://localhost:11434/v1/\", model=\"llama3.2\", token=\"***\").launch()\n```\n\nRead about `gr.load_chat` in [the docs](https://www.gradio.app/docs/gradio/load_chat). If you have your own model, keep reading to see how to create an application around any chat model in Python!\n\n", "heading1": "Note for OpenAI-API compatible endpoints", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "To create a chat application with `gr.ChatInterface()`, the first thing you should do is define your **chat function**. In the simplest case, your chat function should accept two arguments: `message` and `history` (the arguments can be named anything, but must be in this order).\n\n- `message`: a `str` representing the user's most recent message.\n- `history`: a list of openai-style dictionaries with `role` and `content` keys, representing the previous conversation history. May also include additional keys representing message metadata.\n\nFor example, the `history` could look like this:\n\n```python\n[\n    {\"role\": \"user\", \"content\": \"What is the capital of France?\"},\n    {\"role\": \"assistant\", \"content\": \"Paris\"}\n]\n```\n\nwhile the next `message` would be:\n\n```py\n\"And what is its largest city?\"\n```\n\nYour chat function simply needs to return: \n\n* a `str` value, which is the chatbot's response based on the chat `history` and most recent `message`, for example, in this case:\n\n```\nParis is also the largest city.\n```\n\nLet's take a look at a few example chat functions:\n\n**Example: a chatbot that randomly responds with yes or no**\n\nLet's write a chat function that responds `Yes` or `No` randomly.\n\nHere's our chat function:\n\n```python\nimport random\n\ndef random_response(message, history):\n    return random.choice([\"Yes\", \"No\"])\n```\n\nNow, we can plug this into `gr.ChatInterface()` and call the `.launch()` method to create the web interface:\n\n```python\nimport gradio as gr\n\ngr.ChatInterface(\n    fn=random_response, \n    type=\"messages\"\n).launch()\n```\n\nTip: Always set type=\"messages\" in gr.ChatInterface. The default value (type=\"tuples\") is deprecated and will be removed in a future version of Gradio.\n\nThat's it! Here's our running demo, try it out:\n\n$demo_chatinterface_random_response\n\n**Example: a chatbot that alternates between agreeing and disagreeing**\n\nOf course, the previous example was very simplistic, it didn't take user input or the previous history into account! Here's another", "heading1": "Defining a chat function", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "ample: a chatbot that alternates between agreeing and disagreeing**\n\nOf course, the previous example was very simplistic, it didn't take user input or the previous history into account! Here's another simple example showing how to incorporate a user's input as well as the history.\n\n```python\nimport gradio as gr\n\ndef alternatingly_agree(message, history):\n    if len([h for h in history if h['role'] == \"assistant\"]) % 2 == 0:\n        return f\"Yes, I do think that: {message}\"\n    else:\n        return \"I don't think so\"\n\ngr.ChatInterface(\n    fn=alternatingly_agree, \n    type=\"messages\"\n).launch()\n```\n\nWe'll look at more realistic examples of chat functions in our next Guide, which shows [examples of using `gr.ChatInterface` with popular LLMs](../guides/chatinterface-examples). \n\n", "heading1": "Defining a chat function", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "In your chat function, you can use `yield` to generate a sequence of partial responses, each replacing the previous ones. This way, you'll end up with a streaming chatbot. It's that simple!\n\n```python\nimport time\nimport gradio as gr\n\ndef slow_echo(message, history):\n    for i in range(len(message)):\n        time.sleep(0.3)\n        yield \"You typed: \" + message[: i+1]\n\ngr.ChatInterface(\n    fn=slow_echo, \n    type=\"messages\"\n).launch()\n```\n\nWhile the response is streaming, the \"Submit\" button turns into a \"Stop\" button that can be used to stop the generator function.\n\nTip: Even though you are yielding the latest message at each iteration, Gradio only sends the \"diff\" of each message from the server to the frontend, which reduces latency and data consumption over your network.\n\n", "heading1": "Streaming chatbots", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "If you're familiar with Gradio's `gr.Interface` class, the `gr.ChatInterface` includes many of the same arguments that you can use to customize the look and feel of your Chatbot. For example, you can:\n\n- add a title and description above your chatbot using `title` and `description` arguments.\n- add a theme or custom css using `theme` and `css` arguments respectively.\n- add `examples` and even enable `cache_examples`, which make your Chatbot easier for users to try it out.\n- customize the chatbot (e.g. to change the height or add a placeholder) or textbox (e.g. to add a max number of characters or add a placeholder).\n\n**Adding examples**\n\nYou can add preset examples to your `gr.ChatInterface` with the `examples` parameter, which takes a list of string examples. Any examples will appear as \"buttons\" within the Chatbot before any messages are sent. If you'd like to include images or other files as part of your examples, you can do so by using this dictionary format for each example instead of a string: `{\"text\": \"What's in this image?\", \"files\": [\"cheetah.jpg\"]}`. Each file will be a separate message that is added to your Chatbot history.\n\nYou can change the displayed text for each example by using the `example_labels` argument. You can add icons to each example as well using the `example_icons` argument. Both of these arguments take a list of strings, which should be the same length as the `examples` list.\n\nIf you'd like to cache the examples so that they are pre-computed and the results appear instantly, set `cache_examples=True`.\n\n**Customizing the chatbot or textbox component**\n\nIf you want to customize the `gr.Chatbot` or `gr.Textbox` that compose the `ChatInterface`, then you can pass in your own chatbot or textbox components. Here's an example of how we to apply the parameters we've discussed in this section:\n\n```python\nimport gradio as gr\n\ndef yes_man(message, history):\n    if message.endswith(\"?\"):\n        return \"Yes\"\n    else:\n        return \"Ask me anything", "heading1": "Customizing the Chat UI", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": " parameters we've discussed in this section:\n\n```python\nimport gradio as gr\n\ndef yes_man(message, history):\n    if message.endswith(\"?\"):\n        return \"Yes\"\n    else:\n        return \"Ask me anything!\"\n\ngr.ChatInterface(\n    yes_man,\n    type=\"messages\",\n    chatbot=gr.Chatbot(height=300),\n    textbox=gr.Textbox(placeholder=\"Ask me a yes or no question\", container=False, scale=7),\n    title=\"Yes Man\",\n    description=\"Ask Yes Man any question\",\n    theme=\"ocean\",\n    examples=[\"Hello\", \"Am I cool?\", \"Are tomatoes vegetables?\"],\n    cache_examples=True,\n).launch()\n```\n\nHere's another example that adds a \"placeholder\" for your chat interface, which appears before the user has started chatting. The `placeholder` argument of `gr.Chatbot` accepts Markdown or HTML:\n\n```python\ngr.ChatInterface(\n    yes_man,\n    type=\"messages\",\n    chatbot=gr.Chatbot(placeholder=\"<strong>Your Personal Yes-Man</strong><br>Ask Me Anything\"),\n...\n```\n\nThe placeholder appears vertically and horizontally centered in the chatbot.\n\n", "heading1": "Customizing the Chat UI", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "You may want to add multimodal capabilities to your chat interface. For example, you may want users to be able to upload images or files to your chatbot and ask questions about them. You can make your chatbot \"multimodal\" by passing in a single parameter (`multimodal=True`) to the `gr.ChatInterface` class.\n\nWhen `multimodal=True`, the signature of your chat function changes slightly: the first parameter of your function (what we referred to as `message` above) should accept a dictionary consisting of the submitted text and uploaded files that looks like this: \n\n```py\n{\n    \"text\": \"user input\", \n    \"files\": [\n        \"updated_file_1_path.ext\",\n        \"updated_file_2_path.ext\", \n        ...\n    ]\n}\n```\n\nThis second parameter of your chat function, `history`, will be in the same openai-style dictionary format as before. However, if the history contains uploaded files, the `content` key for a file will be not a string, but rather a single-element tuple consisting of the filepath. Each file will be a separate message in the history. So after uploading two files and asking a question, your history might look like this:\n\n```python\n[\n    {\"role\": \"user\", \"content\": (\"cat1.png\")},\n    {\"role\": \"user\", \"content\": (\"cat2.png\")},\n    {\"role\": \"user\", \"content\": \"What's the difference between these two images?\"},\n]\n```\n\nThe return type of your chat function does *not change* when setting `multimodal=True` (i.e. in the simplest case, you should still return a string value). We discuss more complex cases, e.g. returning files [below](returning-complex-responses).\n\nIf you are customizing a multimodal chat interface, you should pass in an instance of `gr.MultimodalTextbox` to the `textbox` parameter. You can customize the `MultimodalTextbox` further by passing in the `sources` parameter, which is a list of sources to enable. Here's an example that illustrates how to set up and customize and multimodal chat interface:\n \n\n```python\nimport gradio as gr\n\ndef count_images(message, hi", "heading1": "Multimodal Chat Interface", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "eter, which is a list of sources to enable. Here's an example that illustrates how to set up and customize and multimodal chat interface:\n \n\n```python\nimport gradio as gr\n\ndef count_images(message, history):\n    num_images = len(message[\"files\"])\n    total_images = 0\n    for message in history:\n        if isinstance(message[\"content\"], tuple):\n            total_images += 1\n    return f\"You just uploaded {num_images} images, total uploaded: {total_images+num_images}\"\n\ndemo = gr.ChatInterface(\n    fn=count_images, \n    type=\"messages\", \n    examples=[\n        {\"text\": \"No files\", \"files\": []}\n    ], \n    multimodal=True,\n    textbox=gr.MultimodalTextbox(file_count=\"multiple\", file_types=[\"image\"], sources=[\"upload\", \"microphone\"])\n)\n\ndemo.launch()\n```\n\n", "heading1": "Multimodal Chat Interface", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "You may want to add additional inputs to your chat function and expose them to your users through the chat UI. For example, you could add a textbox for a system prompt, or a slider that sets the number of tokens in the chatbot's response. The `gr.ChatInterface` class supports an `additional_inputs` parameter which can be used to add additional input components.\n\nThe `additional_inputs` parameters accepts a component or a list of components. You can pass the component instances directly, or use their string shortcuts (e.g. `\"textbox\"` instead of `gr.Textbox()`). If you pass in component instances, and they have _not_ already been rendered, then the components will appear underneath the chatbot within a `gr.Accordion()`. \n\nHere's a complete example:\n\n$code_chatinterface_system_prompt\n\nIf the components you pass into the `additional_inputs` have already been rendered in a parent `gr.Blocks()`, then they will _not_ be re-rendered in the accordion. This provides flexibility in deciding where to lay out the input components. In the example below, we position the `gr.Textbox()` on top of the Chatbot UI, while keeping the slider underneath.\n\n```python\nimport gradio as gr\nimport time\n\ndef echo(message, history, system_prompt, tokens):\n    response = f\"System prompt: {system_prompt}\\n Message: {message}.\"\n    for i in range(min(len(response), int(tokens))):\n        time.sleep(0.05)\n        yield response[: i+1]\n\nwith gr.Blocks() as demo:\n    system_prompt = gr.Textbox(\"You are helpful AI.\", label=\"System Prompt\")\n    slider = gr.Slider(10, 100, render=False)\n\n    gr.ChatInterface(\n        echo, additional_inputs=[system_prompt, slider], type=\"messages\"\n    )\n\ndemo.launch()\n```\n\n**Examples with additional inputs**\n\nYou can also add example values for your additional inputs. Pass in a list of lists to the `examples` parameter, where each inner list represents one sample, and each inner list should be `1 + len(additional_inputs)` long. The first element in the inner list should ", "heading1": "Additional Inputs", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "n a list of lists to the `examples` parameter, where each inner list represents one sample, and each inner list should be `1 + len(additional_inputs)` long. The first element in the inner list should be the example value for the chat message, and each subsequent element should be an example value for one of the additional inputs, in order. When additional inputs are provided, examples are rendered in a table underneath the chat interface.\n\nIf you need to create something even more custom, then its best to construct the chatbot UI using the low-level `gr.Blocks()` API. We have [a dedicated guide for that here](/guides/creating-a-custom-chatbot-with-blocks).\n\n", "heading1": "Additional Inputs", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "In the same way that you can accept additional inputs into your chat function, you can also return additional outputs. Simply pass in a list of components to the `additional_outputs` parameter in `gr.ChatInterface` and return additional values for each component from your chat function. Here's an example that extracts code and outputs it into a separate `gr.Code` component:\n\n$code_chatinterface_artifacts\n\n**Note:** unlike the case of additional inputs, the components passed in `additional_outputs` must be already defined in your `gr.Blocks` context -- they are not rendered automatically. If you need to render them after your `gr.ChatInterface`, you can set `render=False` when they are first defined and then `.render()` them in the appropriate section of your `gr.Blocks()` as we do in the example above.\n\n", "heading1": "Additional Outputs", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "We mentioned earlier that in the simplest case, your chat function should return a `str` response, which will be rendered as Markdown in the chatbot. However, you can also return more complex responses as we discuss below:\n\n\n**Returning files or Gradio components**\n\nCurrently, the following Gradio components can be displayed inside the chat interface:\n* `gr.Image`\n* `gr.Plot`\n* `gr.Audio`\n* `gr.HTML`\n* `gr.Video`\n* `gr.Gallery`\n* `gr.File`\n\nSimply return one of these components from your function to use it with `gr.ChatInterface`. Here's an example that returns an audio file:\n\n```py\nimport gradio as gr\n\ndef music(message, history):\n    if message.strip():\n        return gr.Audio(\"https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav\")\n    else:\n        return \"Please provide the name of an artist\"\n\ngr.ChatInterface(\n    music,\n    type=\"messages\",\n    textbox=gr.Textbox(placeholder=\"Which artist's music do you want to listen to?\", scale=7),\n).launch()\n```\n\nSimilarly, you could return image files with `gr.Image`, video files with `gr.Video`, or arbitrary files with the `gr.File` component.\n\n**Returning Multiple Messages**\n\nYou can return multiple assistant messages from your chat function simply by returning a `list` of messages, each of which is a valid chat type. This lets you, for example, send a message along with files, as in the following example:\n\n$code_chatinterface_echo_multimodal\n\n\n**Displaying intermediate thoughts or tool usage**\n\nThe `gr.ChatInterface` class supports displaying intermediate thoughts or tool usage direct in the chatbot.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/nested-thought.png)\n\n To do this, you will need to return a `gr.ChatMessage` object from your chat function. Here is the schema of the `gr.ChatMessage` data class as well as two internal typed dictionaries:\n \n ```py\n@dataclass\nclass ChatMessage:\n    content: str | Component\n    metadata: MetadataDict = ", "heading1": "Returning Complex Responses", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "ion. Here is the schema of the `gr.ChatMessage` data class as well as two internal typed dictionaries:\n \n ```py\n@dataclass\nclass ChatMessage:\n    content: str | Component\n    metadata: MetadataDict = None\n    options: list[OptionDict] = None\n\nclass MetadataDict(TypedDict):\n    title: NotRequired[str]\n    id: NotRequired[int | str]\n    parent_id: NotRequired[int | str]\n    log: NotRequired[str]\n    duration: NotRequired[float]\n    status: NotRequired[Literal[\"pending\", \"done\"]]\n\nclass OptionDict(TypedDict):\n    label: NotRequired[str]\n    value: str\n ```\n \nAs you can see, the `gr.ChatMessage` dataclass is similar to the openai-style message format, e.g. it has a \"content\" key that refers to the chat message content. But it also includes a \"metadata\" key whose value is a dictionary. If this dictionary includes a \"title\" key, the resulting message is displayed as an intermediate thought with the title being displayed on top of the thought. Here's an example showing the usage:\n\n$code_chatinterface_thoughts\n\nYou can even show nested thoughts, which is useful for agent demos in which one tool may call other tools. To display nested thoughts, include \"id\" and \"parent_id\" keys in the \"metadata\" dictionary. Read our [dedicated guide on displaying intermediate thoughts and tool usage](/guides/agents-and-tool-usage) for more realistic examples.\n\n**Providing preset responses**\n\nWhen returning an assistant message, you may want to provide preset options that a user can choose in response. To do this, again, you will again return a `gr.ChatMessage` instance from your chat function. This time, make sure to set the `options` key specifying the preset responses.\n\nAs shown in the schema for `gr.ChatMessage` above, the value corresponding to the `options` key should be a list of dictionaries, each with a `value` (a string that is the value that should be sent to the chat function when this response is clicked) and an optional `label` (if provided, is the text displayed as the preset r", "heading1": "Returning Complex Responses", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "ies, each with a `value` (a string that is the value that should be sent to the chat function when this response is clicked) and an optional `label` (if provided, is the text displayed as the preset response instead of the `value`). \n\nThis example illustrates how to use preset responses:\n\n$code_chatinterface_options\n\n", "heading1": "Returning Complex Responses", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "You may wish to modify the value of the chatbot with your own events, other than those prebuilt in the `gr.ChatInterface`. For example, you could create a dropdown that prefills the chat history with certain conversations or add a separate button to clear the conversation history. The `gr.ChatInterface` supports these events, but you need to use the `gr.ChatInterface.chatbot_value` as the input or output component in such events. In this example, we use a `gr.Radio` component to prefill the the chatbot with certain conversations:\n\n$code_chatinterface_prefill\n\n", "heading1": "Modifying the Chatbot Value Directly", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "Once you've built your Gradio chat interface and are hosting it on [Hugging Face Spaces](https://hf.space) or somewhere else, then you can query it with a simple API at the `/chat` endpoint. The endpoint just expects the user's message and will return the response, internally keeping track of the message history.\n\n![](https://github.com/gradio-app/gradio/assets/1778297/7b10d6db-6476-4e2e-bebd-ecda802c3b8f)\n\nTo use the endpoint, you should use either the [Gradio Python Client](/guides/getting-started-with-the-python-client) or the [Gradio JS client](/guides/getting-started-with-the-js-client). Or, you can deploy your Chat Interface to other platforms, such as a:\n\n* Discord bot [[tutorial]](../guides/creating-a-discord-bot-from-a-gradio-app)\n* Slack bot [[tutorial]](../guides/creating-a-slack-bot-from-a-gradio-app)\n* Website widget [[tutorial]](../guides/creating-a-website-widget-from-a-gradio-chatbot)\n\n", "heading1": "Using Your Chatbot via API", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "You can enable persistent chat history for your ChatInterface, allowing users to maintain multiple conversations and easily switch between them. When enabled, conversations are stored locally and privately in the user's browser using local storage. So if you deploy a ChatInterface e.g. on [Hugging Face Spaces](https://hf.space), each user will have their own separate chat history that won't interfere with other users' conversations. This means multiple users can interact with the same ChatInterface simultaneously while maintaining their own private conversation histories.\n\nTo enable this feature, simply set `gr.ChatInterface(save_history=True)` (as shown in the example in the next section). Users will then see their previous conversations in a side panel and can continue any previous chat or start a new one.\n\n", "heading1": "Chat History", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "To gather feedback on your chat model, set `gr.ChatInterface(flagging_mode=\"manual\")` and users will be able to thumbs-up or thumbs-down assistant responses. Each flagged response, along with the entire chat history, will get saved in a CSV file in the app working directory (this can be configured via the `flagging_dir` parameter). \n\nYou can also change the feedback options via `flagging_options` parameter. The default options are \"Like\" and \"Dislike\", which appear as the thumbs-up and thumbs-down icons. Any other options appear under a dedicated flag icon. This example shows a ChatInterface that has both chat history (mentioned in the previous section) and user feedback enabled:\n\n$code_chatinterface_streaming_echo\n\nNote that in this example, we set several flagging options: \"Like\", \"Spam\", \"Inappropriate\", \"Other\". Because the case-sensitive string \"Like\" is one of the flagging options, the user will see a thumbs-up icon next to each assistant message. The three other flagging options will appear in a dropdown under the flag icon.\n\n", "heading1": "Collecting User Feedback", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "Now that you've learned about the `gr.ChatInterface` class and how it can be used to create chatbot UIs quickly, we recommend reading one of the following:\n\n* [Our next Guide](../guides/chatinterface-examples) shows examples of how to use `gr.ChatInterface` with popular LLM libraries.\n* If you'd like to build very custom chat applications from scratch, you can build them using the low-level Blocks API, as [discussed in this Guide](../guides/creating-a-custom-chatbot-with-blocks).\n* Once you've deployed your Gradio Chat Interface, its easy to use in other applications because of the built-in API. Here's a tutorial on [how to deploy a Gradio chat interface as a Discord bot](../guides/creating-a-discord-bot-from-a-gradio-app).\n\n\n", "heading1": "What's Next?", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "**Important Note**: if you are getting started, we recommend using the `gr.ChatInterface` to create chatbots -- its a high-level abstraction that makes it possible to create beautiful chatbot applications fast, often with a single line of code. [Read more about it here](/guides/creating-a-chatbot-fast).\n\nThis tutorial will show how to make chatbot UIs from scratch with Gradio's low-level Blocks API. This will give you full control over your Chatbot UI. You'll start by first creating a a simple chatbot to display text, a second one to stream text responses, and finally a chatbot that can handle media files as well. The chatbot interface that we create will look something like this:\n\n$demo_chatbot_streaming\n\n**Prerequisite**: We'll be using the `gradio.Blocks` class to build our Chatbot demo.\nYou can [read the Guide to Blocks first](https://gradio.app/blocks-and-event-listeners) if you are not already familiar with it. Also please make sure you are using the **latest version** version of Gradio: `pip install --upgrade gradio`.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "Let's start with recreating the simple demo above. As you may have noticed, our bot simply randomly responds \"How are you?\", \"Today is a great day\", or \"I'm very hungry\" to any input. Here's the code to create this with Gradio:\n\n$code_chatbot_simple\n\nThere are three Gradio components here:\n\n- A `Chatbot`, whose value stores the entire history of the conversation, as a list of response pairs between the user and bot.\n- A `Textbox` where the user can type their message, and then hit enter/submit to trigger the chatbot response\n- A `ClearButton` button to clear the Textbox and entire Chatbot history\n\nWe have a single function, `respond()`, which takes in the entire history of the chatbot, appends a random message, waits 1 second, and then returns the updated chat history. The `respond()` function also clears the textbox when it returns.\n\nOf course, in practice, you would replace `respond()` with your own more complex function, which might call a pretrained model or an API, to generate a response.\n\n$demo_chatbot_simple\n\nTip: For better type hinting and auto-completion in your IDE, you can use the `gr.ChatMessage` dataclass:\n\n```python\nfrom gradio import ChatMessage\n\ndef chat_function(message, history):\n    history.append(ChatMessage(role=\"user\", content=message))\n    history.append(ChatMessage(role=\"assistant\", content=\"Hello, how can I help you?\"))\n    return history\n```\n\n", "heading1": "A Simple Chatbot Demo", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "There are several ways we can improve the user experience of the chatbot above. First, we can stream responses so the user doesn't have to wait as long for a message to be generated. Second, we can have the user message appear immediately in the chat history, while the chatbot's response is being generated. Here's the code to achieve that:\n\n$code_chatbot_streaming\n\nYou'll notice that when a user submits their message, we now _chain_ two event events with `.then()`:\n\n1. The first method `user()` updates the chatbot with the user message and clears the input field. Because we want this to happen instantly, we set `queue=False`, which would skip any queue had it been enabled. The chatbot's history is appended with `{\"role\": \"user\", \"content\": user_message}`.\n\n2. The second method, `bot()` updates the chatbot history with the bot's response. Finally, we construct the message character by character and `yield` the intermediate outputs as they are being constructed. Gradio automatically turns any function with the `yield` keyword [into a streaming output interface](/guides/key-features/iterative-outputs).\n\n\nOf course, in practice, you would replace `bot()` with your own more complex function, which might call a pretrained model or an API, to generate a response.\n\n\n", "heading1": "Add Streaming to your Chatbot", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "The `gr.Chatbot` component supports a subset of markdown including bold, italics, and code. For example, we could write a function that responds to a user's message, with a bold **That's cool!**, like this:\n\n```py\ndef bot(history):\n    response = {\"role\": \"assistant\", \"content\": \"**That's cool!**\"}\n    history.append(response)\n    return history\n```\n\nIn addition, it can handle media files, such as images, audio, and video. You can use the `MultimodalTextbox` component to easily upload all types of media files to your chatbot. You can customize the `MultimodalTextbox` further by passing in the `sources` parameter, which is a list of sources to enable. To pass in a media file, we must pass in the file a dictionary with a `path` key pointing to a local file and an `alt_text` key. The `alt_text` is optional, so you can also just pass in a tuple with a single element `{\"path\": \"filepath\"}`, like this:\n\n```python\ndef add_message(history, message):\n    for x in message[\"files\"]:\n        history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n    if message[\"text\"] is not None:\n        history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n    return history, gr.MultimodalTextbox(value=None, interactive=False, file_types=[\"image\"], sources=[\"upload\", \"microphone\"])\n```\n\nPutting this together, we can create a _multimodal_ chatbot with a multimodal textbox for a user to submit text and media files. The rest of the code looks pretty much the same as before:\n\n$code_chatbot_multimodal\n$demo_chatbot_multimodal\n\nAnd you're done! That's all the code you need to build an interface for your chatbot model. Finally, we'll end our Guide with some links to Chatbots that are running on Spaces so that you can get an idea of what else is possible:\n\n- [project-baize/Baize-7B](https://huggingface.co/spaces/project-baize/Baize-7B): A stylized chatbot that allows you to stop generation as well as regenerate responses.\n- [MAGAer13/mPLUG-Owl](https://huggingface.co/spaces/MAGAer13/mPLUG-Ow", "heading1": "Adding Markdown, Images, Audio, or Videos", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "ingface.co/spaces/project-baize/Baize-7B): A stylized chatbot that allows you to stop generation as well as regenerate responses.\n- [MAGAer13/mPLUG-Owl](https://huggingface.co/spaces/MAGAer13/mPLUG-Owl): A multimodal chatbot that allows you to upvote and downvote responses.\n", "heading1": "Adding Markdown, Images, Audio, or Videos", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "The Discord bot will listen to messages mentioning it in channels. When it receives a message (which can include text as well as files), it will send it to your Gradio app via Gradio's built-in API. Your bot will reply with the response it receives from the API. \n\nBecause Gradio's API is very flexible, you can create Discord bots that support text, images, audio, streaming, chat history, and a wide variety of other features very easily. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-18%20at%204.26.55%E2%80%AFPM.gif)\n\n", "heading1": "How does it work?", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": "* Install the latest version of `gradio` and the `discord.py` libraries:\n\n```\npip install --upgrade gradio discord.py~=2.0\n```\n\n* Have a running Gradio app. This app can be running locally or on Hugging Face Spaces. In this example, we will be using the [Gradio Playground Space](https://huggingface.co/spaces/abidlabs/gradio-playground-bot), which takes in an image and/or text and generates the code to generate the corresponding Gradio app.\n\nNow, we are ready to get started!\n\n\n1. Create a Discord application\n\nFirst, go to the [Discord apps dashboard](https://discord.com/developers/applications). Look for the \"New Application\" button and click it. Give your application a name, and then click \"Create\".\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/discord-4.png)\n\nOn the resulting screen, you will see basic information about your application. Under the Settings section, click on the \"Bot\" option. You can update your bot's username if you would like.\n\nThen click on the \"Reset Token\" button. A new token will be generated. Copy it as we will need it for the next step.\n\nScroll down to the section that says \"Privileged Gateway Intents\". Your bot will need certain permissions to work correctly. In this tutorial, we will only be using the \"Message Content Intent\" so click the toggle to enable this intent. Save the changes.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/discord-3.png)\n\n\n\n2. Write a Discord bot\n\nLet's start by writing a very simple Discord bot, just to make sure that everything is working. Write the following Python code in a file called `bot.py`, pasting the discord bot token from the previous step:\n\n```python\nbot.py\nimport discord\n\nTOKEN = PASTE YOUR DISCORD BOT TOKEN HERE\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n    print(f'{client.user} has connected to Discord!')\n\nclient.run(TOKEN)\n```\n\nNow, run this file: `python bot.py`, w", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": "CORD BOT TOKEN HERE\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n    print(f'{client.user} has connected to Discord!')\n\nclient.run(TOKEN)\n```\n\nNow, run this file: `python bot.py`, which should run and print a message like:\n\n```text\nWe have logged in as GradioPlaygroundBot1451\n```\n\nIf that is working, we are ready to add Gradio-specific code. We will be using the [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) to query the Gradio Playground Space mentioned above. Here's the updated `bot.py` file:\n\n```python\nimport discord\nfrom gradio_client import Client, handle_file\nimport httpx\nimport os\n\nTOKEN = PASTE YOUR DISCORD BOT TOKEN HERE\n\nintents = discord.Intents.default()\nintents.message_content = True\n\nclient = discord.Client(intents=intents)\ngradio_client = Client(\"abidlabs/gradio-playground-bot\")\n\ndef download_image(attachment):\n    response = httpx.get(attachment.url)\n    image_path = f\"./images/{attachment.filename}\"\n    os.makedirs(\"./images\", exist_ok=True)\n    with open(image_path, \"wb\") as f:\n        f.write(response.content)\n    return image_path\n\n@client.event\nasync def on_ready():\n    print(f'We have logged in as {client.user}')\n\n@client.event\nasync def on_message(message):\n    Ignore messages from the bot itself\n    if message.author == client.user:\n        return\n\n    Check if the bot is mentioned in the message and reply\n    if client.user in message.mentions:\n        Extract the message content without the bot mention\n        clean_message = message.content.replace(f\"<@{client.user.id}>\", \"\").strip()\n\n        Handle images (only the first image is used)\n        files = []\n        if message.attachments:\n            for attachment in message.attachments:\n                if any(attachment.filename.lower().endswith(ext) for ext in ['png', 'jpg', 'jpeg', 'gif', 'webp']):\n                    image_path = download_image(attachment)\n                    files.append(handle_file(image_path))", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": ".filename.lower().endswith(ext) for ext in ['png', 'jpg', 'jpeg', 'gif', 'webp']):\n                    image_path = download_image(attachment)\n                    files.append(handle_file(image_path))\n                    break\n        \n        Stream the responses to the channel\n        for response in gradio_client.submit(\n            message={\"text\": clean_message, \"files\": files},\n        ):\n            await message.channel.send(response[-1])\n\nclient.run(TOKEN)\n```\n\n3. Add the bot to your Discord Server\n\nNow we are ready to install the bot on our server. Go back to the [Discord apps dashboard](https://discord.com/developers/applications). Under the Settings section, click on the \"OAuth2\" option. Scroll down to the \"OAuth2 URL Generator\" box and select the \"bot\" checkbox:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/discord-2.png)\n\n\n\nThen in \"Bot Permissions\" box that pops up underneath, enable the following permissions:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/discord-1.png)\n\n\nCopy the generated URL that appears underneath, which should look something like:\n\n```text\nhttps://discord.com/oauth2/authorize?client_id=1319011745452265575&permissions=377957238784&integration_type=0&scope=bot\n```\n\nPaste it into your browser, which should allow you to add the Discord bot to any Discord server that you manage.\n\n\n4. That's it!\n\nNow you can mention your bot from any channel in your Discord server, optionally attach an image, and it will respond with generated Gradio app code!\n\nThe bot will:\n1. Listen for mentions\n2. Process any attached images\n3. Send the text and images to your Gradio app\n4. Stream the responses back to the Discord channel\n\n This is just a basic example - you can extend it to handle more types of files, add error handling, or integrate with different Gradio apps.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": "c example - you can extend it to handle more types of files, add error handling, or integrate with different Gradio apps.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-18%20at%204.26.55%E2%80%AFPM.gif)\n\nIf you build a Discord bot from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gradio), and we are happy to help you amplify!", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": "The chat widget appears as a small button in the corner of your website. When clicked, it opens a chat interface that communicates with your Gradio app via the JavaScript Client API. Users can ask questions and receive responses directly within the widget.\n\n\n", "heading1": "How does it work?", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": "* A running Gradio app (local or on Hugging Face Spaces). In this example, we'll use the [Gradio Playground Space](https://huggingface.co/spaces/abidlabs/gradio-playground-bot), which helps generate code for Gradio apps based on natural language descriptions.\n\n1. Create and Style the Chat Widget\n\nFirst, add this HTML and CSS to your website:\n\n```html\n<div id=\"chat-widget\" class=\"chat-widget\">\n    <button id=\"chat-toggle\" class=\"chat-toggle\">\ud83d\udcac</button>\n    <div id=\"chat-container\" class=\"chat-container hidden\">\n        <div id=\"chat-header\">\n            <h3>Gradio Assistant</h3>\n            <button id=\"close-chat\">\u00d7</button>\n        </div>\n        <div id=\"chat-messages\"></div>\n        <div id=\"chat-input-area\">\n            <input type=\"text\" id=\"chat-input\" placeholder=\"Ask a question...\">\n            <button id=\"send-message\">Send</button>\n        </div>\n    </div>\n</div>\n\n<style>\n.chat-widget {\n    position: fixed;\n    bottom: 20px;\n    right: 20px;\n    z-index: 1000;\n}\n\n.chat-toggle {\n    width: 50px;\n    height: 50px;\n    border-radius: 50%;\n    background: 007bff;\n    border: none;\n    color: white;\n    font-size: 24px;\n    cursor: pointer;\n}\n\n.chat-container {\n    position: fixed;\n    bottom: 80px;\n    right: 20px;\n    width: 300px;\n    height: 400px;\n    background: white;\n    border-radius: 10px;\n    box-shadow: 0 0 10px rgba(0,0,0,0.1);\n    display: flex;\n    flex-direction: column;\n}\n\n.chat-container.hidden {\n    display: none;\n}\n\nchat-header {\n    padding: 10px;\n    background: 007bff;\n    color: white;\n    border-radius: 10px 10px 0 0;\n    display: flex;\n    justify-content: space-between;\n    align-items: center;\n}\n\nchat-messages {\n    flex-grow: 1;\n    overflow-y: auto;\n    padding: 10px;\n}\n\nchat-input-area {\n    padding: 10px;\n    border-top: 1px solid eee;\n    display: flex;\n}\n\nchat-input {\n    flex-grow: 1;\n    padding: 8px;\n    border: 1px solid ddd;\n    border-radius: 4px;\n    margin-right: 8px;\n}\n\n.message {\n    margin: 8px 0;\n    pad", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": " solid eee;\n    display: flex;\n}\n\nchat-input {\n    flex-grow: 1;\n    padding: 8px;\n    border: 1px solid ddd;\n    border-radius: 4px;\n    margin-right: 8px;\n}\n\n.message {\n    margin: 8px 0;\n    padding: 8px;\n    border-radius: 4px;\n}\n\n.user-message {\n    background: e9ecef;\n    margin-left: 20px;\n}\n\n.bot-message {\n    background: f8f9fa;\n    margin-right: 20px;\n}\n</style>\n```\n\n2. Add the JavaScript\n\nThen, add the following JavaScript code (which uses the Gradio JavaScript Client to connect to the Space) to your website by including this in the `<head>` section of your website:\n\n```html\n<script type=\"module\">\n    import { Client } from \"https://cdn.jsdelivr.net/npm/@gradio/client/dist/index.min.js\";\n    \n    async function initChatWidget() {\n        const client = await Client.connect(\"https://abidlabs-gradio-playground-bot.hf.space\");\n        \n        const chatToggle = document.getElementById('chat-toggle');\n        const chatContainer = document.getElementById('chat-container');\n        const closeChat = document.getElementById('close-chat');\n        const chatInput = document.getElementById('chat-input');\n        const sendButton = document.getElementById('send-message');\n        const messagesContainer = document.getElementById('chat-messages');\n    \n        chatToggle.addEventListener('click', () => {\n            chatContainer.classList.remove('hidden');\n        });\n    \n        closeChat.addEventListener('click', () => {\n            chatContainer.classList.add('hidden');\n        });\n    \n        async function sendMessage() {\n            const userMessage = chatInput.value.trim();\n            if (!userMessage) return;\n\n            appendMessage(userMessage, 'user');\n            chatInput.value = '';\n\n            try {\n                const result = await client.predict(\"/chat\", {\n                    message: {\"text\": userMessage, \"files\": []}\n                });\n                const message = result.data[0];\n                console.log(result.data[0]", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": "client.predict(\"/chat\", {\n                    message: {\"text\": userMessage, \"files\": []}\n                });\n                const message = result.data[0];\n                console.log(result.data[0]);\n                const botMessage = result.data[0].join('\\n');\n                appendMessage(botMessage, 'bot');\n            } catch (error) {\n                console.error('Error:', error);\n                appendMessage('Sorry, there was an error processing your request.', 'bot');\n            }\n        }\n    \n        function appendMessage(text, sender) {\n            const messageDiv = document.createElement('div');\n            messageDiv.className = `message ${sender}-message`;\n            \n            if (sender === 'bot') {\n                messageDiv.innerHTML = marked.parse(text);\n            } else {\n                messageDiv.textContent = text;\n            }\n            \n            messagesContainer.appendChild(messageDiv);\n            messagesContainer.scrollTop = messagesContainer.scrollHeight;\n        }\n    \n        sendButton.addEventListener('click', sendMessage);\n        chatInput.addEventListener('keypress', (e) => {\n            if (e.key === 'Enter') sendMessage();\n        });\n    }\n    \n    initChatWidget();\n</script>\n```\n\n3. That's it!\n\nYour website now has a chat widget that connects to your Gradio app! Users can click the chat button to open the widget and start interacting with your app.\n\nCustomization\n\nYou can customize the appearance of the widget by modifying the CSS. Some ideas:\n- Change the colors to match your website's theme\n- Adjust the size and position of the widget\n- Add animations for opening/closing\n- Modify the message styling\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-19%20at%203.32.46%E2%80%AFPM.gif)\n\nIf you build a website widget from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gradio), and we are hap", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": "%20Recording%202024-12-19%20at%203.32.46%E2%80%AFPM.gif)\n\nIf you build a website widget from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gradio), and we are happy to help you amplify!", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": "Install the @gradio/client package to interact with Gradio APIs using Node.js version >=18.0.0 or in browser-based projects. Use npm or any compatible package manager:\n\n```bash\nnpm i @gradio/client\n```\n\nThis command adds @gradio/client to your project dependencies, allowing you to import it in your JavaScript or TypeScript files.\n\n", "heading1": "Installation via npm", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "For quick addition to your web project, you can use the jsDelivr CDN to load the latest version of @gradio/client directly into your HTML:\n\n```html\n<script type=\"module\">\n\timport { Client } from \"https://cdn.jsdelivr.net/npm/@gradio/client/dist/index.min.js\";\n\t...\n</script>\n```\n\nBe sure to add this to the `<head>` of your HTML. This will install the latest version but we advise hardcoding the version in production. You can find all available versions [here](https://www.jsdelivr.com/package/npm/@gradio/client). This approach is ideal for experimental or prototying purposes, though has some limitations. A complete example would look like this:\n\n```html\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n    <script type=\"module\">\n        import { Client } from \"https://cdn.jsdelivr.net/npm/@gradio/client/dist/index.min.js\";\n        const client = await Client.connect(\"abidlabs/en2fr\");\n        const result = await client.predict(\"/predict\", {\n            text: \"My name is Hannah\"\n        });\n        console.log(result);\n    </script>\n</head>\n</html>\n```\n\n", "heading1": "Installation via CDN", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Start by connecting instantiating a `client` instance and connecting it to a Gradio app that is running on Hugging Face Spaces or generally anywhere on the web.\n\n", "heading1": "Connecting to a running Gradio App", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\"); // a Space that translates from English to French\n```\n\nYou can also connect to private Spaces by passing in your HF token with the `hf_token` property of the options parameter. You can get your HF token here: https://huggingface.co/settings/tokens\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/my-private-space\", { hf_token: \"hf_...\" })\n```\n\n", "heading1": "Connecting to a Hugging Face Space", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "While you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space, and then use it to make as many requests as you'd like! You'll need to pass in your [Hugging Face token](https://huggingface.co/settings/tokens)).\n\n`Client.duplicate` is almost identical to `Client.connect`, the only difference is under the hood:\n\n```js\nimport { Client, handle_file } from \"@gradio/client\";\n\nconst response = await fetch(\n\t\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\"\n);\nconst audio_file = await response.blob();\n\nconst app = await Client.duplicate(\"abidlabs/whisper\", { hf_token: \"hf_...\" });\nconst transcription = await app.predict(\"/predict\", [handle_file(audio_file)]);\n```\n\nIf you have previously duplicated a Space, re-running `Client.duplicate` will _not_ create a new Space. Instead, the client will attach to the previously-created Space. So it is safe to re-run the `Client.duplicate` method multiple times with the same space.\n\n**Note:** if the original Space uses GPUs, your private Space will as well, and your Hugging Face account will get billed based on the price of the GPU. To minimize charges, your Space will automatically go to sleep after 5 minutes of inactivity. You can also set the hardware using the `hardware` and `timeout` properties of `duplicate`'s options object like this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.duplicate(\"abidlabs/whisper\", {\n\thf_token: \"hf_...\",\n\ttimeout: 60,\n\thardware: \"a10g-small\"\n});\n```\n\n", "heading1": "Duplicating a Space for private use", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If your app is running somewhere else, just provide the full URL instead, including the \"http://\" or \"https://\". Here's an example of making predictions to a Gradio app that is running on a share URL:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = Client.connect(\"https://bec81a83-5b5c-471e.gradio.live\");\n```\n\n", "heading1": "Connecting a general Gradio app", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If the Gradio application you are connecting to [requires a username and password](/guides/sharing-your-appauthentication), then provide them as a tuple to the `auth` argument of the `Client` class:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nClient.connect(\n  space_name,\n  { auth: [username, password] }\n)\n```\n\n\n", "heading1": "Connecting to a Gradio app with auth", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are available to you by calling the `Client`'s `view_api` method.\n\nFor the Whisper Space, we can do this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/whisper\");\n\nconst app_info = await app.view_api();\n\nconsole.log(app_info);\n```\n\nAnd we will see the following:\n\n```json\n{\n\t\"named_endpoints\": {\n\t\t\"/predict\": {\n\t\t\t\"parameters\": [\n\t\t\t\t{\n\t\t\t\t\t\"label\": \"text\",\n\t\t\t\t\t\"component\": \"Textbox\",\n\t\t\t\t\t\"type\": \"string\"\n\t\t\t\t}\n\t\t\t],\n\t\t\t\"returns\": [\n\t\t\t\t{\n\t\t\t\t\t\"label\": \"output\",\n\t\t\t\t\t\"component\": \"Textbox\",\n\t\t\t\t\t\"type\": \"string\"\n\t\t\t\t}\n\t\t\t]\n\t\t}\n\t},\n\t\"unnamed_endpoints\": {}\n}\n```\n\nThis shows us that we have 1 API endpoint in this space, and shows us how to use the API endpoint to make a prediction: we should call the `.predict()` method (which we will explore below), providing a parameter `input_audio` of type `string`, which is a url to a file.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()` method. Although this isn't necessary if a Gradio app has only 1 named endpoint, it does allow us to call different endpoints in a single app if they are available. If an app has unnamed API endpoints, these can also be displayed by running `.view_api(all_endpoints=True)`.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the \"Use via API\" link in the footer of the Gradio app, which shows us the same information, along with example usage. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \"API Recorder\" that lets you interact with the Gradio UI normally and converts your interactions into the corresponding code to run with the JS Client.\n\n\n", "heading1": "The \"View API\" Page", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()` method with the appropriate arguments:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst result = await app.predict(\"/predict\", [\"Hello\"]);\n```\n\nIf there are multiple parameters, then you should pass them as an array to `.predict()`, like this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/calculator\");\nconst result = await app.predict(\"/predict\", [4, \"add\", 5]);\n```\n\nFor certain inputs, such as images, you should pass in a `Buffer`, `Blob` or `File` depending on what is most convenient. In node, this would be a `Buffer` or `Blob`; in a browser environment, this would be a `Blob` or `File`.\n\n```js\nimport { Client, handle_file } from \"@gradio/client\";\n\nconst response = await fetch(\n\t\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\"\n);\nconst audio_file = await response.blob();\n\nconst app = await Client.connect(\"abidlabs/whisper\");\nconst result = await app.predict(\"/predict\", [handle_file(audio_file)]);\n```\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If the API you are working with can return results over time, or you wish to access information about the status of a job, you can use the iterable interface for more flexibility. This is especially useful for iterative endpoints or generator endpoints that will produce a series of values over time as discrete responses.\n\n```js\nimport { Client } from \"@gradio/client\";\n\nfunction log_result(payload) {\n\tconst {\n\t\tdata: [translation]\n\t} = payload;\n\n\tconsole.log(`The translated result is: ${translation}`);\n}\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst job = app.submit(\"/predict\", [\"Hello\"]);\n\nfor await (const message of job) {\n\tlog_result(message);\n}\n```\n\n", "heading1": "Using events", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The event interface also allows you to get the status of the running job by instantiating the client with the `events` options passing `status` and `data` as an array:\n\n\n```ts\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\", {\n\tevents: [\"status\", \"data\"]\n});\n```\n\nThis ensures that status messages are also reported to the client.\n\n`status`es are returned as an object with the following attributes: `status` (a human readbale status of the current job, `\"pending\" | \"generating\" | \"complete\" | \"error\"`), `code` (the detailed gradio code for the job), `position` (the current position of this job in the queue), `queue_size` (the total queue size), `eta` (estimated time this job will complete), `success` (a boolean representing whether the job completed successfully), and `time` ( as `Date` object detailing the time that the status was generated).\n\n```js\nimport { Client } from \"@gradio/client\";\n\nfunction log_status(status) {\n\tconsole.log(\n\t\t`The current status for this job is: ${JSON.stringify(status, null, 2)}.`\n\t);\n}\n\nconst app = await Client.connect(\"abidlabs/en2fr\", {\n\tevents: [\"status\", \"data\"]\n});\nconst job = app.submit(\"/predict\", [\"Hello\"]);\n\nfor await (const message of job) {\n\tif (message.type === \"status\") {\n\t\tlog_status(message);\n\t}\n}\n```\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The job instance also has a `.cancel()` method that cancels jobs that have been queued but not started. For example, if you run:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst job_one = app.submit(\"/predict\", [\"Hello\"]);\nconst job_two = app.submit(\"/predict\", [\"Friends\"]);\n\njob_one.cancel();\njob_two.cancel();\n```\n\nIf the first job has started processing, then it will not be canceled but the client will no longer listen for updates (throwing away the job). If the second job has not yet started, it will be successfully canceled and removed from the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a series of values. You can listen for these values in real time using the iterable interface:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/count_generator\");\nconst job = app.submit(0, [9]);\n\nfor await (const message of job) {\n\tconsole.log(message.data);\n}\n```\n\nThis will log out the values as they are generated by the endpoint.\n\nYou can also cancel jobs that that have iterative outputs, in which case the job will finish immediately.\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/count_generator\");\nconst job = app.submit(0, [9]);\n\nfor await (const message of job) {\n\tconsole.log(message.data);\n}\n\nsetTimeout(() => {\n\tjob.cancel();\n}, 3000);\n```\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Let's start with what seems like the most complex bit -- using machine learning to remove the music from a video.\n\nLuckily for us, there's an existing Space we can use to make this process easier: [https://huggingface.co/spaces/abidlabs/music-separation](https://huggingface.co/spaces/abidlabs/music-separation). This Space takes an audio file and produces two separate audio files: one with the instrumental music and one with all other sounds in the original clip. Perfect to use with our client!\n\nOpen a new Python file, say `main.py`, and start by importing the `Client` class from `gradio_client` and connecting it to this Space:\n\n```py\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/music-separation\")\n\ndef acapellify(audio_path):\n    result = client.predict(handle_file(audio_path), api_name=\"/predict\")\n    return result[0]\n```\n\nThat's all the code that's needed -- notice that the API endpoints returns two audio files (one without the music, and one with just the music) in a list, and so we just return the first element of the list.\n\n---\n\n**Note**: since this is a public Space, there might be other users using this Space as well, which might result in a slow experience. You can duplicate this Space with your own [Hugging Face token](https://huggingface.co/settings/tokens) and create a private Space that only you have will have access to and bypass the queue. To do that, simply replace the first two lines above with:\n\n```py\nfrom gradio_client import Client\n\nclient = Client.duplicate(\"abidlabs/music-separation\", hf_token=YOUR_HF_TOKEN)\n```\n\nEverything else remains the same!\n\n---\n\nNow, of course, we are working with video files, so we first need to extract the audio from the video files. For this, we will be using the `ffmpeg` library, which does a lot of heavy lifting when it comes to working with audio and video files. The most common way to use `ffmpeg` is through the command line, which we'll call via Python's `subprocess` module:\n\nOur video p", "heading1": "Step 1: Write the Video Processing Function", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "t of heavy lifting when it comes to working with audio and video files. The most common way to use `ffmpeg` is through the command line, which we'll call via Python's `subprocess` module:\n\nOur video processing workflow will consist of three steps:\n\n1. First, we start by taking in a video filepath and extracting the audio using `ffmpeg`.\n2. Then, we pass in the audio file through the `acapellify()` function above.\n3. Finally, we combine the new audio with the original video to produce a final acapellified video.\n\nHere's the complete code in Python, which you can add to your `main.py` file:\n\n```python\nimport subprocess\n\ndef process_video(video_path):\n    old_audio = os.path.basename(video_path).split(\".\")[0] + \".m4a\"\n    subprocess.run(['ffmpeg', '-y', '-i', video_path, '-vn', '-acodec', 'copy', old_audio])\n\n    new_audio = acapellify(old_audio)\n\n    new_video = f\"acap_{video_path}\"\n    subprocess.call(['ffmpeg', '-y', '-i', video_path, '-i', new_audio, '-map', '0:v', '-map', '1:a', '-c:v', 'copy', '-c:a', 'aac', '-strict', 'experimental', f\"static/{new_video}\"])\n    return new_video\n```\n\nYou can read up on [ffmpeg documentation](https://ffmpeg.org/ffmpeg.html) if you'd like to understand all of the command line parameters, as they are beyond the scope of this tutorial.\n\n", "heading1": "Step 1: Write the Video Processing Function", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Next up, we'll create a simple FastAPI app. If you haven't used FastAPI before, check out [the great FastAPI docs](https://fastapi.tiangolo.com/). Otherwise, this basic template, which we add to `main.py`, will look pretty familiar:\n\n```python\nimport os\nfrom fastapi import FastAPI, File, UploadFile, Request\nfrom fastapi.responses import HTMLResponse, RedirectResponse\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.templating import Jinja2Templates\n\napp = FastAPI()\nos.makedirs(\"static\", exist_ok=True)\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\ntemplates = Jinja2Templates(directory=\"templates\")\n\nvideos = []\n\n@app.get(\"/\", response_class=HTMLResponse)\nasync def home(request: Request):\n    return templates.TemplateResponse(\n        \"home.html\", {\"request\": request, \"videos\": videos})\n\n@app.post(\"/uploadvideo/\")\nasync def upload_video(video: UploadFile = File(...)):\n    video_path = video.filename\n    with open(video_path, \"wb+\") as fp:\n        fp.write(video.file.read())\n\n    new_video = process_video(video.filename)\n    videos.append(new_video)\n    return RedirectResponse(url='/', status_code=303)\n```\n\nIn this example, the FastAPI app has two routes: `/` and `/uploadvideo/`.\n\nThe `/` route returns an HTML template that displays a gallery of all uploaded videos.\n\nThe `/uploadvideo/` route accepts a `POST` request with an `UploadFile` object, which represents the uploaded video file. The video file is \"acapellified\" via the `process_video()` method, and the output video is stored in a list which stores all of the uploaded videos in memory.\n\nNote that this is a very basic example and if this were a production app, you will need to add more logic to handle file storage, user authentication, and security considerations.\n\n", "heading1": "Step 2: Create a FastAPI app (Backend Routes)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Finally, we create the frontend of our web application. First, we create a folder called `templates` in the same directory as `main.py`. We then create a template, `home.html` inside the `templates` folder. Here is the resulting file structure:\n\n```csv\n\u251c\u2500\u2500 main.py\n\u251c\u2500\u2500 templates\n\u2502   \u2514\u2500\u2500 home.html\n```\n\nWrite the following as the contents of `home.html`:\n\n```html\n&lt;!DOCTYPE html> &lt;html> &lt;head> &lt;title>Video Gallery&lt;/title>\n&lt;style> body { font-family: sans-serif; margin: 0; padding: 0;\nbackground-color: f5f5f5; } h1 { text-align: center; margin-top: 30px;\nmargin-bottom: 20px; } .gallery { display: flex; flex-wrap: wrap;\njustify-content: center; gap: 20px; padding: 20px; } .video { border: 2px solid\nccc; box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.2); border-radius: 5px; overflow:\nhidden; width: 300px; margin-bottom: 20px; } .video video { width: 100%; height:\n200px; } .video p { text-align: center; margin: 10px 0; } form { margin-top:\n20px; text-align: center; } input[type=\"file\"] { display: none; } .upload-btn {\ndisplay: inline-block; background-color: 3498db; color: fff; padding: 10px\n20px; font-size: 16px; border: none; border-radius: 5px; cursor: pointer; }\n.upload-btn:hover { background-color: 2980b9; } .file-name { margin-left: 10px;\n} &lt;/style> &lt;/head> &lt;body> &lt;h1>Video Gallery&lt;/h1> {% if videos %}\n&lt;div class=\"gallery\"> {% for video in videos %} &lt;div class=\"video\">\n&lt;video controls> &lt;source src=\"{{ url_for('static', path=video) }}\"\ntype=\"video/mp4\"> Your browser does not support the video tag. &lt;/video>\n&lt;p>{{ video }}&lt;/p> &lt;/div> {% endfor %} &lt;/div> {% else %} &lt;p>No\nvideos uploaded yet.&lt;/p> {% endif %} &lt;form action=\"/uploadvideo/\"\nmethod=\"post\" enctype=\"multipart/form-data\"> &lt;label for=\"video-upload\"\nclass=\"upload-btn\">Choose video file&lt;/label> &lt;input type=\"file\"\nname=\"video\" id=\"video-upload\"> &lt;span class=\"file-name\">&lt;/span> &lt;button\ntype=\"submit\" class=\"upload-btn\">Upload&lt;/butto", "heading1": "Step 3: Create a FastAPI app (Frontend Template)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "class=\"upload-btn\">Choose video file&lt;/label> &lt;input type=\"file\"\nname=\"video\" id=\"video-upload\"> &lt;span class=\"file-name\">&lt;/span> &lt;button\ntype=\"submit\" class=\"upload-btn\">Upload&lt;/button> &lt;/form> &lt;script> //\nDisplay selected file name in the form const fileUpload =\ndocument.getElementById(\"video-upload\"); const fileName =\ndocument.querySelector(\".file-name\"); fileUpload.addEventListener(\"change\", (e)\n=> { fileName.textContent = e.target.files[0].name; }); &lt;/script> &lt;/body>\n&lt;/html>\n```\n\n", "heading1": "Step 3: Create a FastAPI app (Frontend Template)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Finally, we are ready to run our FastAPI app, powered by the Gradio Python Client!\n\nOpen up a terminal and navigate to the directory containing `main.py`. Then run the following command in the terminal:\n\n```bash\n$ uvicorn main:app\n```\n\nYou should see an output that looks like this:\n\n```csv\nLoaded as API: https://abidlabs-music-separation.hf.space \u2714\nINFO:     Started server process [1360]\nINFO:     Waiting for application startup.\nINFO:     Application startup complete.\nINFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n```\n\nAnd that's it! Start uploading videos and you'll get some \"acapellified\" videos in response (might take seconds to minutes to process depending on the length of your videos). Here's how the UI looks after uploading two videos:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/acapellify.png)\n\nIf you'd like to learn more about how to use the Gradio Python Client in your projects, [read the dedicated Guide](/guides/getting-started-with-the-python-client/).\n", "heading1": "Step 4: Run your FastAPI app", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "`@gradio/lite` is a JavaScript library that enables you to run Gradio applications directly within your web browser. It achieves this by utilizing Pyodide, a Python runtime for WebAssembly, which allows Python code to be executed in the browser environment. With `@gradio/lite`, you can **write regular Python code for your Gradio applications**, and they will **run seamlessly in the browser** without the need for server-side infrastructure.\n\n", "heading1": "What is `@gradio/lite`?", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "Let's build a \"Hello World\" Gradio app in `@gradio/lite`\n\n\n1. Import JS and CSS\n\nStart by creating a new HTML file, if you don't have one already. Importing the JavaScript and CSS corresponding to the `@gradio/lite` package by using the following code:\n\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n</html>\n```\n\nNote that you should generally use the latest version of `@gradio/lite` that is available. You can see the [versions available here](https://www.jsdelivr.com/package/npm/@gradio/lite?tab=files).\n\n2. Create the `<gradio-lite>` tags\n\nSomewhere in the body of your HTML page (wherever you'd like the Gradio app to be rendered), create opening and closing `<gradio-lite>` tags.\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nNote: you can add the `theme` attribute to the `<gradio-lite>` tag to force the theme to be dark or light (by default, it respects the system theme). E.g.\n\n```html\n<gradio-lite theme=\"dark\">\n...\n</gradio-lite>\n```\n\n3. Write your Gradio app inside of the tags\n\nNow, write your Gradio app as you would normally, in Python! Keep in mind that since this is Python, whitespace and indentations matter.\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\n\t\timport gradio as gr\n\n\t\tdef greet(name):\n\t\t\treturn \"Hello, \" + name + \"!\"\n\n\t\tgr.Interface(greet, \"textbox\", \"textbox\").launch()\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nAn", "heading1": "Getting Started", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "head>\n\t<body>\n\t\t<gradio-lite>\n\t\timport gradio as gr\n\n\t\tdef greet(name):\n\t\t\treturn \"Hello, \" + name + \"!\"\n\n\t\tgr.Interface(greet, \"textbox\", \"textbox\").launch()\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nAnd that's it! You should now be able to open your HTML page in the browser and see the Gradio app rendered! Note that it may take a little while for the Gradio app to load initially since Pyodide can take a while to install in your browser.\n\n**Note on debugging**: to see any errors in your Gradio-lite application, open the inspector in your web browser. All errors (including Python errors) will be printed there.\n\n", "heading1": "Getting Started", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "What if you want to create a Gradio app that spans multiple files? Or that has custom Python requirements? Both are possible with `@gradio/lite`!\n\nMultiple Files\n\nAdding multiple files within a `@gradio/lite` app is very straightforward: use the `<gradio-file>` tag. You can have as many `<gradio-file>` tags as you want, but each one needs to have a `name` attribute and the entry point to your Gradio app should have the `entrypoint` attribute.\n\nHere's an example:\n\n```html\n<gradio-lite>\n\n<gradio-file name=\"app.py\" entrypoint>\nimport gradio as gr\nfrom utils import add\n\ndemo = gr.Interface(fn=add, inputs=[\"number\", \"number\"], outputs=\"number\")\n\ndemo.launch()\n</gradio-file>\n\n<gradio-file name=\"utils.py\" >\ndef add(a, b):\n\treturn a + b\n</gradio-file>\n\n</gradio-lite>\n\n```\n\nAdditional Requirements\n\nIf your Gradio app has additional requirements, it is usually possible to [install them in the browser using micropip](https://pyodide.org/en/stable/usage/loading-packages.htmlloading-packages). We've created a wrapper to make this paticularly convenient: simply list your requirements in the same syntax as a `requirements.txt` and enclose them with `<gradio-requirements>` tags.\n\nHere, we install `transformers_js_py` to run a text classification model directly in the browser!\n\n```html\n<gradio-lite>\n\n<gradio-requirements>\ntransformers_js_py\n</gradio-requirements>\n\n<gradio-file name=\"app.py\" entrypoint>\nfrom transformers_js import import_transformers_js\nimport gradio as gr\n\ntransformers = await import_transformers_js()\npipeline = transformers.pipeline\npipe = await pipeline('sentiment-analysis')\n\nasync def classify(text):\n\treturn await pipe(text)\n\ndemo = gr.Interface(classify, \"textbox\", \"json\")\ndemo.launch()\n</gradio-file>\n\n</gradio-lite>\n\n```\n\n**Try it out**: You can see this example running in [this Hugging Face Static Space](https://huggingface.co/spaces/abidlabs/gradio-lite-classify), which lets you host static (serverless) web applications for free. Visit the page and y", "heading1": "More Examples: Adding Additional Files and Requirements", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "xample running in [this Hugging Face Static Space](https://huggingface.co/spaces/abidlabs/gradio-lite-classify), which lets you host static (serverless) web applications for free. Visit the page and you'll be able to run a machine learning model without internet access!\n\nSharedWorker mode\n\nBy default, Gradio-Lite executes Python code in a [Web Worker](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API) with [Pyodide](https://pyodide.org/) runtime, and each Gradio-Lite app has its own worker.\nIt has some benefits such as environment isolation.\n\nHowever, when there are many Gradio-Lite apps in the same page, it may cause performance issues such as high memory usage because each app has its own worker and Pyodide runtime.\nIn such cases, you can use the **SharedWorker mode** to share a single Pyodide runtime in a [SharedWorker](https://developer.mozilla.org/en-US/docs/Web/API/SharedWorker) among multiple Gradio-Lite apps. To enable the SharedWorker mode, set the `shared-worker` attribute to the `<gradio-lite>` tag.\n\n```html\n<!-- These two Gradio-Lite apps share a single worker -->\n\n<gradio-lite shared-worker>\nimport gradio as gr\n...\n</gradio-lite>\n\n<gradio-lite shared-worker>\nimport gradio as gr\n...\n</gradio-lite>\n```\n\nWhen using the SharedWorker mode, you should be aware of the following points:\n* The apps share the same Python environment, which means that they can access the same  modules and objects. If, for example, one app makes changes to some modules, the changes will be visible to other apps.\n* The file system is shared among the apps, while each app's files are mounted in each home directory, so each app can access the files of other apps.\n\nCode and Demo Playground\n\nIf you'd like to see the code side-by-side with the demo just pass in the `playground` attribute to the gradio-lite element. This will create an interactive playground that allows you to change the code and update the demo! If you're using playground, you can also set layo", "heading1": "More Examples: Adding Additional Files and Requirements", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": " `playground` attribute to the gradio-lite element. This will create an interactive playground that allows you to change the code and update the demo! If you're using playground, you can also set layout to either 'vertical' or 'horizontal' which will determine if the code editor and preview are side-by-side or on top of each other (by default it's reposnsive with the width of the page).\n\n```html\n<gradio-lite playground layout=\"horizontal\">\nimport gradio as gr\n\ngr.Interface(fn=lambda x: x,\n\t\t\tinputs=gr.Textbox(),\n\t\t\toutputs=gr.Textbox()\n\t\t).launch()\n</gradio-lite>\n```\n\n", "heading1": "More Examples: Adding Additional Files and Requirements", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "1. Serverless Deployment\nThe primary advantage of @gradio/lite is that it eliminates the need for server infrastructure. This simplifies deployment, reduces server-related costs, and makes it easier to share your Gradio applications with others.\n\n2. Low Latency\nBy running in the browser, @gradio/lite offers low-latency interactions for users. There's no need for data to travel to and from a server, resulting in faster responses and a smoother user experience.\n\n3. Privacy and Security\nSince all processing occurs within the user's browser, `@gradio/lite` enhances privacy and security. User data remains on their device, providing peace of mind regarding data handling.\n\nLimitations\n\n* Currently, the biggest limitation in using `@gradio/lite` is that your Gradio apps will generally take more time (usually 5-15 seconds) to load initially in the browser. This is because the browser needs to load the Pyodide runtime before it can render Python code.\n\n* Not every Python package is supported by Pyodide. While `gradio` and many other popular packages (including `numpy`, `scikit-learn`, and `transformers-js`) can be installed in Pyodide, if your app has many dependencies, its worth checking whether whether the dependencies are included in Pyodide, or can be [installed with `micropip`](https://micropip.pyodide.org/en/v0.2.2/project/api.htmlmicropip.install).\n\n", "heading1": "Benefits of Using `@gradio/lite`", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "You can immediately try out `@gradio/lite` by copying and pasting this code in a local `index.html` file and opening it with your browser:\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\n\t\timport gradio as gr\n\n\t\tdef greet(name):\n\t\t\treturn \"Hello, \" + name + \"!\"\n\n\t\tgr.Interface(greet, \"textbox\", \"textbox\").launch()\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\n\nWe've also created a playground on the Gradio website that allows you to interactively edit code and see the results immediately!\n\nPlayground: https://www.gradio.app/playground\n", "heading1": "Try it out!", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "If you already have a recent version of `gradio`, then the `gradio_client` is included as a dependency. But note that this documentation reflects the latest version of the `gradio_client`, so upgrade if you're not sure!\n\nThe lightweight `gradio_client` package can be installed from pip (or pip3) and is tested to work with **Python versions 3.10 or higher**:\n\n```bash\n$ pip install --upgrade gradio_client\n```\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Start by connecting instantiating a `Client` object and connecting it to a Gradio app that is running on Hugging Face Spaces.\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/en2fr\")  a Space that translates from English to French\n```\n\nYou can also connect to private Spaces by passing in your HF token with the `hf_token` parameter. You can get your HF token here: https://huggingface.co/settings/tokens\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/my-private-space\", hf_token=\"...\")\n```\n\n\n", "heading1": "Connecting to a Gradio App on Hugging Face Spaces", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "While you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space,\nand then use it to make as many requests as you'd like!\n\nThe `gradio_client` includes a class method: `Client.duplicate()` to make this process simple (you'll need to pass in your [Hugging Face token](https://huggingface.co/settings/tokens) or be logged in using the Hugging Face CLI):\n\n```python\nimport os\nfrom gradio_client import Client, handle_file\n\nHF_TOKEN = os.environ.get(\"HF_TOKEN\")\n\nclient = Client.duplicate(\"abidlabs/whisper\", hf_token=HF_TOKEN)\nclient.predict(handle_file(\"audio_sample.wav\"))\n\n>> \"This is a test of the whisper speech recognition model.\"\n```\n\nIf you have previously duplicated a Space, re-running `duplicate()` will _not_ create a new Space. Instead, the Client will attach to the previously-created Space. So it is safe to re-run the `Client.duplicate()` method multiple times.\n\n**Note:** if the original Space uses GPUs, your private Space will as well, and your Hugging Face account will get billed based on the price of the GPU. To minimize charges, your Space will automatically go to sleep after 1 hour of inactivity. You can also set the hardware using the `hardware` parameter of `duplicate()`.\n\n", "heading1": "Duplicating a Space for private use", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "If your app is running somewhere else, just provide the full URL instead, including the \"http://\" or \"https://\". Here's an example of making predictions to a Gradio app that is running on a share URL:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"https://bec81a83-5b5c-471e.gradio.live\")\n```\n\n", "heading1": "Connecting a general Gradio app", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "If the Gradio application you are connecting to [requires a username and password](/guides/sharing-your-appauthentication), then provide them as a tuple to the `auth` argument of the `Client` class:\n\n```python\nfrom gradio_client import Client\n\nClient(\n  space_name,\n  auth=[username, password]\n)\n```\n\n\n", "heading1": "Connecting to a Gradio app with auth", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are available to you by calling the `Client.view_api()` method. For the Whisper Space, we see the following:\n\n```bash\nClient.predict() Usage Info\n---------------------------\nNamed API endpoints: 1\n\n - predict(audio, api_name=\"/predict\") -> output\n    Parameters:\n     - [Audio] audio: filepath (required)  \n    Returns:\n     - [Textbox] output: str \n```\n\nWe see  that we have 1 API endpoint in this space, and shows us how to use the API endpoint to make a prediction: we should call the `.predict()` method (which we will explore below), providing a parameter `input_audio` of type `str`, which is a `filepath or URL`.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()` method. Although this isn't necessary if a Gradio app has only 1 named endpoint, it does allow us to call different endpoints in a single app if they are available.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the \"Use via API\" link in the footer of the Gradio app, which shows us the same information, along with example usage. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \"API Recorder\" that lets you interact with the Gradio UI normally and converts your interactions into the corresponding code to run with the Python Client.\n\n", "heading1": "The \"View API\" Page", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()` function with the appropriate arguments:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/en2fr\", api_name='/predict')\nclient.predict(\"Hello\")\n\n>> Bonjour\n```\n\nIf there are multiple parameters, then you should pass them as separate arguments to `.predict()`, like this:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"gradio/calculator\")\nclient.predict(4, \"add\", 5)\n\n>> 9.0\n```\n\nIt is recommended to provide key-word arguments instead of positional arguments:\n\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"gradio/calculator\")\nclient.predict(num1=4, operation=\"add\", num2=5)\n\n>> 9.0\n```\n\nThis allows you to take advantage of default arguments. For example, this Space includes the default value for the Slider component so you do not need to provide it when accessing it with the client.\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/image_generator\")\nclient.predict(text=\"an astronaut riding a camel\")\n```\n\nThe default value is the initial value of the corresponding Gradio component. If the component does not have an initial value, but if the corresponding argument in the predict function has a default value of `None`, then that parameter is also optional in the client. Of course, if you'd like to override it, you can include it as well:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/image_generator\")\nclient.predict(text=\"an astronaut riding a camel\", steps=25)\n```\n\nFor providing files or URLs as inputs, you should pass in the filepath or URL to the file enclosed within `gradio_client.handle_file()`. This takes care of uploading the file to the Gradio server and ensures that the file is preprocessed correctly:\n\n```python\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/whisper\")\nclient.predict(\n    audio=handle_file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/s", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "```python\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/whisper\")\nclient.predict(\n    audio=handle_file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\")\n)\n\n>> \"My thought I have nobody by a beauty and will as you poured. Mr. Rochester is serve in that so don't find simpus, and devoted abode, to at might in a r\u2014\"\n```\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "One should note that `.predict()` is a _blocking_ operation as it waits for the operation to complete before returning the prediction.\n\nIn many cases, you may be better off letting the job run in the background until you need the results of the prediction. You can do this by creating a `Job` instance using the `.submit()` method, and then later calling `.result()` on the job to get the result. For example:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(space=\"abidlabs/en2fr\")\njob = client.submit(\"Hello\", api_name=\"/predict\")  This is not blocking\n\nDo something else\n\njob.result()  This is blocking\n\n>> Bonjour\n```\n\n", "heading1": "Running jobs asynchronously", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Alternatively, one can add one or more callbacks to perform actions after the job has completed running, like this:\n\n```python\nfrom gradio_client import Client\n\ndef print_result(x):\n    print(\"The translated result is: {x}\")\n\nclient = Client(space=\"abidlabs/en2fr\")\n\njob = client.submit(\"Hello\", api_name=\"/predict\", result_callbacks=[print_result])\n\nDo something else\n\n>> The translated result is: Bonjour\n\n```\n\n", "heading1": "Adding callbacks", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The `Job` object also allows you to get the status of the running job by calling the `.status()` method. This returns a `StatusUpdate` object with the following attributes: `code` (the status code, one of a set of defined strings representing the status. See the `utils.Status` class), `rank` (the current position of this job in the queue), `queue_size` (the total queue size), `eta` (estimated time this job will complete), `success` (a boolean representing whether the job completed successfully), and `time` (the time that the status was generated).\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/calculator\")\njob = client.submit(5, \"add\", 4, api_name=\"/predict\")\njob.status()\n\n>> <Status.STARTING: 'STARTING'>\n```\n\n_Note_: The `Job` class also has a `.done()` instance method which returns a boolean indicating whether the job has completed.\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The `Job` class also has a `.cancel()` instance method that cancels jobs that have been queued but not started. For example, if you run:\n\n```py\nclient = Client(\"abidlabs/whisper\")\njob1 = client.submit(handle_file(\"audio_sample1.wav\"))\njob2 = client.submit(handle_file(\"audio_sample2.wav\"))\njob1.cancel()  will return False, assuming the job has started\njob2.cancel()  will return True, indicating that the job has been canceled\n```\n\nIf the first job has started processing, then it will not be canceled. If the second job\nhas not yet started, it will be successfully canceled and removed from the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a series of values. You can get the series of values that have been returned at any time from such a generator endpoint by running `job.outputs()`:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/count_generator\")\njob = client.submit(3, api_name=\"/count\")\nwhile not job.done():\n    time.sleep(0.1)\njob.outputs()\n\n>> ['0', '1', '2']\n```\n\nNote that running `job.result()` on a generator endpoint only gives you the _first_ value returned by the endpoint.\n\nThe `Job` object is also iterable, which means you can use it to display the results of a generator function as they are returned from the endpoint. Here's the equivalent example using the `Job` as a generator:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/count_generator\")\njob = client.submit(3, api_name=\"/count\")\n\nfor o in job:\n    print(o)\n\n>> 0\n>> 1\n>> 2\n```\n\nYou can also cancel jobs that that have iterative outputs, in which case the job will finish as soon as the current iteration finishes running.\n\n```py\nfrom gradio_client import Client\nimport time\n\nclient = Client(\"abidlabs/test-yield\")\njob = client.submit(\"abcdef\")\ntime.sleep(3)\njob.cancel()  job cancels after 2 iterations\n```\n\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Gradio demos can include [session state](https://www.gradio.app/guides/state-in-blocks), which provides a way for demos to persist information from user interactions within a page session.\n\nFor example, consider the following demo, which maintains a list of words that a user has submitted in a `gr.State` component. When a user submits a new word, it is added to the state, and the number of previous occurrences of that word is displayed:\n\n```python\nimport gradio as gr\n\ndef count(word, list_of_words):\n    return list_of_words.count(word), list_of_words + [word]\n\nwith gr.Blocks() as demo:\n    words = gr.State([])\n    textbox = gr.Textbox()\n    number = gr.Number()\n    textbox.submit(count, inputs=[textbox, words], outputs=[number, words])\n    \ndemo.launch()\n```\n\nIf you were to connect this this Gradio app using the Python Client, you would notice that the API information only shows a single input and output:\n\n```csv\nClient.predict() Usage Info\n---------------------------\nNamed API endpoints: 1\n\n - predict(word, api_name=\"/count\") -> value_31\n    Parameters:\n     - [Textbox] word: str (required)  \n    Returns:\n     - [Number] value_31: float \n```\n\nThat is because the Python client handles state automatically for you -- as you make a series of requests, the returned state from one request is stored internally and automatically supplied for the subsequent request. If you'd like to reset the state, you can do that by calling `Client.reset_session()`.\n", "heading1": "Demos with Session State", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "You generally don't need to install cURL, as it comes pre-installed on many operating systems. Run:\n\n```bash\ncurl --version\n```\n\nto confirm that `curl` is installed. If it is not already installed, you can install it by visiting https://curl.se/download.html. \n\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "To query a Gradio app, you'll need its full URL. This is usually just the URL that the Gradio app is hosted on, for example: https://bec81a83-5b5c-471e.gradio.live\n\n\n**Hugging Face Spaces**\n\nHowever, if you are querying a Gradio on Hugging Face Spaces, you will need to use the URL of the embedded Gradio app, not the URL of the Space webpage. For example:\n\n```bash\n\u274c Space URL: https://huggingface.co/spaces/abidlabs/en2fr\n\u2705 Gradio app URL: https://abidlabs-en2fr.hf.space/\n```\n\nYou can get the Gradio app URL by clicking the \"view API\" link at the bottom of the page. Or, you can right-click on the page and then click on \"View Frame Source\" or the equivalent in your browser to view the URL of the embedded Gradio app.\n\nWhile you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space,\nand then use it to make as many requests as you'd like!\n\nNote: to query private Spaces, you will need to pass in your Hugging Face (HF) token. You can get your HF token here: https://huggingface.co/settings/tokens. In this case, you will need to include an additional header in both of your `curl` calls that we'll discuss below:\n\n```bash\n-H \"Authorization: Bearer $HF_TOKEN\"\n```\n\nNow, we are ready to make the two `curl` requests.\n\n", "heading1": "Step 0: Get the URL for your Gradio App", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "The first of the two `curl` requests is `POST` request that submits the input payload to the Gradio app. \n\nThe syntax of the `POST` request is as follows:\n\n```bash\n$ curl -X POST $URL/call/$API_NAME -H \"Content-Type: application/json\" -d '{\n  \"data\": $PAYLOAD\n}'\n```\n\nHere:\n\n* `$URL` is the URL of the Gradio app as obtained in Step 0\n* `$API_NAME` is the name of the API endpoint for the event that you are running. You can get the API endpoint names by clicking the \"view API\" link at the bottom of the page.\n*  `$PAYLOAD` is a valid JSON data list containing the input payload, one element for each input component.\n\nWhen you make this `POST` request successfully, you will get an event id that is printed to the terminal in this format:\n\n```bash\n>> {\"event_id\": $EVENT_ID}   \n```\n\nThis `EVENT_ID` will be needed in the subsequent `curl` request to fetch the results of the prediction. \n\nHere are some examples of how to make the `POST` request\n\n**Basic Example**\n\nRevisiting the example at the beginning of the page, here is how to make the `POST` request for a simple Gradio application that takes in a single input text component:\n\n```bash\n$ curl -X POST https://abidlabs-en2fr.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Hello, my friend.\"] \n}'\n```\n\n**Multiple Input Components**\n\nThis [Gradio demo](https://huggingface.co/spaces/gradio/hello_world_3) accepts three inputs: a string corresponding to the `gr.Textbox`, a boolean value corresponding to the `gr.Checkbox`, and a numerical value corresponding to the `gr.Slider`. Here is the `POST` request:\n\n```bash\ncurl -X POST https://gradio-hello-world-3.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Hello\", true, 5]\n}'\n```\n\n**Private Spaces**\n\nAs mentioned earlier, if you are making a request to a private Space, you will need to pass in a [Hugging Face token](https://huggingface.co/settings/tokens) that has read access to the Space. The request will look like this:\n\n```bash\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "king a request to a private Space, you will need to pass in a [Hugging Face token](https://huggingface.co/settings/tokens) that has read access to the Space. The request will look like this:\n\n```bash\n$ curl -X POST https://private-space.hf.space/call/predict -H \"Content-Type: application/json\" -H \"Authorization: Bearer $HF_TOKEN\" -d '{\n  \"data\": [\"Hello, my friend.\"] \n}'\n```\n\n**Files**\n\nIf you are using `curl` to query a Gradio application that requires file inputs, the files *need* to be provided as URLs, and The URL needs to be enclosed in a dictionary in this format:\n\n```bash\n{\"path\": $URL}\n```\n\nHere is an example `POST` request:\n\n```bash\n$ curl -X POST https://gradio-image-mod.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n  \"data\": [{\"path\": \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\"}] \n}'\n```\n\n\n**Stateful Demos**\n\nIf your Gradio demo [persists user state](/guides/interface-state) across multiple interactions (e.g. is a chatbot), you can pass in a `session_hash` alongside the `data`. Requests with the same `session_hash` are assumed to be part of the same user session. Here's how that might look:\n\n```bash\nThese two requests will share a session\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Are you sentient?\"],\n  \"session_hash\": \"randomsequence1234\"\n}'\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Really?\"],\n  \"session_hash\": \"randomsequence1234\"\n}'\n\nThis request will be treated as a new session\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Are you sentient?\"],\n  \"session_hash\": \"newsequence5678\"\n}'\n```\n\n\n\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "ient?\"],\n  \"session_hash\": \"newsequence5678\"\n}'\n```\n\n\n\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "Once you have received the `EVENT_ID` corresponding to your prediction, you can stream the results. Gradio stores these results  in a least-recently-used cache in the Gradio app. By default, the cache can store 2,000 results (across all users and endpoints of the app). \n\nTo stream the results for your prediction, make a `GET` request with the following syntax:\n\n```bash\n$ curl -N $URL/call/$API_NAME/$EVENT_ID\n```\n\n\nTip: If you are fetching results from a private Space, include a header with your HF token like this: `-H \"Authorization: Bearer $HF_TOKEN\"` in the `GET` request.\n\nThis should produce a stream of responses in this format:\n\n```bash\nevent: ... \ndata: ...\nevent: ... \ndata: ...\n...\n```\n\nHere: `event` can be one of the following:\n* `generating`: indicating an intermediate result\n* `complete`: indicating that the prediction is complete and the final result \n* `error`: indicating that the prediction was not completed successfully\n* `heartbeat`: sent every 15 seconds to keep the request alive\n\nThe `data` is in the same format as the input payload: valid JSON data list containing the output result, one element for each output component.\n\nHere are some examples of what results you should expect if a request is completed successfully:\n\n**Basic Example**\n\nRevisiting the example at the beginning of the page, we would expect the result to look like this:\n\n```bash\nevent: complete\ndata: [\"Bonjour, mon ami.\"]\n```\n\n**Multiple Outputs**\n\nIf your endpoint returns multiple values, they will appear as elements of the `data` list:\n\n```bash\nevent: complete\ndata: [\"Good morning Hello. It is 5 degrees today\", -15.0]\n```\n\n**Streaming Example**\n\nIf your Gradio app [streams a sequence of values](/guides/streaming-outputs), then they will be streamed directly to your terminal, like this:\n\n```bash\nevent: generating\ndata: [\"Hello, w!\"]\nevent: generating\ndata: [\"Hello, wo!\"]\nevent: generating\ndata: [\"Hello, wor!\"]\nevent: generating\ndata: [\"Hello, worl!\"]\nevent: generating\ndata: [\"Hello, w", "heading1": "Step 2: GET the result", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "```bash\nevent: generating\ndata: [\"Hello, w!\"]\nevent: generating\ndata: [\"Hello, wo!\"]\nevent: generating\ndata: [\"Hello, wor!\"]\nevent: generating\ndata: [\"Hello, worl!\"]\nevent: generating\ndata: [\"Hello, world!\"]\nevent: complete\ndata: [\"Hello, world!\"]\n```\n\n**File Example**\n\nIf your Gradio app returns a file, the file will be represented as a dictionary in this format (including potentially some additional keys):\n\n```python\n{\n    \"orig_name\": \"example.jpg\",\n    \"path\": \"/path/in/server.jpg\",\n    \"url\": \"https:/example.com/example.jpg\",\n    \"meta\": {\"_type\": \"gradio.FileData\"}\n}\n```\n\nIn your terminal, it may appear like this:\n\n```bash\nevent: complete\ndata: [{\"path\": \"/tmp/gradio/359933dc8d6cfe1b022f35e2c639e6e42c97a003/image.webp\", \"url\": \"https://gradio-image-mod.hf.space/c/file=/tmp/gradio/359933dc8d6cfe1b022f35e2c639e6e42c97a003/image.webp\", \"size\": null, \"orig_name\": \"image.webp\", \"mime_type\": null, \"is_stream\": false, \"meta\": {\"_type\": \"gradio.FileData\"}}]\n```\n\n", "heading1": "Step 2: GET the result", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "What if your Gradio application has [authentication enabled](/guides/sharing-your-appauthentication)? In that case, you'll need to make an additional `POST` request with cURL to authenticate yourself before you make any queries. Here are the complete steps:\n\nFirst, login with a `POST` request supplying a valid username and password:\n\n```bash\ncurl -X POST $URL/login \\\n     -d \"username=$USERNAME&password=$PASSWORD\" \\\n     -c cookies.txt\n```\n\nIf the credentials are correct, you'll get `{\"success\":true}` in response and the cookies will be saved in `cookies.txt`.\n\nNext, you'll need to include these cookies when you make the original `POST` request, like this:\n\n```bash\n$ curl -X POST $URL/call/$API_NAME -b cookies.txt -H \"Content-Type: application/json\" -d '{\n  \"data\": $PAYLOAD\n}'\n```\n\nFinally, you'll need to `GET` the results, again supplying the cookies from the file:\n\n```bash\ncurl -N $URL/call/$API_NAME/$EVENT_ID -b cookies.txt\n```\n", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "What are agents?\n\nA [LangChain agent](https://docs.langchain.com/docs/components/agents/agent) is a Large Language Model (LLM) that takes user input and reports an output based on using one of many tools at its disposal.\n\nWhat is Gradio?\n\n[Gradio](https://github.com/gradio-app/gradio) is the defacto standard framework for building Machine Learning Web Applications and sharing them with the world - all with just python! \ud83d\udc0d\n\n", "heading1": "Some background", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "To get started with `gradio_tools`, all you need to do is import and initialize your tools and pass them to the langchain agent!\n\nIn the following example, we import the `StableDiffusionPromptGeneratorTool` to create a good prompt for stable diffusion, the\n`StableDiffusionTool` to create an image with our improved prompt, the `ImageCaptioningTool` to caption the generated image, and\nthe `TextToVideoTool` to create a video from a prompt.\n\nWe then tell our agent to create an image of a dog riding a skateboard, but to please improve our prompt ahead of time. We also ask\nit to caption the generated image and create a video for it. The agent can decide which tool to use without us explicitly telling it.\n\n```python\nimport os\n\nif not os.getenv(\"OPENAI_API_KEY\"):\n    raise ValueError(\"OPENAI_API_KEY must be set\")\n\nfrom langchain.agents import initialize_agent\nfrom langchain.llms import OpenAI\nfrom gradio_tools import (StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool,\n                          TextToVideoTool)\n\nfrom langchain.memory import ConversationBufferMemory\n\nllm = OpenAI(temperature=0)\nmemory = ConversationBufferMemory(memory_key=\"chat_history\")\ntools = [StableDiffusionTool().langchain, ImageCaptioningTool().langchain,\n         StableDiffusionPromptGeneratorTool().langchain, TextToVideoTool().langchain]\n\n\nagent = initialize_agent(tools, llm, memory=memory, agent=\"conversational-react-description\", verbose=True)\noutput = agent.run(input=(\"Please create a photo of a dog riding a skateboard \"\n                          \"but improve my prompt prior to using an image generator.\"\n                          \"Please caption the generated image and create a video for it using the improved prompt.\"))\n```\n\nYou'll note that we are using some pre-built tools that come with `gradio_tools`. Please see this [doc](https://github.com/freddyaboulton/gradio-toolsgradio-tools-gradio--llm-agents) for a complete list of the tools that come with `gradio_tools`.\nIf ", "heading1": "gradio_tools - An end-to-end example", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "that come with `gradio_tools`. Please see this [doc](https://github.com/freddyaboulton/gradio-toolsgradio-tools-gradio--llm-agents) for a complete list of the tools that come with `gradio_tools`.\nIf you would like to use a tool that's not currently in `gradio_tools`, it is very easy to add your own. That's what the next section will cover.\n\n", "heading1": "gradio_tools - An end-to-end example", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "The core abstraction is the `GradioTool`, which lets you define a new tool for your LLM as long as you implement a standard interface:\n\n```python\nclass GradioTool(BaseTool):\n\n    def __init__(self, name: str, description: str, src: str) -> None:\n\n    @abstractmethod\n    def create_job(self, query: str) -> Job:\n        pass\n\n    @abstractmethod\n    def postprocess(self, output: Tuple[Any] | Any) -> str:\n        pass\n```\n\nThe requirements are:\n\n1. The name for your tool\n2. The description for your tool. This is crucial! Agents decide which tool to use based on their description. Be precise and be sure to include example of what the input and the output of the tool should look like.\n3. The url or space id, e.g. `freddyaboulton/calculator`, of the Gradio application. Based on this value, `gradio_tool` will create a [gradio client](https://github.com/gradio-app/gradio/blob/main/client/python/README.md) instance to query the upstream application via API. Be sure to click the link and learn more about the gradio client library if you are not familiar with it.\n4. create_job - Given a string, this method should parse that string and return a job from the client. Most times, this is as simple as passing the string to the `submit` function of the client. More info on creating jobs [here](https://github.com/gradio-app/gradio/blob/main/client/python/README.mdmaking-a-prediction)\n5. postprocess - Given the result of the job, convert it to a string the LLM can display to the user.\n6. _Optional_ - Some libraries, e.g. [MiniChain](https://github.com/srush/MiniChain/tree/main), may need some info about the underlying gradio input and output types used by the tool. By default, this will return gr.Textbox() but\n   if you'd like to provide more accurate info, implement the `_block_input(self, gr)` and `_block_output(self, gr)` methods of the tool. The `gr` variable is the gradio module (the result of `import gradio as gr`). It will be\n   automatically imported by the `GradiTool` parent", "heading1": "gradio_tools - creating your own tool", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "lf, gr)` and `_block_output(self, gr)` methods of the tool. The `gr` variable is the gradio module (the result of `import gradio as gr`). It will be\n   automatically imported by the `GradiTool` parent class and passed to the `_block_input` and `_block_output` methods.\n\nAnd that's it!\n\nOnce you have created your tool, open a pull request to the `gradio_tools` repo! We welcome all contributions.\n\n", "heading1": "gradio_tools - creating your own tool", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "Here is the code for the StableDiffusion tool as an example:\n\n```python\nfrom gradio_tool import GradioTool\nimport os\n\nclass StableDiffusionTool(GradioTool):\n    \"\"\"Tool for calling stable diffusion from llm\"\"\"\n\n    def __init__(\n        self,\n        name=\"StableDiffusion\",\n        description=(\n            \"An image generator. Use this to generate images based on \"\n            \"text input. Input should be a description of what the image should \"\n            \"look like. The output will be a path to an image file.\"\n        ),\n        src=\"gradio-client-demos/stable-diffusion\",\n        hf_token=None,\n    ) -> None:\n        super().__init__(name, description, src, hf_token)\n\n    def create_job(self, query: str) -> Job:\n        return self.client.submit(query, \"\", 9, fn_index=1)\n\n    def postprocess(self, output: str) -> str:\n        return [os.path.join(output, i) for i in os.listdir(output) if not i.endswith(\"json\")][0]\n\n    def _block_input(self, gr) -> \"gr.components.Component\":\n        return gr.Textbox()\n\n    def _block_output(self, gr) -> \"gr.components.Component\":\n        return gr.Image()\n```\n\nSome notes on this implementation:\n\n1. All instances of `GradioTool` have an attribute called `client` that is a pointed to the underlying [gradio client](https://github.com/gradio-app/gradio/tree/main/client/pythongradio_client-use-a-gradio-app-as-an-api----in-3-lines-of-python). That is what you should use\n   in the `create_job` method.\n2. `create_job` just passes the query string to the `submit` function of the client with some other parameters hardcoded, i.e. the negative prompt string and the guidance scale. We could modify our tool to also accept these values from the input string in a subsequent version.\n3. The `postprocess` method simply returns the first image from the gallery of images created by the stable diffusion space. We use the `os` module to get the full path of the image.\n\n", "heading1": "Example tool - Stable Diffusion", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "You now know how to extend the abilities of your LLM with the 1000s of gradio spaces running in the wild!\nAgain, we welcome any contributions to the [gradio_tools](https://github.com/freddyaboulton/gradio-tools) library.\nWe're excited to see the tools you all build!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "Gradio-Lite\n\nGradio-Lite is the serverless version of Gradio, allowing you to build serverless web UI applications by embedding Python code within HTML. For a detailed introduction to Gradio-Lite itself, please read [this Guide](./gradio-lite).\n\nTransformers.js and Transformers.js.py\n\nTransformers.js is the JavaScript version of the Transformers library that allows you to run machine learning models entirely in the browser.\nSince Transformers.js is a JavaScript library, it cannot be directly used from the Python code of Gradio-Lite applications. To address this, we use a wrapper library called [Transformers.js.py](https://github.com/whitphx/transformers.js.py).\nThe name Transformers.js.py may sound unusual, but it represents the necessary technology stack for using Transformers.js from Python code within a browser environment. The regular Transformers library is not compatible with browser environments.\n\n", "heading1": "Libraries Used", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "Here's an example of how to use Gradio-Lite and Transformers.js together.\nPlease create an HTML file and paste the following code:\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n\npipe = await pipeline('sentiment-analysis')\n\ndemo = gr.Interface.from_pipeline(pipe)\n\ndemo.launch()\n\n\t\t\t<gradio-requirements>\ntransformers-js-py\n\t\t\t</gradio-requirements>\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nHere is a running example of the code above (after the app has loaded, you could disconnect your Internet connection and the app will still work since its running entirely in your browser):\n\n<gradio-lite shared-worker>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n<!-- --->\npipe = await pipeline('sentiment-analysis')\n<!-- --->\ndemo = gr.Interface.from_pipeline(pipe)\n<!-- --->\ndemo.launch()\n<gradio-requirements>\ntransformers-js-py\n</gradio-requirements>\n</gradio-lite>\n\nAnd you you can open your HTML file in a browser to see the Gradio app running!\n\nThe Python code inside the `<gradio-lite>` tag is the Gradio application code. For more details on this part, please refer to [this article](./gradio-lite).\nThe `<gradio-requirements>` tag is used to specify packages to be installed in addition to Gradio-Lite and its dependencies. In this case, we are using Transformers.js.py (`transformers-js-py`), so it is specified here.\n\nLet's break down the code:\n\n`pipe = await pipeline('sentiment-analysis')` creates a Transformers.js pipeline.\nIn this example, we create a sentiment analysis pipeline.\nFor more information on the available pipeline types and usage, please refer to the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/index).\n\n`demo = gr.Interface.from_pipeline(pipe)` creates a Gradio a", "heading1": "Sample Code", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "vailable pipeline types and usage, please refer to the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/index).\n\n`demo = gr.Interface.from_pipeline(pipe)` creates a Gradio app instance. By passing the Transformers.js.py pipeline to `gr.Interface.from_pipeline()`, we can create an interface that utilizes that pipeline with predefined input and output components.\n\nFinally, `demo.launch()` launches the created app.\n\n", "heading1": "Sample Code", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "You can modify the line `pipe = await pipeline('sentiment-analysis')` in the sample above to try different models or tasks.\n\nFor example, if you change it to `pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment')`, you can test the same sentiment analysis task but with a different model. The second argument of the `pipeline` function specifies the model name.\nIf it's not specified like in the first example, the default model is used. For more details on these specs, refer to the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/index).\n\n<gradio-lite shared-worker>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n<!-- --->\npipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment')\n<!-- --->\ndemo = gr.Interface.from_pipeline(pipe)\n<!-- --->\ndemo.launch()\n<gradio-requirements>\ntransformers-js-py\n</gradio-requirements>\n</gradio-lite>\n\nAs another example, changing it to `pipe = await pipeline('image-classification')` creates a pipeline for image classification instead of sentiment analysis.\nIn this case, the interface created with `demo = gr.Interface.from_pipeline(pipe)` will have a UI for uploading an image and displaying the classification result. The `gr.Interface.from_pipeline` function automatically creates an appropriate UI based on the type of pipeline.\n\n<gradio-lite shared-worker>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n<!-- --->\npipe = await pipeline('image-classification')\n<!-- --->\ndemo = gr.Interface.from_pipeline(pipe)\n<!-- --->\ndemo.launch()\n<gradio-requirements>\ntransformers-js-py\n</gradio-requirements>\n</gradio-lite>\n\n<br>\n\n**Note**: If you use an audio pipeline, such as `automatic-speech-recognition`, you will need to put `transformers-js-py[audio]` in your `<gradio-requirements>` as there are additional requirements needed to process audio files.\n\n", "heading1": "Customizing the Model or Pipeline", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "Instead of using `gr.Interface.from_pipeline()`, you can define the user interface using Gradio's regular API.\nHere's an example where the Python code inside the `<gradio-lite>` tag has been modified from the previous sample:\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n\npipe = await pipeline('sentiment-analysis')\n\nasync def fn(text):\n\tresult = await pipe(text)\n\treturn result\n\ndemo = gr.Interface(\n\tfn=fn,\n\tinputs=gr.Textbox(),\n\toutputs=gr.JSON(),\n)\n\ndemo.launch()\n\n\t\t\t<gradio-requirements>\ntransformers-js-py\n\t\t\t</gradio-requirements>\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nIn this example, we modified the code to construct the Gradio user interface manually so that we could output the result as JSON.\n\n<gradio-lite shared-worker>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n<!-- --->\npipe = await pipeline('sentiment-analysis')\n<!-- --->\nasync def fn(text):\n\tresult = await pipe(text)\n\treturn result\n<!-- --->\ndemo = gr.Interface(\n\tfn=fn,\n\tinputs=gr.Textbox(),\n\toutputs=gr.JSON(),\n)\n<!-- --->\ndemo.launch()\n<gradio-requirements>\ntransformers-js-py\n</gradio-requirements>\n</gradio-lite>\n\n", "heading1": "Customizing the UI", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "By combining Gradio-Lite and Transformers.js (and Transformers.js.py), you can create serverless machine learning applications that run entirely in the browser.\n\nGradio-Lite provides a convenient method to create an interface for a given Transformers.js pipeline, `gr.Interface.from_pipeline()`.\nThis method automatically constructs the interface based on the pipeline's task type.\n\nAlternatively, you can define the interface manually using Gradio's regular API, as shown in the second example.\n\nBy using these libraries, you can build and deploy machine learning applications without the need for server-side Python setup or external dependencies.\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "Did you know that apart from being a full-stack machine learning demo, a Gradio Blocks app is also a regular-old python function!?\n\nThis means that if you have a gradio Blocks (or Interface) app called `demo`, you can use `demo` like you would any python function.\n\nSo doing something like `output = demo(\"Hello\", \"friend\")` will run the first event defined in `demo` on the inputs \"Hello\" and \"friend\" and store it\nin the variable `output`.\n\nIf I put you to sleep \ud83e\udd71, please bear with me! By using apps like functions, you can seamlessly compose Gradio apps.\nThe following section will show how.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-blocks-like-functions", "source_page_title": "Building With Blocks - Using Blocks Like Functions Guide"}, {"text": "Let's say we have the following demo that translates english text to german text.\n\n$code_english_translator\n\nI already went ahead and hosted it in Hugging Face spaces at [gradio/english_translator](https://huggingface.co/spaces/gradio/english_translator).\n\nYou can see the demo below as well:\n\n$demo_english_translator\n\nNow, let's say you have an app that generates english text, but you wanted to additionally generate german text.\n\nYou could either:\n\n1. Copy the source code of my english-to-german translation and paste it in your app.\n\n2. Load my english-to-german translation in your app and treat it like a normal python function.\n\nOption 1 technically always works, but it often introduces unwanted complexity.\n\nOption 2 lets you borrow the functionality you want without tightly coupling our apps.\n\nAll you have to do is call the `Blocks.load` class method in your source file.\nAfter that, you can use my translation app like a regular python function!\n\nThe following code snippet and demo shows how to use `Blocks.load`.\n\nNote that the variable `english_translator` is my english to german app, but its used in `generate_text` like a regular function.\n\n$code_generate_english_german\n\n$demo_generate_english_german\n\n", "heading1": "Treating Blocks like functions", "source_page_url": "https://gradio.app/guides/using-blocks-like-functions", "source_page_title": "Building With Blocks - Using Blocks Like Functions Guide"}, {"text": "If the app you are loading defines more than one function, you can specify which function to use\nwith the `fn_index` and `api_name` parameters.\n\nIn the code for our english to german demo, you'll see the following line:\n\n```python\ntranslate_btn.click(translate, inputs=english, outputs=german, api_name=\"translate-to-german\")\n```\n\nThe `api_name` gives this function a unique name in our app. You can use this name to tell gradio which\nfunction in the upstream space you want to use:\n\n```python\nenglish_generator(text, api_name=\"translate-to-german\")[0][\"generated_text\"]\n```\n\nYou can also use the `fn_index` parameter.\nImagine my app also defined an english to spanish translation function.\nIn order to use it in our text generation app, we would use the following code:\n\n```python\nenglish_generator(text, fn_index=1)[0][\"generated_text\"]\n```\n\nFunctions in gradio spaces are zero-indexed, so since the spanish translator would be the second function in my space,\nyou would use index 1.\n\n", "heading1": "How to control which function in the app to use", "source_page_url": "https://gradio.app/guides/using-blocks-like-functions", "source_page_title": "Building With Blocks - Using Blocks Like Functions Guide"}, {"text": "We showed how treating a Blocks app like a regular python helps you compose functionality across different apps.\nAny Blocks app can be treated like a function, but a powerful pattern is to `load` an app hosted on\n[Hugging Face Spaces](https://huggingface.co/spaces) prior to treating it like a function in your own app.\nYou can also load models hosted on the [Hugging Face Model Hub](https://huggingface.co/models) - see the [Using Hugging Face Integrations](/using_hugging_face_integrations) guide for an example.\n\nHappy building! \u2692\ufe0f\n", "heading1": "Parting Remarks", "source_page_url": "https://gradio.app/guides/using-blocks-like-functions", "source_page_title": "Building With Blocks - Using Blocks Like Functions Guide"}, {"text": "Take a look at the demo below.\n\n$code_hello_blocks\n$demo_hello_blocks\n\n- First, note the `with gr.Blocks() as demo:` clause. The Blocks app code will be contained within this clause.\n- Next come the Components. These are the same Components used in `Interface`. However, instead of being passed to some constructor, Components are automatically added to the Blocks as they are created within the `with` clause.\n- Finally, the `click()` event listener. Event listeners define the data flow within the app. In the example above, the listener ties the two Textboxes together. The Textbox `name` acts as the input and Textbox `output` acts as the output to the `greet` method. This dataflow is triggered when the Button `greet_btn` is clicked. Like an Interface, an event listener can take multiple inputs or outputs.\n\nYou can also attach event listeners using decorators - skip the `fn` argument and assign `inputs` and `outputs` directly:\n\n$code_hello_blocks_decorator\n\n", "heading1": "Blocks Structure", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "In the example above, you'll notice that you are able to edit Textbox `name`, but not Textbox `output`. This is because any Component that acts as an input to an event listener is made interactive. However, since Textbox `output` acts only as an output, Gradio determines that it should not be made interactive. You can override the default behavior and directly configure the interactivity of a Component with the boolean `interactive` keyword argument, e.g. `gr.Textbox(interactive=True)`.\n\n```python\noutput = gr.Textbox(label=\"Output\", interactive=True)\n```\n\n_Note_: What happens if a Gradio component is neither an input nor an output? If a component is constructed with a default value, then it is presumed to be displaying content and is rendered non-interactive. Otherwise, it is rendered interactive. Again, this behavior can be overridden by specifying a value for the `interactive` argument.\n\n", "heading1": "Event Listeners and Interactivity", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Take a look at the demo below:\n\n$code_blocks_hello\n$demo_blocks_hello\n\nInstead of being triggered by a click, the `welcome` function is triggered by typing in the Textbox `inp`. This is due to the `change()` event listener. Different Components support different event listeners. For example, the `Video` Component supports a `play()` event listener, triggered when a user presses play. See the [Docs](http://gradio.app/docscomponents) for the event listeners for each Component.\n\n", "heading1": "Types of Event Listeners", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "A Blocks app is not limited to a single data flow the way Interfaces are. Take a look at the demo below:\n\n$code_reversible_flow\n$demo_reversible_flow\n\nNote that `num1` can act as input to `num2`, and also vice-versa! As your apps get more complex, you will have many data flows connecting various Components.\n\nHere's an example of a \"multi-step\" demo, where the output of one model (a speech-to-text model) gets fed into the next model (a sentiment classifier).\n\n$code_blocks_speech_text_sentiment\n$demo_blocks_speech_text_sentiment\n\n", "heading1": "Multiple Data Flows", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "The event listeners you've seen so far have a single input component. If you'd like to have multiple input components pass data to the function, you have two options on how the function can accept input component values:\n\n1. as a list of arguments, or\n2. as a single dictionary of values, keyed by the component\n\nLet's see an example of each:\n$code_calculator_list_and_dict\n\nBoth `add()` and `sub()` take `a` and `b` as inputs. However, the syntax is different between these listeners.\n\n1. To the `add_btn` listener, we pass the inputs as a list. The function `add()` takes each of these inputs as arguments. The value of `a` maps to the argument `num1`, and the value of `b` maps to the argument `num2`.\n2. To the `sub_btn` listener, we pass the inputs as a set (note the curly brackets!). The function `sub()` takes a single dictionary argument `data`, where the keys are the input components, and the values are the values of those components.\n\nIt is a matter of preference which syntax you prefer! For functions with many input components, option 2 may be easier to manage.\n\n$demo_calculator_list_and_dict\n\n", "heading1": "Function Input List vs Dict", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Similarly, you may return values for multiple output components either as:\n\n1. a list of values, or\n2. a dictionary keyed by the component\n\nLet's first see an example of (1), where we set the values of two output components by returning two values:\n\n```python\nwith gr.Blocks() as demo:\n    food_box = gr.Number(value=10, label=\"Food Count\")\n    status_box = gr.Textbox()\n\n    def eat(food):\n        if food > 0:\n            return food - 1, \"full\"\n        else:\n            return 0, \"hungry\"\n\n    gr.Button(\"Eat\").click(\n        fn=eat,\n        inputs=food_box,\n        outputs=[food_box, status_box]\n    )\n```\n\nAbove, each return statement returns two values corresponding to `food_box` and `status_box`, respectively.\n\n**Note:** if your event listener has a single output component, you should **not** return it as a single-item list. This will not work, since Gradio does not know whether to interpret that outer list as part of your return value. You should instead just return that value directly.\n\nNow, let's see option (2). Instead of returning a list of values corresponding to each output component in order, you can also return a dictionary, with the key corresponding to the output component and the value as the new value. This also allows you to skip updating some output components.\n\n```python\nwith gr.Blocks() as demo:\n    food_box = gr.Number(value=10, label=\"Food Count\")\n    status_box = gr.Textbox()\n\n    def eat(food):\n        if food > 0:\n            return {food_box: food - 1, status_box: \"full\"}\n        else:\n            return {status_box: \"hungry\"}\n\n    gr.Button(\"Eat\").click(\n        fn=eat,\n        inputs=food_box,\n        outputs=[food_box, status_box]\n    )\n```\n\nNotice how when there is no food, we only update the `status_box` element. We skipped updating the `food_box` component.\n\nDictionary returns are helpful when an event listener affects many components on return, or conditionally affects outputs and not others.\n\nKeep in mind that with dictionary returns,", "heading1": "Function Return List vs Dict", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "d_box` component.\n\nDictionary returns are helpful when an event listener affects many components on return, or conditionally affects outputs and not others.\n\nKeep in mind that with dictionary returns, we still need to specify the possible outputs in the event listener.\n\n", "heading1": "Function Return List vs Dict", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "The return value of an event listener function is usually the updated value of the corresponding output Component. Sometimes we want to update the configuration of the Component as well, such as the visibility. In this case, we return a new Component, setting the properties we want to change.\n\n$code_blocks_essay_simple\n$demo_blocks_essay_simple\n\nSee how we can configure the Textbox itself through a new `gr.Textbox()` method. The `value=` argument can still be used to update the value along with Component configuration. Any arguments we do not set will preserve their previous values.\n\n", "heading1": "Updating Component Configurations", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "In some cases, you may want to leave a component's value unchanged. Gradio includes a special function, `gr.skip()`, which can be returned from your function. Returning this function will keep the output component (or components') values as is. Let us illustrate with an example:\n\n$code_skip\n$demo_skip\n\nNote the difference between returning `None` (which generally resets a component's value to an empty state) versus returning `gr.skip()`, which leaves the component value unchanged.\n\nTip: if you have multiple output components, and you want to leave all of their values unchanged, you can just return a single `gr.skip()` instead of returning a tuple of skips, one for each element.\n\n", "heading1": "Not Changing a Component's Value", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "You can also run events consecutively by using the `then` method of an event listener. This will run an event after the previous event has finished running. This is useful for running events that update components in multiple steps.\n\nFor example, in the chatbot example below, we first update the chatbot with the user message immediately, and then update the chatbot with the computer response after a simulated delay.\n\n$code_chatbot_consecutive\n$demo_chatbot_consecutive\n\nThe `.then()` method of an event listener executes the subsequent event regardless of whether the previous event raised any errors. If you'd like to only run subsequent events if the previous event executed successfully, use the `.success()` method, which takes the same arguments as `.then()`.\n\n", "heading1": "Running Events Consecutively", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Often times, you may want to bind multiple triggers to the same function. For example, you may want to allow a user to click a submit button, or press enter to submit a form. You can do this using the `gr.on` method and passing a list of triggers to the `trigger`.\n\n$code_on_listener_basic\n$demo_on_listener_basic\n\nYou can use decorator syntax as well:\n\n$code_on_listener_decorator\n\nYou can use `gr.on` to create \"live\" events by binding to the `change` event of components that implement it. If you do not specify any triggers, the function will automatically bind to all `change` event of all input components that include a `change` event (for example `gr.Textbox` has a `change` event whereas `gr.Button` does not).\n\n$code_on_listener_live\n$demo_on_listener_live\n\nYou can follow `gr.on` with `.then`, just like any regular event listener. This handy method should save you from having to write a lot of repetitive code!\n\n", "heading1": "Binding Multiple Triggers to a Function", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "If you want to set a Component's value to always be a function of the value of other Components, you can use the following shorthand:\n\n```python\nwith gr.Blocks() as demo:\n  num1 = gr.Number()\n  num2 = gr.Number()\n  product = gr.Number(lambda a, b: a * b, inputs=[num1, num2])\n```\n\nThis functionally the same as:\n```python\nwith gr.Blocks() as demo:\n  num1 = gr.Number()\n  num2 = gr.Number()\n  product = gr.Number()\n\n  gr.on(\n    [num1.change, num2.change, demo.load], \n    lambda a, b: a * b, \n    inputs=[num1, num2], \n    outputs=product\n  )\n```\n", "heading1": "Binding a Component Value Directly to a Function of Other Components", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Elements within a `with gr.Row` clause will all be displayed horizontally. For example, to display two Buttons side by side:\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Row():\n        btn1 = gr.Button(\"Button 1\")\n        btn2 = gr.Button(\"Button 2\")\n```\n\nYou can set every element in a Row to have the same height. Configure this with the `equal_height` argument.\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Row(equal_height=True):\n        textbox = gr.Textbox()\n        btn2 = gr.Button(\"Button 2\")\n```\n\nThe widths of elements in a Row can be controlled via a combination of `scale` and `min_width` arguments that are present in every Component.\n\n- `scale` is an integer that defines how an element will take up space in a Row. If scale is set to `0`, the element will not expand to take up space. If scale is set to `1` or greater, the element will expand. Multiple elements in a row will expand proportional to their scale. Below, `btn2` will expand twice as much as `btn1`, while `btn0` will not expand at all:\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Row():\n        btn0 = gr.Button(\"Button 0\", scale=0)\n        btn1 = gr.Button(\"Button 1\", scale=1)\n        btn2 = gr.Button(\"Button 2\", scale=2)\n```\n\n- `min_width` will set the minimum width the element will take. The Row will wrap if there isn't sufficient space to satisfy all `min_width` values.\n\nLearn more about Rows in the [docs](https://gradio.app/docs/row).\n\n", "heading1": "Rows", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Components within a Column will be placed vertically atop each other. Since the vertical layout is the default layout for Blocks apps anyway, to be useful, Columns are usually nested within Rows. For example:\n\n$code_rows_and_columns\n$demo_rows_and_columns\n\nSee how the first column has two Textboxes arranged vertically. The second column has an Image and Button arranged vertically. Notice how the relative widths of the two columns is set by the `scale` parameter. The column with twice the `scale` value takes up twice the width.\n\nLearn more about Columns in the [docs](https://gradio.app/docs/column).\n\nFill Browser Height / Width\n\nTo make an app take the full width of the browser by removing the side padding, use `gr.Blocks(fill_width=True)`. \n\nTo make top level Components expand to take the full height of the browser, use `fill_height` and apply scale to the expanding Components.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks(fill_height=True) as demo:\n    gr.Chatbot(scale=1)\n    gr.Textbox(scale=0)\n```\n\n", "heading1": "Columns and Nesting", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Some components support setting height and width. These parameters accept either a number (interpreted as pixels) or a string. Using a string allows the direct application of any CSS unit to the encapsulating Block element.\n\nBelow is an example illustrating the use of viewport width (vw):\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    im = gr.ImageEditor(width=\"50vw\")\n\ndemo.launch()\n```\n\n", "heading1": "Dimensions", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "You can also create Tabs using the `with gr.Tab('tab_name'):` clause. Any component created inside of a `with gr.Tab('tab_name'):` context appears in that tab. Consecutive Tab clauses are grouped together so that a single tab can be selected at one time, and only the components within that Tab's context are shown.\n\nFor example:\n\n$code_blocks_flipper\n$demo_blocks_flipper\n\nAlso note the `gr.Accordion('label')` in this example. The Accordion is a layout that can be toggled open or closed. Like `Tabs`, it is a layout element that can selectively hide or show content. Any components that are defined inside of a `with gr.Accordion('label'):` will be hidden or shown when the accordion's toggle icon is clicked.\n\nLearn more about [Tabs](https://gradio.app/docs/tab) and [Accordions](https://gradio.app/docs/accordion) in the docs.\n\n", "heading1": "Tabs and Accordions", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "The sidebar is a collapsible panel that renders child components on the left side of the screen and can be expanded or collapsed.\n\nFor example:\n\n$code_blocks_sidebar\n\nLearn more about [Sidebar](https://gradio.app/docs/gradio/sidebar) in the docs.\n\n", "heading1": "Sidebar", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Both Components and Layout elements have a `visible` argument that can set initially and also updated. Setting `gr.Column(visible=...)` on a Column can be used to show or hide a set of Components.\n\n$code_blocks_form\n$demo_blocks_form\n\n", "heading1": "Visibility", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "In some cases, you might want to define components before you actually render them in your UI. For instance, you might want to show an examples section using `gr.Examples` above the corresponding `gr.Textbox` input. Since `gr.Examples` requires as a parameter the input component object, you will need to first define the input component, but then render it later, after you have defined the `gr.Examples` object.\n\nThe solution to this is to define the `gr.Textbox` outside of the `gr.Blocks()` scope and use the component's `.render()` method wherever you'd like it placed in the UI.\n\nHere's a full code example:\n\n```python\ninput_textbox = gr.Textbox()\n\nwith gr.Blocks() as demo:\n    gr.Examples([\"hello\", \"bonjour\", \"merhaba\"], input_textbox)\n    input_textbox.render()\n```\n\nSimilarly, if you have already defined a component in a Gradio app, but wish to unrender it so that you can define in a different part of your application, then you can call the `.unrender()` method. In the following example, the `Textbox` will appear in the third column:\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    with gr.Row():\n        with gr.Column():\n            gr.Markdown(\"Row 1\")\n            textbox = gr.Textbox()\n        with gr.Column():\n            gr.Markdown(\"Row 2\")\n            textbox.unrender()\n        with gr.Column():\n            gr.Markdown(\"Row 3\")\n            textbox.render()\n\ndemo.launch()\n```\n", "heading1": "Defining and Rendering Components Separately", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Global state in Gradio apps is very simple: any variable created outside of a function is shared globally between all users.\n\nThis makes managing global state very simple and without the need for external services. For example, in this application, the `visitor_count` variable is shared between all users\n\n```py\nimport gradio as gr\n\nShared between all users\nvisitor_count = 0\n\ndef increment_counter():\n    global visitor_count\n    visitor_count += 1\n    return visitor_count\n\nwith gr.Blocks() as demo:    \n    number = gr.Textbox(label=\"Total Visitors\", value=\"Counting...\")\n    demo.load(increment_counter, inputs=None, outputs=number)\n\ndemo.launch()\n```\n\nThis means that any time you do _not_ want to share a value between users, you should declare it _within_ a function. But what if you need to share values between function calls, e.g. a chat history? In that case, you should use one of the subsequent approaches to manage state.\n\n", "heading1": "Global State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Gradio supports session state, where data persists across multiple submits within a page session. To reiterate, session data is _not_ shared between different users of your model, and does _not_ persist if a user refreshes the page to reload the Gradio app. To store data in a session state, you need to do three things:\n\n1. Create a `gr.State()` object. If there is a default value to this stateful object, pass that into the constructor. Note that `gr.State` objects must be [deepcopy-able](https://docs.python.org/3/library/copy.html), otherwise you will need to use a different approach as described below.\n2. In the event listener, put the `State` object as an input and output as needed.\n3. In the event listener function, add the variable to the input parameters and the return value.\n\nLet's take a look at a simple example. We have a simple checkout app below where you add items to a cart. You can also see the size of the cart.\n\n$code_simple_state\n\nNotice how we do this with state:\n\n1. We store the cart items in a `gr.State()` object, initialized here to be an empty list.\n2. When adding items to the cart, the event listener uses the cart as both input and output - it returns the updated cart with all the items inside. \n3. We can attach a `.change` listener to cart, that uses the state variable as input as well.\n\nYou can think of `gr.State` as an invisible Gradio component that can store any kind of value. Here, `cart` is not visible in the frontend but is used for calculations.\n\nThe `.change` listener for a state variable triggers after any event listener changes the value of a state variable. If the state variable holds a sequence (like a `list`, `set`, or `dict`), a change is triggered if any of the elements inside change. If it holds an object or primitive, a change is triggered if the **hash** of the  value changes. So if you define a custom class and create a `gr.State` variable that is an instance of that class, make sure that the the class includes a sensible `__", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "riggered if the **hash** of the  value changes. So if you define a custom class and create a `gr.State` variable that is an instance of that class, make sure that the the class includes a sensible `__hash__` implementation.\n\nThe value of a session State variable is cleared when the user refreshes the page. The value is stored on in the app backend for 60 minutes after the user closes the tab (this can be configured by the `delete_cache` parameter in `gr.Blocks`).\n\nLearn more about `State` in the [docs](https://gradio.app/docs/gradio/state).\n\n**What about objects that cannot be deepcopied?**\n\nAs mentioned earlier, the value stored in `gr.State` must be [deepcopy-able](https://docs.python.org/3/library/copy.html). If you are working with a complex object that cannot be deepcopied, you can take a different approach to manually read the user's `session_hash` and store a global `dictionary` with instances of your object for each user. Here's how you would do that:\n\n```py\nimport gradio as gr\n\nclass NonDeepCopyable:\n    def __init__(self):\n        from threading import Lock\n        self.counter = 0\n        self.lock = Lock()  Lock objects cannot be deepcopied\n    \n    def increment(self):\n        with self.lock:\n            self.counter += 1\n            return self.counter\n\nGlobal dictionary to store user-specific instances\ninstances = {}\n\ndef initialize_instance(request: gr.Request):\n    instances[request.session_hash] = NonDeepCopyable()\n    return \"Session initialized!\"\n\ndef cleanup_instance(request: gr.Request):\n    if request.session_hash in instances:\n        del instances[request.session_hash]\n\ndef increment_counter(request: gr.Request):\n    if request.session_hash in instances:\n        instance = instances[request.session_hash]\n        return instance.increment()\n    return \"Error: Session not initialized\"\n\nwith gr.Blocks() as demo:\n    output = gr.Textbox(label=\"Status\")\n    counter = gr.Number(label=\"Counter Value\")\n    increment_btn = gr.Button(\"Increment Co", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "   return \"Error: Session not initialized\"\n\nwith gr.Blocks() as demo:\n    output = gr.Textbox(label=\"Status\")\n    counter = gr.Number(label=\"Counter Value\")\n    increment_btn = gr.Button(\"Increment Counter\")\n    increment_btn.click(increment_counter, inputs=None, outputs=counter)\n    \n    Initialize instance when page loads\n    demo.load(initialize_instance, inputs=None, outputs=output)    \n    Clean up instance when page is closed/refreshed\n    demo.unload(cleanup_instance)    \n\ndemo.launch()\n```\n\n", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Gradio also supports browser state, where data persists in the browser's localStorage even after the page is refreshed or closed. This is useful for storing user preferences, settings, API keys, or other data that should persist across sessions. To use local state:\n\n1. Create a `gr.BrowserState` object. You can optionally provide an initial default value and a key to identify the data in the browser's localStorage.\n2. Use it like a regular `gr.State` component in event listeners as inputs and outputs.\n\nHere's a simple example that saves a user's username and password across sessions:\n\n$code_browserstate\n\nNote: The value stored in `gr.BrowserState` does not persist if the Grado app is restarted. To persist it, you can hardcode specific values of `storage_key` and `secret` in the `gr.BrowserState` component and restart the Gradio app on the same server name and server port. However, this should only be done if you are running trusted Gradio apps, as in principle, this can allow one Gradio app to access localStorage data that was created by a different Gradio app.\n", "heading1": "Browser State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Gradio themes are the easiest way to customize the look and feel of your app. You can choose from a variety of themes, or create your own. To do so, pass the `theme=` kwarg to the `Blocks` constructor. For example:\n\n```python\nwith gr.Blocks(theme=gr.themes.Glass()):\n    ...\n```\n\nGradio comes with a set of prebuilt themes which you can load from `gr.themes.*`. You can extend these themes or create your own themes from scratch - see the [Theming guide](/guides/theming-guide) for more details.\n\nFor additional styling ability, you can pass any CSS to your app as a string using the `css=` kwarg. You can also pass a pathlib.Path to a css file or a list of such paths to the `css_paths=` kwarg.\n\n**Warning**: The use of query selectors in custom JS and CSS is _not_ guaranteed to work across Gradio versions that bind to Gradio's own HTML elements as the Gradio HTML DOM may change. We recommend using query selectors sparingly.\n\nThe base class for the Gradio app is `gradio-container`, so here's an example that changes the background color of the Gradio app:\n\n```python\nwith gr.Blocks(css=\".gradio-container {background-color: red}\") as demo:\n    ...\n```\n\nIf you'd like to reference external files in your css, preface the file path (which can be a relative or absolute path) with `\"/gradio_api/file=\"`, for example:\n\n```python\nwith gr.Blocks(css=\".gradio-container {background: url('/gradio_api/file=clouds.jpg')}\") as demo:\n    ...\n```\n\nNote: By default, most files in the host machine are not accessible to users running the Gradio app. As a result, you should make sure that any referenced files (such as `clouds.jpg` here) are either URLs or [allowed paths, as described here](/main/guides/file-access).\n\n\n", "heading1": "Adding custom CSS to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "You can `elem_id` to add an HTML element `id` to any component, and `elem_classes` to add a class or list of classes. This will allow you to select elements more easily with CSS. This approach is also more likely to be stable across Gradio versions as built-in class names or ids may change (however, as mentioned in the warning above, we cannot guarantee complete compatibility between Gradio versions if you use custom CSS as the DOM elements may themselves change).\n\n```python\ncss = \"\"\"\nwarning {background-color: FFCCCB}\n.feedback textarea {font-size: 24px !important}\n\"\"\"\n\nwith gr.Blocks(css=css) as demo:\n    box1 = gr.Textbox(value=\"Good Job\", elem_classes=\"feedback\")\n    box2 = gr.Textbox(value=\"Failure\", elem_id=\"warning\", elem_classes=\"feedback\")\n```\n\nThe CSS `warning` ruleset will only target the second Textbox, while the `.feedback` ruleset will target both. Note that when targeting classes, you might need to put the `!important` selector to override the default Gradio styles.\n\n", "heading1": "The `elem_id` and `elem_classes` Arguments", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "There are 3 ways to add javascript code to your Gradio demo:\n\n1. You can add JavaScript code as a string to the `js` parameter of the `Blocks` or `Interface` initializer. This will run the JavaScript code when the demo is first loaded.\n\nBelow is an example of adding custom js to show an animated welcome message when the demo first loads.\n\n$code_blocks_js_load\n$demo_blocks_js_load\n\n\n2. When using `Blocks` and event listeners, events have a `js` argument that can take a JavaScript function as a string and treat it just like a Python event listener function. You can pass both a JavaScript function and a Python function (in which case the JavaScript function is run first) or only Javascript (and set the Python `fn` to `None`). Take a look at the code below:\n   \n$code_blocks_js_methods\n$demo_blocks_js_methods\n\n3. Lastly, you can add JavaScript code to the `head` param of the `Blocks` initializer. This will add the code to the head of the HTML document. For example, you can add Google Analytics to your demo like so:\n\n\n```python\nhead = f\"\"\"\n<script async src=\"https://www.googletagmanager.com/gtag/js?id={google_analytics_tracking_id}\"></script>\n<script>\n  window.dataLayer = window.dataLayer || [];\n  function gtag(){{dataLayer.push(arguments);}}\n  gtag('js', new Date());\n  gtag('config', '{google_analytics_tracking_id}');\n</script>\n\"\"\"\n\nwith gr.Blocks(head=head) as demo:\n    gr.HTML(\"<h1>My App</h1>\")\n\ndemo.launch()\n```\n\nThe `head` parameter accepts any HTML tags you would normally insert into the `<head>` of a page. For example, you can also include `<meta>` tags to `head` in order to update the social sharing preview for your Gradio app like this:\n\n```py\nimport gradio as gr\n\ncustom_head = \"\"\"\n<!-- HTML Meta Tags -->\n<title>Sample App</title>\n<meta name=\"description\" content=\"An open-source web application showcasing various features and capabilities.\">\n\n<!-- Facebook Meta Tags -->\n<meta property=\"og:url\" content=\"https://example.com\">\n<meta property=\"og:type\" content=\"webs", "heading1": "Adding custom JavaScript to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "n open-source web application showcasing various features and capabilities.\">\n\n<!-- Facebook Meta Tags -->\n<meta property=\"og:url\" content=\"https://example.com\">\n<meta property=\"og:type\" content=\"website\">\n<meta property=\"og:title\" content=\"Sample App\">\n<meta property=\"og:description\" content=\"An open-source web application showcasing various features and capabilities.\">\n<meta property=\"og:image\" content=\"https://cdn.britannica.com/98/152298-050-8E45510A/Cheetah.jpg\">\n\n<!-- Twitter Meta Tags -->\n<meta name=\"twitter:card\" content=\"summary_large_image\">\n<meta name=\"twitter:creator\" content=\"@example_user\">\n<meta name=\"twitter:title\" content=\"Sample App\">\n<meta name=\"twitter:description\" content=\"An open-source web application showcasing various features and capabilities.\">\n<meta name=\"twitter:image\" content=\"https://cdn.britannica.com/98/152298-050-8E45510A/Cheetah.jpg\">\n<meta property=\"twitter:domain\" content=\"example.com\">\n<meta property=\"twitter:url\" content=\"https://example.com\">  \n\"\"\"\n\nwith gr.Blocks(title=\"My App\", head=custom_head) as demo:\n    gr.HTML(\"<h1>My App</h1>\")\n\ndemo.launch()\n```\n\n\n\nNote that injecting custom JS can affect browser behavior and accessibility (e.g. keyboard shortcuts may be lead to unexpected behavior if your Gradio app is embedded in another webpage). You should test your interface across different browsers and be mindful of how scripts may interact with browser defaults. Here's an example where pressing `Shift + s` triggers the `click` event of a specific `Button` component if the browser focus is _not_ on an input component (e.g. `Textbox` component):\n\n```python\nimport gradio as gr\n\nshortcut_js = \"\"\"\n<script>\nfunction shortcuts(e) {\n    var event = document.all ? window.event : e;\n    switch (e.target.tagName.toLowerCase()) {\n        case \"input\":\n        case \"textarea\":\n        break;\n        default:\n        if (e.key.toLowerCase() == \"s\" && e.shiftKey) {\n            document.getElementById(\"my_btn\").click();\n        }\n    }\n}\ndoc", "heading1": "Adding custom JavaScript to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "e \"input\":\n        case \"textarea\":\n        break;\n        default:\n        if (e.key.toLowerCase() == \"s\" && e.shiftKey) {\n            document.getElementById(\"my_btn\").click();\n        }\n    }\n}\ndocument.addEventListener('keypress', shortcuts, false);\n</script>\n\"\"\"\n\nwith gr.Blocks(head=shortcut_js) as demo:\n    action_button = gr.Button(value=\"Name\", elem_id=\"my_btn\")\n    textbox = gr.Textbox()\n    action_button.click(lambda : \"button pressed\", None, textbox)\n    \ndemo.launch()\n```\n\n", "heading1": "Adding custom JavaScript to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "In the example below, we will create a variable number of Textboxes. When the user edits the input Textbox, we create a Textbox for each letter in the input. Try it out below:\n\n$code_render_split_simple\n$demo_render_split_simple\n\nSee how we can now create a variable number of Textboxes using our custom logic - in this case, a simple `for` loop. The `@gr.render` decorator enables this with the following steps:\n\n1. Create a function and attach the @gr.render decorator to it.\n2. Add the input components to the `inputs=` argument of @gr.render, and create a corresponding argument in your function for each component. This function will automatically re-run on any change to a component.\n3. Add all components inside the function that you want to render based on the inputs.\n\nNow whenever the inputs change, the function re-runs, and replaces the components created from the previous function run with the latest run. Pretty straightforward! Let's add a little more complexity to this app:\n\n$code_render_split\n$demo_render_split\n\nBy default, `@gr.render` re-runs are triggered by the `.load` listener to the app and the `.change` listener to any input component provided. We can override this by explicitly setting the triggers in the decorator, as we have in this app to only trigger on `input_text.submit` instead. \nIf you are setting custom triggers, and you also want an automatic render at the start of the app, make sure to add `demo.load` to your list of triggers.\n\n", "heading1": "Dynamic Number of Components", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "If you're creating components, you probably want to attach event listeners to them as well. Let's take a look at an example that takes in a variable number of Textbox as input, and merges all the text into a single box.\n\n$code_render_merge_simple\n$demo_render_merge_simple\n\nLet's take a look at what's happening here:\n\n1. The state variable `text_count` is keeping track of the number of Textboxes to create. By clicking on the Add button, we increase `text_count` which triggers the render decorator.\n2. Note that in every single Textbox we create in the render function, we explicitly set a `key=` argument. This key allows us to preserve the value of this Component between re-renders. If you type in a value in a textbox, and then click the Add button, all the Textboxes re-render, but their values aren't cleared because the `key=` maintains the the value of a Component across a render.\n3. We've stored the Textboxes created in a list, and provide this list as input to the merge button event listener. Note that **all event listeners that use Components created inside a render function must also be defined inside that render function**. The event listener can still reference Components outside the render function, as we do here by referencing `merge_btn` and `output` which are both defined outside the render function.\n\nJust as with Components, whenever a function re-renders, the event listeners created from the previous render are cleared and the new event listeners from the latest run are attached. \n\nThis allows us to create highly customizable and complex interactions! \n\n", "heading1": "Dynamic Event Listeners", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "The `key=` argument is used to let Gradio know that the same component is being generated when your render function re-runs. This does two things:\n\n1. The same element in the browser is re-used from the previous render for this Component. This gives browser performance gains - as there's no need to destroy and rebuild a component on a render - and preserves any browser attributes that the Component may have had. If your Component is nested within layout items like `gr.Row`, make sure they are keyed as well because the keys of the parents must also match.\n2. Properties that may be changed by the user or by other event listeners are preserved. By default, only the \"value\" of Component is preserved, but you can specify any list of properties to preserve using the `preserved_by_key=` kwarg.\n\nSee the example below:\n\n$code_render_preserve_key\n$demo_render_preserve_key\n\nYou'll see in this example, when you change the `number_of_boxes` slider, there's a new re-render to update the number of box rows. If you click the \"Change Label\" buttons, they change the `label` and `info` properties of the corresponding textbox. You can also enter text in any textbox to change its value. If you change number of boxes after this, the re-renders \"reset\" the `info`, but the `label` and any entered `value` is still preserved.\n\nNote you can also key any event listener, e.g. `button.click(key=...)` if the same listener is being recreated with the same inputs and outputs across renders. This gives performance benefits, and also prevents errors from occuring if an event was triggered in a previous render, then a re-render occurs, and then the previous event finishes processing. By keying your listener, Gradio knows where to send the data properly. \n\n", "heading1": "Closer Look at `keys=` parameter", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "Let's look at two examples that use all the features above. First, try out the to-do list app below: \n\n$code_todo_list\n$demo_todo_list\n\nNote that almost the entire app is inside a single `gr.render` that reacts to the tasks `gr.State` variable. This variable is a nested list, which presents some complexity. If you design a `gr.render` to react to a list or dict structure, ensure you do the following:\n\n1. Any event listener that modifies a state variable in a manner that should trigger a re-render must set the state variable as an output. This lets Gradio know to check if the variable has changed behind the scenes. \n2. In a `gr.render`, if a variable in a loop is used inside an event listener function, that variable should be \"frozen\" via setting it to itself as a default argument in the function header. See how we have `task=task` in both `mark_done` and `delete`. This freezes the variable to its \"loop-time\" value.\n\nLet's take a look at one last example that uses everything we learned. Below is an audio mixer. Provide multiple audio tracks and mix them together.\n\n$code_audio_mixer\n$demo_audio_mixer\n\nTwo things to note in this app:\n1. Here we provide `key=` to all the components! We need to do this so that if we add another track after setting the values for an existing track, our input values to the existing track do not get reset on re-render.\n2. When there are lots of components of different types and arbitrary counts passed to an event listener, it is easier to use the set and dictionary notation for inputs rather than list notation. Above, we make one large set of all the input `gr.Audio` and `gr.Slider` components when we pass the inputs to the `merge` function. In the function body we query the component values as a dict.\n\nThe `gr.render` expands gradio capabilities extensively - see what you can make out of it! \n", "heading1": "Putting it Together", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "By default, each event listener has its own queue, which handles one request at a time. This can be configured via two arguments:\n\n- `concurrency_limit`: This sets the maximum number of concurrent executions for an event listener. By default, the limit is 1 unless configured otherwise in `Blocks.queue()`. You can also set it to `None` for no limit (i.e., an unlimited number of concurrent executions). For example:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    prompt = gr.Textbox()\n    image = gr.Image()\n    generate_btn = gr.Button(\"Generate Image\")\n    generate_btn.click(image_gen, prompt, image, concurrency_limit=5)\n```\n\nIn the code above, up to 5 requests can be processed simultaneously for this event listener. Additional requests will be queued until a slot becomes available.\n\nIf you want to manage multiple event listeners using a shared queue, you can use the `concurrency_id` argument:\n\n- `concurrency_id`: This allows event listeners to share a queue by assigning them the same ID. For example, if your setup has only 2 GPUs but multiple functions require GPU access, you can create a shared queue for all those functions. Here's how that might look:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    prompt = gr.Textbox()\n    image = gr.Image()\n    generate_btn_1 = gr.Button(\"Generate Image via model 1\")\n    generate_btn_2 = gr.Button(\"Generate Image via model 2\")\n    generate_btn_3 = gr.Button(\"Generate Image via model 3\")\n    generate_btn_1.click(image_gen_1, prompt, image, concurrency_limit=2, concurrency_id=\"gpu_queue\")\n    generate_btn_2.click(image_gen_2, prompt, image, concurrency_id=\"gpu_queue\")\n    generate_btn_3.click(image_gen_3, prompt, image, concurrency_id=\"gpu_queue\")\n```\n\nIn this example, all three event listeners share a queue identified by `\"gpu_queue\"`. The queue can handle up to 2 concurrent requests at a time, as defined by the `concurrency_limit`.\n\nNotes\n\n- To ensure unlimited concurrency for an event listener, se", "heading1": "Configuring the Queue", "source_page_url": "https://gradio.app/guides/queuing", "source_page_title": "Additional Features - Queuing Guide"}, {"text": " identified by `\"gpu_queue\"`. The queue can handle up to 2 concurrent requests at a time, as defined by the `concurrency_limit`.\n\nNotes\n\n- To ensure unlimited concurrency for an event listener, set `concurrency_limit=None`.  This is useful if your function is calling e.g. an external API which handles the rate limiting of requests itself.\n- The default concurrency limit for all queues can be set globally using the `default_concurrency_limit` parameter in `Blocks.queue()`. \n\nThese configurations make it easy to manage the queuing behavior of your Gradio app.\n", "heading1": "Configuring the Queue", "source_page_url": "https://gradio.app/guides/queuing", "source_page_title": "Additional Features - Queuing Guide"}, {"text": "**API endpoint names**\n\nWhen you create a Gradio application, the API endpoint names are automatically generated based on the function names. You can change this by using the `api_name` parameter in `gr.Interface` or `gr.ChatInterface`. If you are using Gradio `Blocks`, you can name each event listener, like this:\n\n```python\nbtn.click(add, [num1, num2], output, api_name=\"addition\")\n```\n\n**Hiding API endpoints**\n\nWhen building a complex Gradio app, you might want to hide certain API endpoints from appearing on the view API page, e.g. if they correspond to functions that simply update the UI. You can set the  `show_api` parameter to `False` in any `Blocks` event listener to achieve this, e.g. \n\n```python\nbtn.click(add, [num1, num2], output, show_api=False)\n```\n\n**Disabling API endpoints**\n\nHiding the API endpoint doesn't disable it. A user can still programmatically call the API endpoint if they know the name. If you want to disable an API endpoint altogether, set `api_name=False`, e.g. \n\n```python\nbtn.click(add, [num1, num2], output, api_name=False)\n```\n\nNote: setting an `api_name=False` also means that downstream apps will not be able to load your Gradio app using `gr.load()` as this function uses the Gradio API under the hood.\n\n**Adding API endpoints**\n\nYou can also add new API routes to your Gradio application that do not correspond to events in your UI.\n\nFor example, in this Gradio application, we add a new route that adds numbers and slices a list:\n\n```py\nimport gradio as gr\nwith gr.Blocks() as demo:\n    with gr.Row():\n        input = gr.Textbox()\n        button = gr.Button(\"Submit\")\n    output = gr.Textbox()\n    def fn(a: int, b: int, c: list[str]) -> tuple[int, str]:\n        return a + b, c[a:b]\n    gr.api(fn, api_name=\"add_and_slice\")\n\n_, url, _ = demo.launch()\n```\n\nThis will create a new route `/add_and_slice` which will show up in the \"view API\" page. It can be programmatically called by the Python or JS Clients (discussed below) like this:\n\n```py\nfrom grad", "heading1": "Configuring the API Page", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "``\n\nThis will create a new route `/add_and_slice` which will show up in the \"view API\" page. It can be programmatically called by the Python or JS Clients (discussed below) like this:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(url)\nresult = client.predict(\n        a=3,\n        b=5,\n        c=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n        api_name=\"/add_and_slice\"\n)\nprint(result)\n```\n\n", "heading1": "Configuring the API Page", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "This API page not only lists all of the endpoints that can be used to query the Gradio app, but also shows the usage of both [the Gradio Python client](https://gradio.app/guides/getting-started-with-the-python-client/), and [the Gradio JavaScript client](https://gradio.app/guides/getting-started-with-the-js-client/). \n\nFor each endpoint, Gradio automatically generates a complete code snippet with the parameters and their types, as well as example inputs, allowing you to immediately test an endpoint. Here's an example showing an image file input and `str` output:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-snippet.png)\n\n\n", "heading1": "The Clients", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "Instead of reading through the view API page, you can also use Gradio's built-in API recorder to generate the relevant code snippet. Simply click on the \"API Recorder\" button, use your Gradio app via the UI as you would normally, and then the API Recorder will generate the code using the Clients to recreate your all of your interactions programmatically.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/api-recorder.gif)\n\n", "heading1": "The API Recorder \ud83e\ude84", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "The API page also includes instructions on how to use the Gradio app as an Model Context Protocol (MCP) server, which is a standardized way to expose functions as tools so that they can be used by LLMs. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-mcp.png)\n\nFor the MCP sever, each tool, its description, and its parameters are listed, along with instructions on how to integrate with popular MCP Clients. Read more about Gradio's [MCP integration here](https://www.gradio.app/guides/building-mcp-server-with-gradio).\n\n", "heading1": "MCP Server", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "You can access the complete OpenAPI (formerly Swagger) specification of your Gradio app's API at the endpoint `<your-gradio-app-url>/gradio_api/openapi.json`. The OpenAPI specification is a standardized, language-agnostic interface description for REST APIs that enables both humans and computers to discover and understand the capabilities of your service.\n", "heading1": "OpenAPI Specification", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "Gradio can stream audio and video directly from your generator function.\nThis lets your user hear your audio or see your video nearly as soon as it's `yielded` by your function.\nAll you have to do is \n\n1. Set `streaming=True` in your `gr.Audio` or `gr.Video` output component.\n2. Write a python generator that yields the next \"chunk\" of audio or video.\n3. Set `autoplay=True` so that the media starts playing automatically.\n\nFor audio, the next \"chunk\" can be either an `.mp3` or `.wav` file or a `bytes` sequence of audio.\nFor video, the next \"chunk\" has to be either `.mp4` file or a file with `h.264` codec with a `.ts` extension.\nFor smooth playback, make sure chunks are consistent lengths and larger than 1 second.\n\nWe'll finish with some simple examples illustrating these points.\n\nStreaming Audio\n\n```python\nimport gradio as gr\nfrom time import sleep\n\ndef keep_repeating(audio_file):\n    for _ in range(10):\n        sleep(0.5)\n        yield audio_file\n\ngr.Interface(keep_repeating,\n             gr.Audio(sources=[\"microphone\"], type=\"filepath\"),\n             gr.Audio(streaming=True, autoplay=True)\n).launch()\n```\n\nStreaming Video\n\n```python\nimport gradio as gr\nfrom time import sleep\n\ndef keep_repeating(video_file):\n    for _ in range(10):\n        sleep(0.5)\n        yield video_file\n\ngr.Interface(keep_repeating,\n             gr.Video(sources=[\"webcam\"], format=\"mp4\"),\n             gr.Video(streaming=True, autoplay=True)\n).launch()\n```\n\n", "heading1": "Streaming Media", "source_page_url": "https://gradio.app/guides/streaming-outputs", "source_page_title": "Additional Features - Streaming Outputs Guide"}, {"text": "For an end-to-end example of streaming media, see the object detection from video [guide](/main/guides/object-detection-from-video) or the streaming AI-generated audio with [transformers](https://huggingface.co/docs/transformers/index) [guide](/main/guides/streaming-ai-generated-audio).", "heading1": "End-to-End Examples", "source_page_url": "https://gradio.app/guides/streaming-outputs", "source_page_title": "Additional Features - Streaming Outputs Guide"}, {"text": "Gradio demos can be easily shared publicly by setting `share=True` in the `launch()` method. Like this:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n    return \"Hello \" + name + \"!\"\n\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n\ndemo.launch(share=True)  Share your demo with just 1 extra parameter \ud83d\ude80\n```\n\nThis generates a public, shareable link that you can send to anybody! When you send this link, the user on the other side can try out the model in their browser. Because the processing happens on your device (as long as your device stays on), you don't have to worry about any packaging any dependencies.\n\n![sharing](https://github.com/gradio-app/gradio/blob/main/guides/assets/sharing.svg?raw=true)\n\n\nA share link usually looks something like this: **https://07ff8706ab.gradio.live**. Although the link is served through the Gradio Share Servers, these servers are only a proxy for your local server, and do not store any data sent through your app. Share links expire after 1 week. (it is [also possible to set up your own Share Server](https://github.com/huggingface/frp/) on your own cloud server to overcome this restriction.)\n\nTip: Keep in mind that share links are publicly accessible, meaning that anyone can use your model for prediction! Therefore, make sure not to expose any sensitive information through the functions you write, or allow any critical changes to occur on your device. Or you can [add authentication to your Gradio app](authentication) as discussed below.\n\nNote that by default, `share=False`, which means that your server is only running locally. (This is the default, except in Google Colab notebooks, where share links are automatically created). As an alternative to using share links, you can use use [SSH port-forwarding](https://www.ssh.com/ssh/tunneling/example) to share your local server with specific users.\n\n\n", "heading1": "Sharing Demos", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "If you'd like to have a permanent link to your Gradio demo on the internet, use Hugging Face Spaces. [Hugging Face Spaces](http://huggingface.co/spaces/) provides the infrastructure to permanently host your machine learning model for free!\n\nAfter you have [created a free Hugging Face account](https://huggingface.co/join), you have two methods to deploy your Gradio app to Hugging Face Spaces:\n\n1. From terminal: run `gradio deploy` in your app directory. The CLI will gather some basic metadata and then launch your app. To update your space, you can re-run this command or enable the Github Actions option to automatically update the Spaces on `git push`.\n\n2. From your browser: Drag and drop a folder containing your Gradio model and all related files [here](https://huggingface.co/new-space). See [this guide how to host on Hugging Face Spaces](https://huggingface.co/blog/gradio-spaces) for more information, or watch the embedded video:\n\n<video autoplay muted loop>\n  <source src=\"https://github.com/gradio-app/gradio/blob/main/guides/assets/hf_demo.mp4?raw=true\" type=\"video/mp4\" />\n</video>\n\n", "heading1": "Hosting on HF Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "You can add a button to your Gradio app that creates a unique URL you can use to share your app and all components **as they currently are** with others. This is useful for sharing unique and interesting generations from your application , or for saving a snapshot of your app at a particular point in time.\n\nTo add a deep link button to your app, place the `gr.DeepLinkButton` component anywhere in your app.\nFor the URL to be accessible to others, your app must be available at a public URL. So be sure to host your app like Hugging Face Spaces or use the `share=True` parameter when launching your app.\n\nLet's see an example of how this works. Here's a simple Gradio chat ap that uses the `gr.DeepLinkButton` component. After a couple of messages, click the deep link button and paste it into a new browser tab to see the app as it is at that point in time.\n\n$code_deep_link\n$demo_deep_link\n\n\n", "heading1": "Sharing Deep Links", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "Once you have hosted your app on Hugging Face Spaces (or on your own server), you may want to embed the demo on a different website, such as your blog or your portfolio. Embedding an interactive demo allows people to try out the machine learning model that you have built, without needing to download or install anything \u2014 right in their browser! The best part is that you can embed interactive demos even in static websites, such as GitHub pages.\n\nThere are two ways to embed your Gradio demos. You can find quick links to both options directly on the Hugging Face Space page, in the \"Embed this Space\" dropdown option:\n\n![Embed this Space dropdown option](https://github.com/gradio-app/gradio/blob/main/guides/assets/embed_this_space.png?raw=true)\n\nEmbedding with Web Components\n\nWeb components typically offer a better experience to users than IFrames. Web components load lazily, meaning that they won't slow down the loading time of your website, and they automatically adjust their height based on the size of the Gradio app.\n\nTo embed with Web Components:\n\n1. Import the gradio JS library into into your site by adding the script below in your site (replace {GRADIO_VERSION} in the URL with the library version of Gradio you are using).\n\n```html\n<script\n\ttype=\"module\"\n\tsrc=\"https://gradio.s3-us-west-2.amazonaws.com/{GRADIO_VERSION}/gradio.js\"\n></script>\n```\n\n2. Add\n\n```html\n<gradio-app src=\"https://$your_space_host.hf.space\"></gradio-app>\n```\n\nelement where you want to place the app. Set the `src=` attribute to your Space's embed URL, which you can find in the \"Embed this Space\" button. For example:\n\n```html\n<gradio-app\n\tsrc=\"https://abidlabs-pytorch-image-classifier.hf.space\"\n></gradio-app>\n```\n\n<script>\nfetch(\"https://pypi.org/pypi/gradio/json\"\n).then(r => r.json()\n).then(obj => {\n    let v = obj.info.version;\n    content = document.querySelector('.prose');\n    content.innerHTML = content.innerHTML.replaceAll(\"{GRADIO_VERSION}\", v);\n});\n</script>\n\nYou can see examples of h", "heading1": "Embedding Hosted Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "=> {\n    let v = obj.info.version;\n    content = document.querySelector('.prose');\n    content.innerHTML = content.innerHTML.replaceAll(\"{GRADIO_VERSION}\", v);\n});\n</script>\n\nYou can see examples of how web components look <a href=\"https://www.gradio.app\">on the Gradio landing page</a>.\n\nYou can also customize the appearance and behavior of your web component with attributes that you pass into the `<gradio-app>` tag:\n\n- `src`: as we've seen, the `src` attributes links to the URL of the hosted Gradio demo that you would like to embed\n- `space`: an optional shorthand if your Gradio demo is hosted on Hugging Face Space. Accepts a `username/space_name` instead of a full URL. Example: `gradio/Echocardiogram-Segmentation`. If this attribute attribute is provided, then `src` does not need to be provided.\n- `control_page_title`: a boolean designating whether the html title of the page should be set to the title of the Gradio app (by default `\"false\"`)\n- `initial_height`: the initial height of the web component while it is loading the Gradio app, (by default `\"300px\"`). Note that the final height is set based on the size of the Gradio app.\n- `container`: whether to show the border frame and information about where the Space is hosted (by default `\"true\"`)\n- `info`: whether to show just the information about where the Space is hosted underneath the embedded app (by default `\"true\"`)\n- `autoscroll`: whether to autoscroll to the output when prediction has finished (by default `\"false\"`)\n- `eager`: whether to load the Gradio app as soon as the page loads (by default `\"false\"`)\n- `theme_mode`: whether to use the `dark`, `light`, or default `system` theme mode (by default `\"system\"`)\n- `render`: an event that is triggered once the embedded space has finished rendering.\n\nHere's an example of how to use these attributes to create a Gradio app that does not lazy load and has an initial height of 0px.\n\n```html\n<gradio-app\n\tspace=\"gradio/Echocardiogram-Segmentation\"\n\teager=\"true\"\n\tinit", "heading1": "Embedding Hosted Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "ple of how to use these attributes to create a Gradio app that does not lazy load and has an initial height of 0px.\n\n```html\n<gradio-app\n\tspace=\"gradio/Echocardiogram-Segmentation\"\n\teager=\"true\"\n\tinitial_height=\"0px\"\n></gradio-app>\n```\n\nHere's another example of how to use the `render` event. An event listener is used to capture the `render` event and will call the `handleLoadComplete()` function once rendering is complete.\n\n```html\n<script>\n\tfunction handleLoadComplete() {\n\t\tconsole.log(\"Embedded space has finished rendering\");\n\t}\n\n\tconst gradioApp = document.querySelector(\"gradio-app\");\n\tgradioApp.addEventListener(\"render\", handleLoadComplete);\n</script>\n```\n\n_Note: While Gradio's CSS will never impact the embedding page, the embedding page can affect the style of the embedded Gradio app. Make sure that any CSS in the parent page isn't so general that it could also apply to the embedded Gradio app and cause the styling to break. Element selectors such as `header { ... }` and `footer { ... }` will be the most likely to cause issues._\n\nEmbedding with IFrames\n\nTo embed with IFrames instead (if you cannot add javascript to your website, for example), add this element:\n\n```html\n<iframe src=\"https://$your_space_host.hf.space\"></iframe>\n```\n\nAgain, you can find the `src=` attribute to your Space's embed URL, which you can find in the \"Embed this Space\" button.\n\nNote: if you use IFrames, you'll probably want to add a fixed `height` attribute and set `style=\"border:0;\"` to remove the border. In addition, if your app requires permissions such as access to the webcam or the microphone, you'll need to provide that as well using the `allow` attribute.\n\n", "heading1": "Embedding Hosted Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "You can use almost any Gradio app as an API! In the footer of a Gradio app [like this one](https://huggingface.co/spaces/gradio/hello_world), you'll see a \"Use via API\" link.\n\n![Use via API](https://github.com/gradio-app/gradio/blob/main/guides/assets/use_via_api.png?raw=true)\n\nThis is a page that lists the endpoints that can be used to query the Gradio app, via our supported clients: either [the Python client](https://gradio.app/guides/getting-started-with-the-python-client/), or [the JavaScript client](https://gradio.app/guides/getting-started-with-the-js-client/). For each endpoint, Gradio automatically generates the parameters and their types, as well as example inputs, like this.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe endpoints are automatically created when you launch a Gradio application. If you are using Gradio `Blocks`, you can also name each event listener, such as\n\n```python\nbtn.click(add, [num1, num2], output, api_name=\"addition\")\n```\n\nThis will add and document the endpoint `/addition/` to the automatically generated API page. Read more about the [API page here](./view-api-page).\n\n", "heading1": "API Page", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "When a user makes a prediction to your app, you may need the underlying network request, in order to get the request headers (e.g. for advanced authentication), log the client's IP address, getting the query parameters, or for other reasons. Gradio supports this in a similar manner to FastAPI: simply add a function parameter whose type hint is `gr.Request` and Gradio will pass in the network request as that parameter. Here is an example:\n\n```python\nimport gradio as gr\n\ndef echo(text, request: gr.Request):\n    if request:\n        print(\"Request headers dictionary:\", request.headers)\n        print(\"IP address:\", request.client.host)\n        print(\"Query parameters:\", dict(request.query_params))\n    return text\n\nio = gr.Interface(echo, \"textbox\", \"textbox\").launch()\n```\n\nNote: if your function is called directly instead of through the UI (this happens, for\nexample, when examples are cached, or when the Gradio app is called via API), then `request` will be `None`.\nYou should handle this case explicitly to ensure that your app does not throw any errors. That is why\nwe have the explicit check `if request`.\n\n", "heading1": "Accessing the Network Request Directly", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "In some cases, you might have an existing FastAPI app, and you'd like to add a path for a Gradio demo.\nYou can easily do this with `gradio.mount_gradio_app()`.\n\nHere's a complete example:\n\n$code_custom_path\n\nNote that this approach also allows you run your Gradio apps on custom paths (`http://localhost:8000/gradio` in the example above).\n\n\n", "heading1": "Mounting Within Another FastAPI App", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "Password-protected app\n\nYou may wish to put an authentication page in front of your app to limit who can open your app. With the `auth=` keyword argument in the `launch()` method, you can provide a tuple with a username and password, or a list of acceptable username/password tuples; Here's an example that provides password-based authentication for a single user named \"admin\":\n\n```python\ndemo.launch(auth=(\"admin\", \"pass1234\"))\n```\n\nFor more complex authentication handling, you can even pass a function that takes a username and password as arguments, and returns `True` to allow access, `False` otherwise.\n\nHere's an example of a function that accepts any login where the username and password are the same:\n\n```python\ndef same_auth(username, password):\n    return username == password\ndemo.launch(auth=same_auth)\n```\n\nIf you have multiple users, you may wish to customize the content that is shown depending on the user that is logged in. You can retrieve the logged in user by [accessing the network request directly](accessing-the-network-request-directly) as discussed above, and then reading the `.username` attribute of the request. Here's an example:\n\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n    return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as demo:\n    m = gr.Markdown()\n    demo.load(update_message, None, m)\n\ndemo.launch(auth=[(\"Abubakar\", \"Abubakar\"), (\"Ali\", \"Ali\")])\n```\n\nNote: For authentication to work properly, third party cookies must be enabled in your browser. This is not the case by default for Safari or for Chrome Incognito Mode.\n\nIf users visit the `/logout` page of your Gradio app, they will automatically be logged out and session cookies deleted. This allows you to add logout functionality to your Gradio app as well. Let's update the previous example to include a log out button:\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n    return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as ", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": " Let's update the previous example to include a log out button:\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n    return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as demo:\n    m = gr.Markdown()\n    logout_button = gr.Button(\"Logout\", link=\"/logout\")\n    demo.load(update_message, None, m)\n\ndemo.launch(auth=[(\"Pete\", \"Pete\"), (\"Dawood\", \"Dawood\")])\n```\n\nNote: Gradio's built-in authentication provides a straightforward and basic layer of access control but does not offer robust security features for applications that require stringent access controls (e.g.  multi-factor authentication, rate limiting, or automatic lockout policies).\n\nOAuth (Login via Hugging Face)\n\nGradio natively supports OAuth login via Hugging Face. In other words, you can easily add a _\"Sign in with Hugging Face\"_ button to your demo, which allows you to get a user's HF username as well as other information from their HF profile. Check out [this Space](https://huggingface.co/spaces/Wauplin/gradio-oauth-demo) for a live demo.\n\nTo enable OAuth, you must set `hf_oauth: true` as a Space metadata in your README.md file. This will register your Space\nas an OAuth application on Hugging Face. Next, you can use `gr.LoginButton` to add a login button to\nyour Gradio app. Once a user is logged in with their HF account, you can retrieve their profile by adding a parameter of type\n`gr.OAuthProfile` to any Gradio function. The user profile will be automatically injected as a parameter value. If you want\nto perform actions on behalf of the user (e.g. list user's private repos, create repo, etc.), you can retrieve the user\ntoken by adding a parameter of type `gr.OAuthToken`. You must define which scopes you will use in your Space metadata\n(see [documentation](https://huggingface.co/docs/hub/spaces-oauthscopes) for more details).\n\nHere is a short example:\n\n$code_login_with_huggingface\n\nWhen the user clicks on the login button, they get redirected in a new page to authorize your ", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "docs/hub/spaces-oauthscopes) for more details).\n\nHere is a short example:\n\n$code_login_with_huggingface\n\nWhen the user clicks on the login button, they get redirected in a new page to authorize your Space.\n\n<center>\n<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/oauth_sign_in.png\" style=\"width:300px; max-width:80%\">\n</center>\n\nUsers can revoke access to their profile at any time in their [settings](https://huggingface.co/settings/connected-applications).\n\nAs seen above, OAuth features are available only when your app runs in a Space. However, you often need to test your app\nlocally before deploying it. To test OAuth features locally, your machine must be logged in to Hugging Face. Please run `huggingface-cli login` or set `HF_TOKEN` as environment variable with one of your access token. You can generate a new token in your settings page (https://huggingface.co/settings/tokens). Then, clicking on the `gr.LoginButton` will log in to your local Hugging Face profile, allowing you to debug your app with your Hugging Face account before deploying it to a Space.\n\n**Security Note**: It is important to note that adding a `gr.LoginButton` does not restrict users from using your app, in the same way that adding [username-password authentication](/guides/sharing-your-apppassword-protected-app) does. This means that users of your app who have not logged in with Hugging Face can still access and run events in your Gradio app -- the difference is that the `gr.OAuthProfile` or `gr.OAuthToken` will be `None` in the corresponding functions.\n\n\nOAuth (with external providers)\n\nIt is also possible to authenticate with external OAuth providers (e.g. Google OAuth) in your Gradio apps. To do this, first mount your Gradio app within a FastAPI app ([as discussed above](mounting-within-another-fast-api-app)). Then, you must write an *authentication function*, which gets the user's username from the OAuth provider and returns it. Th", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": " FastAPI app ([as discussed above](mounting-within-another-fast-api-app)). Then, you must write an *authentication function*, which gets the user's username from the OAuth provider and returns it. This function should be passed to the `auth_dependency` parameter in `gr.mount_gradio_app`.\n\nSimilar to [FastAPI dependency functions](https://fastapi.tiangolo.com/tutorial/dependencies/), the function specified by `auth_dependency` will run before any Gradio-related route in your FastAPI app. The function should accept a single parameter: the FastAPI `Request` and return either a string (representing a user's username) or `None`. If a string is returned, the user will be able to access the Gradio-related routes in your FastAPI app.\n\nFirst, let's show a simplistic example to illustrate the `auth_dependency` parameter:\n\n```python\nfrom fastapi import FastAPI, Request\nimport gradio as gr\n\napp = FastAPI()\n\ndef get_user(request: Request):\n    return request.headers.get(\"user\")\n\ndemo = gr.Interface(lambda s: f\"Hello {s}!\", \"textbox\", \"textbox\")\n\napp = gr.mount_gradio_app(app, demo, path=\"/demo\", auth_dependency=get_user)\n\nif __name__ == '__main__':\n    uvicorn.run(app)\n```\n\nIn this example, only requests that include a \"user\" header will be allowed to access the Gradio app. Of course, this does not add much security, since any user can add this header in their request.\n\nHere's a more complete example showing how to add Google OAuth to a Gradio app (assuming you've already created OAuth Credentials on the [Google Developer Console](https://console.cloud.google.com/project)):\n\n```python\nimport os\nfrom authlib.integrations.starlette_client import OAuth, OAuthError\nfrom fastapi import FastAPI, Depends, Request\nfrom starlette.config import Config\nfrom starlette.responses import RedirectResponse\nfrom starlette.middleware.sessions import SessionMiddleware\nimport uvicorn\nimport gradio as gr\n\napp = FastAPI()\n\nReplace these with your own OAuth settings\nGOOGLE_CLIENT_ID = \"...\"\nGOOGLE_C", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "Response\nfrom starlette.middleware.sessions import SessionMiddleware\nimport uvicorn\nimport gradio as gr\n\napp = FastAPI()\n\nReplace these with your own OAuth settings\nGOOGLE_CLIENT_ID = \"...\"\nGOOGLE_CLIENT_SECRET = \"...\"\nSECRET_KEY = \"...\"\n\nconfig_data = {'GOOGLE_CLIENT_ID': GOOGLE_CLIENT_ID, 'GOOGLE_CLIENT_SECRET': GOOGLE_CLIENT_SECRET}\nstarlette_config = Config(environ=config_data)\noauth = OAuth(starlette_config)\noauth.register(\n    name='google',\n    server_metadata_url='https://accounts.google.com/.well-known/openid-configuration',\n    client_kwargs={'scope': 'openid email profile'},\n)\n\nSECRET_KEY = os.environ.get('SECRET_KEY') or \"a_very_secret_key\"\napp.add_middleware(SessionMiddleware, secret_key=SECRET_KEY)\n\nDependency to get the current user\ndef get_user(request: Request):\n    user = request.session.get('user')\n    if user:\n        return user['name']\n    return None\n\n@app.get('/')\ndef public(user: dict = Depends(get_user)):\n    if user:\n        return RedirectResponse(url='/gradio')\n    else:\n        return RedirectResponse(url='/login-demo')\n\n@app.route('/logout')\nasync def logout(request: Request):\n    request.session.pop('user', None)\n    return RedirectResponse(url='/')\n\n@app.route('/login')\nasync def login(request: Request):\n    redirect_uri = request.url_for('auth')\n    If your app is running on https, you should ensure that the\n    `redirect_uri` is https, e.g. uncomment the following lines:\n    \n    from urllib.parse import urlparse, urlunparse\n    redirect_uri = urlunparse(urlparse(str(redirect_uri))._replace(scheme='https'))\n    return await oauth.google.authorize_redirect(request, redirect_uri)\n\n@app.route('/auth')\nasync def auth(request: Request):\n    try:\n        access_token = await oauth.google.authorize_access_token(request)\n    except OAuthError:\n        return RedirectResponse(url='/')\n    request.session['user'] = dict(access_token)[\"userinfo\"]\n    return RedirectResponse(url='/')\n\nwith gr.Blocks() as login_demo:\n    gr.Button(", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "t OAuthError:\n        return RedirectResponse(url='/')\n    request.session['user'] = dict(access_token)[\"userinfo\"]\n    return RedirectResponse(url='/')\n\nwith gr.Blocks() as login_demo:\n    gr.Button(\"Login\", link=\"/login\")\n\napp = gr.mount_gradio_app(app, login_demo, path=\"/login-demo\")\n\ndef greet(request: gr.Request):\n    return f\"Welcome to Gradio, {request.username}\"\n\nwith gr.Blocks() as main_demo:\n    m = gr.Markdown(\"Welcome to Gradio!\")\n    gr.Button(\"Logout\", link=\"/logout\")\n    main_demo.load(greet, None, m)\n\napp = gr.mount_gradio_app(app, main_demo, path=\"/gradio\", auth_dependency=get_user)\n\nif __name__ == '__main__':\n    uvicorn.run(app)\n```\n\nThere are actually two separate Gradio apps in this example! One that simply displays a log in button (this demo is accessible to any user), while the other main demo is only accessible to users that are logged in. You can try this example out on [this Space](https://huggingface.co/spaces/gradio/oauth-example).\n\n\n", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "By default, Gradio collects certain analytics to help us better understand the usage of the `gradio` library. This includes the following information:\n\n* What environment the Gradio app is running on (e.g. Colab Notebook, Hugging Face Spaces)\n* What input/output components are being used in the Gradio app\n* Whether the Gradio app is utilizing certain advanced features, such as `auth` or `show_error`\n* The IP address which is used solely to measure the number of unique developers using Gradio\n* The version of Gradio that is running\n\nNo information is collected from _users_ of your Gradio app. If you'd like to disable analytics altogether, you can do so by setting the `analytics_enabled` parameter to `False` in `gr.Blocks`, `gr.Interface`, or `gr.ChatInterface`. Or, you can set the GRADIO_ANALYTICS_ENABLED environment variable to `\"False\"` to apply this to all Gradio apps created across your system.\n\n*Note*: this reflects the analytics policy as of `gradio>=4.32.0`.\n\n", "heading1": "Analytics", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "[Progressive Web Apps (PWAs)](https://developer.mozilla.org/en-US/docs/Web/Progressive_web_apps) are web applications that are regular web pages or websites, but can appear to the user like installable platform-specific applications.\n\nGradio apps can be easily served as PWAs by setting the `pwa=True` parameter in the `launch()` method. Here's an example:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n    return \"Hello \" + name + \"!\"\n\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n\ndemo.launch(pwa=True)  Launch your app as a PWA\n```\n\nThis will generate a PWA that can be installed on your device. Here's how it looks:\n\n![Installing PWA](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/install-pwa.gif)\n\nWhen you specify `favicon_path` in the `launch()` method, the icon will be used as the app's icon. Here's an example:\n\n```python\ndemo.launch(pwa=True, favicon_path=\"./hf-logo.svg\")  Use a custom icon for your PWA\n```\n\n![Custom PWA Icon](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/pwa-favicon.png)\n", "heading1": "Progressive Web App (PWA)", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "- **1. Static files**. You can designate static files or directories using the `gr.set_static_paths` function. Static files  are not be copied to the Gradio cache (see below) and will be served directly from your computer. This can help save disk space and reduce the time your app takes to launch but be mindful of possible security implications as any static files are accessible to all useres of your Gradio app.\n\n- **2. Files in the `allowed_paths` parameter in `launch()`**. This parameter allows you to pass in a list of additional directories or exact filepaths you'd like to allow users to have access to. (By default, this parameter is an empty list).\n\n- **3. Files in Gradio's cache**. After you launch your Gradio app, Gradio copies certain files into a temporary cache and makes these files accessible to users. Let's unpack this in more detail below.\n\n\n", "heading1": "Files Gradio allows users to access", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "First, it's important to understand why Gradio has a cache at all. Gradio copies files to a cache directory before returning them to the frontend. This prevents files from being overwritten by one user while they are still needed by another user of your application. For example, if your prediction function returns a video file, then Gradio will move that video to the cache after your prediction function runs and returns a URL the frontend can use to show the video. Any file in the cache is available via URL to all users of your running application.\n\nTip: You can customize the location of the cache by setting the `GRADIO_TEMP_DIR` environment variable to an absolute path, such as `/home/usr/scripts/project/temp/`. \n\nFiles Gradio moves to the cache\n\nGradio moves three kinds of files into the cache\n\n1. Files specified by the developer before runtime, e.g. cached examples, default values of components, or files passed into parameters such as the `avatar_images` of `gr.Chatbot`\n\n2. File paths returned by a prediction function in your Gradio application, if they ALSO meet one of the conditions below:\n\n* It is in the `allowed_paths` parameter of the `Blocks.launch` method.\n* It is in the current working directory of the python interpreter.\n* It is in the temp directory obtained by `tempfile.gettempdir()`.\n\n**Note:** files in the current working directory whose name starts with a period (`.`) will not be moved to the cache, even if they are returned from a prediction function, since they often contain sensitive information. \n\nIf none of these criteria are met, the prediction function that is returning that file will raise an exception instead of moving the file to cache. Gradio performs this check so that arbitrary files on your machine cannot be accessed.\n\n3. Files uploaded by a user to your Gradio app (e.g. through the `File` or `Image` input components).\n\nTip: If at any time Gradio blocks a file that you would like it to process, add its path to the `allowed_paths` p", "heading1": "The Gradio cache", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "d by a user to your Gradio app (e.g. through the `File` or `Image` input components).\n\nTip: If at any time Gradio blocks a file that you would like it to process, add its path to the `allowed_paths` parameter.\n\n", "heading1": "The Gradio cache", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "While running, Gradio apps will NOT ALLOW users to access:\n\n- **Files that you explicitly block via the `blocked_paths` parameter in `launch()`**. You can pass in a list of additional directories or exact filepaths to the `blocked_paths` parameter in `launch()`. This parameter takes precedence over the files that Gradio exposes by default, or by the `allowed_paths` parameter or the `gr.set_static_paths` function.\n\n- **Any other paths on the host machine**. Users should NOT be able to access other arbitrary paths on the host.\n\n", "heading1": "The files Gradio will not allow others to access", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "Sharing your Gradio application will also allow users to upload files to your computer or server. You can set a maximum file size for uploads to prevent abuse and to preserve disk space. You can do this with the `max_file_size` parameter of `.launch`. For example, the following two code snippets limit file uploads to 5 megabytes per file.\n\n```python\nimport gradio as gr\n\ndemo = gr.Interface(lambda x: x, \"image\", \"image\")\n\ndemo.launch(max_file_size=\"5mb\")\nor\ndemo.launch(max_file_size=5 * gr.FileSize.MB)\n```\n\n", "heading1": "Uploading Files", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "* Set a `max_file_size` for your application.\n* Do not return arbitrary user input from a function that is connected to a file-based output component (`gr.Image`, `gr.File`, etc.). For example, the following interface would allow anyone to move an arbitrary file in your local directory to the cache: `gr.Interface(lambda s: s, \"text\", \"file\")`. This is because the user input is treated as an arbitrary file path. \n* Make `allowed_paths` as small as possible. If a path in `allowed_paths` is a directory, any file within that directory can be accessed. Make sure the entires of `allowed_paths` only contains files related to your application.\n* Run your gradio application from the same directory the application file is located in. This will narrow the scope of files Gradio will be allowed to move into the cache. For example, prefer `python app.py` to `python Users/sources/project/app.py`.\n\n\n", "heading1": "Best Practices", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "Both `gr.set_static_paths` and the `allowed_paths` parameter in launch expect absolute paths. Below is a minimal example to display a local `.png` image file in an HTML block.\n\n```txt\n\u251c\u2500\u2500 assets\n\u2502   \u2514\u2500\u2500 logo.png\n\u2514\u2500\u2500 app.py\n```\nFor the example directory structure, `logo.png` and any other files in the `assets` folder can be accessed from your Gradio app in `app.py` as follows:\n\n```python\nfrom pathlib import Path\n\nimport gradio as gr\n\ngr.set_static_paths(paths=[Path.cwd().absolute()/\"assets\"])\n\nwith gr.Blocks() as demo:\n    gr.HTML(\"<img src='/gradio_api/file=assets/logo.png'>\")\n\ndemo.launch()\n```\n", "heading1": "Example: Accessing local files", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "When a user closes their browser tab, Gradio will automatically delete any `gr.State` variables associated with that user session after 60 minutes. If the user connects again within those 60 minutes, no state will be deleted.\n\nYou can control the deletion behavior further with the following two parameters of `gr.State`:\n\n1. `delete_callback` - An arbitrary function that will be called when the variable is deleted. This function must take the state value as input. This function is useful for deleting variables from GPU memory.\n2. `time_to_live` - The number of seconds the state should be stored for after it is created or updated. This will delete variables before the session is closed, so it's useful for clearing state for potentially long running sessions.\n\n", "heading1": "Automatic deletion of `gr.State`", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "Your Gradio application will save uploaded and generated files to a special directory called the cache directory. Gradio uses a hashing scheme to ensure that duplicate files are not saved to the cache but over time the size of the cache will grow (especially if your app goes viral \ud83d\ude09).\n\nGradio can periodically clean up the cache for you if you specify the `delete_cache` parameter of `gr.Blocks()`, `gr.Interface()`, or `gr.ChatInterface()`. \nThis parameter is a tuple of the form `[frequency, age]` both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance will be deleted if more than `age` seconds have passed since the file was created. \nFor example, setting this to (86400, 86400) will delete temporary files every day if they are older than a day old.\nAdditionally, the cache will be deleted entirely when the server restarts.\n\n", "heading1": "Automatic cache cleanup via `delete_cache`", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "Additionally, Gradio now includes a `Blocks.unload()` event, allowing you to run arbitrary cleanup functions when users disconnect (this does not have a 60 minute delay).\nUnlike other gradio events, this event does not accept inputs or outptus.\nYou can think of the `unload` event as the opposite of the `load` event.\n\n", "heading1": "The `unload` event", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "The following demo uses all of these features. When a user visits the page, a special unique directory is created for that user.\nAs the user interacts with the app, images are saved to disk in that special directory.\nWhen the user closes the page, the images created in that session are deleted via the `unload` event.\nThe state and files in the cache are cleaned up automatically as well.\n\n$code_state_cleanup\n$demo_state_cleanup", "heading1": "Putting it all together", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "Let's create a demo where a user can choose a filter to apply to their webcam stream. Users can choose from an edge-detection filter, a cartoon filter, or simply flipping the stream vertically.\n\n$code_streaming_filter\n$demo_streaming_filter\n\nYou will notice that if you change the filter value it will immediately take effect in the output stream. That is an important difference of stream events in comparison to other Gradio events. The input values of the stream can be changed while the stream is being processed. \n\nTip: We set the \"streaming\" parameter of the image output component to be \"True\". Doing so lets the server automatically convert our output images into base64 format, a format that is efficient for streaming.\n\n", "heading1": "A Realistic Image Demo", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "For some image streaming demos, like the one above, we don't need to display separate input and output components. Our app would look cleaner if we could just display the modified output stream.\n\nWe can do so by just specifying the input image component as the output of the stream event.\n\n$code_streaming_filter_unified\n$demo_streaming_filter_unified\n\n", "heading1": "Unified Image Demos", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "Your streaming function should be stateless. It should take the current input and return its corresponding output. However, there are cases where you may want to keep track of past inputs or outputs. For example, you may want to keep a buffer of the previous `k` inputs to improve the accuracy of your transcription demo. You can do this with Gradio's `gr.State()` component.\n\nLet's showcase this with a sample demo:\n\n```python\ndef transcribe_handler(current_audio, state, transcript):\n    next_text = transcribe(current_audio, history=state)\n    state.append(current_audio)\n    state = state[-3:]\n    return state, transcript + next_text\n\nwith gr.Blocks() as demo:\n    with gr.Row():\n        with gr.Column():\n            mic = gr.Audio(sources=\"microphone\")\n            state = gr.State(value=[])\n        with gr.Column():\n            transcript = gr.Textbox(label=\"Transcript\")\n    mic.stream(transcribe_handler, [mic, state, transcript], [state, transcript],\n               time_limit=10, stream_every=1)\n\n\ndemo.launch()\n```\n\n", "heading1": "Keeping track of past inputs or outputs", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "For an end-to-end example of streaming from the webcam, see the object detection from webcam [guide](/main/guides/object-detection-from-webcam-with-webrtc).", "heading1": "End-to-End Examples", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "1. `GRADIO_SERVER_PORT`\n\n- **Description**: Specifies the port on which the Gradio app will run.\n- **Default**: `7860`\n- **Example**:\n  ```bash\n  export GRADIO_SERVER_PORT=8000\n  ```\n\n2. `GRADIO_SERVER_NAME`\n\n- **Description**: Defines the host name for the Gradio server. To make Gradio accessible from any IP address, set this to `\"0.0.0.0\"`\n- **Default**: `\"127.0.0.1\"` \n- **Example**:\n  ```bash\n  export GRADIO_SERVER_NAME=\"0.0.0.0\"\n  ```\n\n3. `GRADIO_NUM_PORTS`\n\n- **Description**: Defines the number of ports to try when starting the Gradio server.\n- **Default**: `100`\n- **Example**:\n  ```bash\n  export GRADIO_NUM_PORTS=200\n  ```\n\n4. `GRADIO_ANALYTICS_ENABLED`\n\n- **Description**: Whether Gradio should provide \n- **Default**: `\"True\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_ANALYTICS_ENABLED=\"True\"\n  ```\n\n5. `GRADIO_DEBUG`\n\n- **Description**: Enables or disables debug mode in Gradio. If debug mode is enabled, the main thread does not terminate allowing error messages to be printed in environments such as Google Colab.\n- **Default**: `0`\n- **Example**:\n  ```sh\n  export GRADIO_DEBUG=1\n  ```\n\n6. `GRADIO_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag inputs/outputs in the Gradio interface. See [the Guide on flagging](/guides/using-flagging) for more details.\n- **Default**: `\"manual\"`\n- **Options**: `\"never\"`, `\"manual\"`, `\"auto\"`\n- **Example**:\n  ```sh\n  export GRADIO_FLAGGING_MODE=\"never\"\n  ```\n\n7. `GRADIO_TEMP_DIR`\n\n- **Description**: Specifies the directory where temporary files created by Gradio are stored.\n- **Default**: System default temporary directory\n- **Example**:\n  ```sh\n  export GRADIO_TEMP_DIR=\"/path/to/temp\"\n  ```\n\n8. `GRADIO_ROOT_PATH`\n\n- **Description**: Sets the root path for the Gradio application. Useful if running Gradio [behind a reverse proxy](/guides/running-gradio-on-your-web-server-with-nginx).\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_ROOT_PATH=", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "r the Gradio application. Useful if running Gradio [behind a reverse proxy](/guides/running-gradio-on-your-web-server-with-nginx).\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_ROOT_PATH=\"/myapp\"\n  ```\n\n9. `GRADIO_SHARE`\n\n- **Description**: Enables or disables sharing the Gradio app.\n- **Default**: `\"False\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_SHARE=\"True\"\n  ```\n\n10. `GRADIO_ALLOWED_PATHS`\n\n- **Description**: Sets a list of complete filepaths or parent directories that gradio is allowed to serve. Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app. Multiple items can be specified by separating items with commas.\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_ALLOWED_PATHS=\"/mnt/sda1,/mnt/sda2\"\n  ```\n\n11. `GRADIO_BLOCKED_PATHS`\n\n- **Description**: Sets a list of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default. Multiple items can be specified by separating items with commas.\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_BLOCKED_PATHS=\"/users/x/gradio_app/admin,/users/x/gradio_app/keys\"\n  ```\n\n12. `FORWARDED_ALLOW_IPS`\n\n- **Description**: This is not a Gradio-specific environment variable, but rather one used in server configurations, specifically `uvicorn` which is used by Gradio internally. This environment variable is useful when deploying applications behind a reverse proxy. It defines a list of IP addresses that are trusted to forward traffic to your application. When set, the application will trust the `X-Forwarded-For` header from these IP addresses to determine the original IP address of the user making the request. This means that if you use the `gr.Request` [objec", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": " the application will trust the `X-Forwarded-For` header from these IP addresses to determine the original IP address of the user making the request. This means that if you use the `gr.Request` [object's](https://www.gradio.app/docs/gradio/request) `client.host` property, it will correctly get the user's IP address instead of the IP address of the reverse proxy server. Note that only trusted IP addresses (i.e. the IP addresses of your reverse proxy servers) should be added, as any server with these IP addresses can modify the `X-Forwarded-For` header and spoof the client's IP address.\n- **Default**: `\"127.0.0.1\"`\n- **Example**:\n  ```sh\n  export FORWARDED_ALLOW_IPS=\"127.0.0.1,192.168.1.100\"\n  ```\n\n13. `GRADIO_CACHE_EXAMPLES`\n\n- **Description**: Whether or not to cache examples by default in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()` when no explicit argument is passed for the `cache_examples` parameter. You can set this environment variable to either the string \"true\" or \"false\".\n- **Default**: `\"false\"`\n- **Example**:\n  ```sh\n  export GRADIO_CACHE_EXAMPLES=\"true\"\n  ```\n\n\n14. `GRADIO_CACHE_MODE`\n\n- **Description**: How to cache examples. Only applies if `cache_examples` is set to `True` either via enviornment variable or by an explicit parameter, AND no no explicit argument is passed for the `cache_mode` parameter in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()`. Can be set to either the strings \"lazy\" or \"eager.\" If \"lazy\", examples are cached after their first use for all users of the app. If \"eager\", all examples are cached at app launch.\n\n- **Default**: `\"eager\"`\n- **Example**:\n  ```sh\n  export GRADIO_CACHE_MODE=\"lazy\"\n  ```\n\n\n15. `GRADIO_EXAMPLES_CACHE`\n\n- **Description**:  If you set `cache_examples=True` in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()`, Gradio will run your prediction function and save the results to disk. By default, this is in the `.gradio/cached_examples//` subdirectory within your", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "e()`, `gr.ChatInterface()` or in `gr.Examples()`, Gradio will run your prediction function and save the results to disk. By default, this is in the `.gradio/cached_examples//` subdirectory within your app's working directory. You can customize the location of cached example files created by Gradio by setting the environment variable `GRADIO_EXAMPLES_CACHE` to an absolute path or a path relative to your working directory.\n- **Default**: `\".gradio/cached_examples/\"`\n- **Example**:\n  ```sh\n  export GRADIO_EXAMPLES_CACHE=\"custom_cached_examples/\"\n  ```\n\n\n16. `GRADIO_SSR_MODE`\n\n- **Description**: Controls whether server-side rendering (SSR) is enabled. When enabled, the initial HTML is rendered on the server rather than the client, which can improve initial page load performance and SEO.\n\n- **Default**: `\"False\"` (except on Hugging Face Spaces, where this environment variable sets it to `True`)\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_SSR_MODE=\"True\"\n  ```\n\n17. `GRADIO_NODE_SERVER_NAME`\n\n- **Description**: Defines the host name for the Gradio node server. (Only applies if `ssr_mode` is set to `True`.)\n- **Default**: `GRADIO_SERVER_NAME` if it is set, otherwise `\"127.0.0.1\"`\n- **Example**:\n  ```sh\n  export GRADIO_NODE_SERVER_NAME=\"0.0.0.0\"\n  ```\n\n18. `GRADIO_NODE_NUM_PORTS`\n\n- **Description**: Defines the number of ports to try when starting the Gradio node server. (Only applies if `ssr_mode` is set to `True`.)\n- **Default**: `100`\n- **Example**:\n  ```sh\n  export GRADIO_NODE_NUM_PORTS=200\n  ```\n\n19. `GRADIO_RESET_EXAMPLES_CACHE`\n\n- **Description**: If set to \"True\", Gradio will delete and recreate the examples cache directory when the app starts instead of reusing the cached example if they already exist. \n- **Default**: `\"False\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_RESET_EXAMPLES_CACHE=\"True\"\n  ```\n\n20. `GRADIO_CHAT_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "e\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_RESET_EXAMPLES_CACHE=\"True\"\n  ```\n\n20. `GRADIO_CHAT_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag messages in `gr.ChatInterface` applications. Similar to `GRADIO_FLAGGING_MODE` but specifically for chat interfaces.\n- **Default**: `\"never\"`\n- **Options**: `\"never\"`, `\"manual\"`\n- **Example**:\n  ```sh\n  export GRADIO_CHAT_FLAGGING_MODE=\"manual\"\n  ```\n\n21. `GRADIO_WATCH_DIRS`\n\n- **Description**: Specifies directories to watch for file changes when running Gradio in development mode. When files in these directories change, the Gradio app will automatically reload. Multiple directories can be specified by separating them with commas. This is primarily used by the `gradio` CLI command for development workflows.\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_WATCH_DIRS=\"/path/to/src,/path/to/templates\"\n  ```\n\n22. `GRADIO_VIBE_MODE`\n\n- **Description**: Enables the Vibe editor mode, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. When enabled, anyone who can access the Gradio endpoint can modify files and run arbitrary code on the host machine. Use with extreme caution in production environments.\n- **Default**: `\"\"`\n- **Options**: Any non-empty string enables the mode\n- **Example**:\n  ```sh\n  export GRADIO_VIBE_MODE=\"1\"\n  ```\n\n\n\n", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "To set environment variables in your terminal, use the `export` command followed by the variable name and its value. For example:\n\n```sh\nexport GRADIO_SERVER_PORT=8000\n```\n\nIf you're using a `.env` file to manage your environment variables, you can add them like this:\n\n```sh\nGRADIO_SERVER_PORT=8000\nGRADIO_SERVER_NAME=\"localhost\"\n```\n\nThen, use a tool like `dotenv` to load these variables when running your application.\n\n\n\n", "heading1": "How to Set Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "You can initialize the `I18n` class with multiple language dictionaries to add custom translations:\n\n```python\nimport gradio as gr\n\nCreate an I18n instance with translations for multiple languages\ni18n = gr.I18n(\n    en={\"greeting\": \"Hello, welcome to my app!\", \"submit\": \"Submit\"},\n    es={\"greeting\": \"\u00a1Hola, bienvenido a mi aplicaci\u00f3n!\", \"submit\": \"Enviar\"},\n    fr={\"greeting\": \"Bonjour, bienvenue dans mon application!\", \"submit\": \"Soumettre\"}\n)\n\nwith gr.Blocks() as demo:\n    Use the i18n method to translate the greeting\n    gr.Markdown(i18n(\"greeting\"))\n    with gr.Row():\n        input_text = gr.Textbox(label=\"Input\")\n        output_text = gr.Textbox(label=\"Output\")\n    \n    submit_btn = gr.Button(i18n(\"submit\"))\n\nPass the i18n instance to the launch method\ndemo.launch(i18n=i18n)\n```\n\n", "heading1": "Setting Up Translations", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "When you use the `i18n` instance with a translation key, Gradio will show the corresponding translation to users based on their browser's language settings or the language they've selected in your app.\n\nIf a translation isn't available for the user's locale, the system will fall back to English (if available) or display the key itself.\n\n", "heading1": "How It Works", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "Locale codes should follow the BCP 47 format (e.g., 'en', 'en-US', 'zh-CN'). The `I18n` class will warn you if you use an invalid locale code.\n\n", "heading1": "Valid Locale Codes", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "The following component properties typically support internationalization:\n\n- `description`\n- `info`\n- `title`\n- `placeholder`\n- `value`\n- `label`\n\nNote that support may vary depending on the component, and some properties might have exceptions where internationalization is not applicable. You can check this by referring to the typehint for the parameter and if it contains `I18nData`, then it supports internationalization.", "heading1": "Supported Component Properties", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "Client side functions are ideal for updating component properties (like visibility, placeholders, interactive state, or styling). \n\nHere's a basic example:\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    with gr.Row() as row:\n        btn = gr.Button(\"Hide this row\")\n    \n    This function runs in the browser without a server roundtrip\n    btn.click(\n        lambda: gr.Row(visible=False), \n        None, \n        row, \n        js=True\n    )\n\ndemo.launch()\n```\n\n\n", "heading1": "When to Use Client Side Functions", "source_page_url": "https://gradio.app/guides/client-side-functions", "source_page_title": "Additional Features - Client Side Functions Guide"}, {"text": "Client side functions have some important restrictions:\n* They can only update component properties (not values)\n* They cannot take any inputs\n\nHere are some functions that will work with `js=True`:\n\n```py\nSimple property updates\nlambda: gr.Textbox(lines=4)\n\nMultiple component updates\nlambda: [gr.Textbox(lines=4), gr.Button(interactive=False)]\n\nUsing gr.update() for property changes\nlambda: gr.update(visible=True, interactive=False)\n```\n\nWe are working to increase the space of functions that can be transpiled to JavaScript so that they can be run in the browser. [Follow the Groovy library for more info](https://github.com/abidlabs/groovy-transpiler).\n\n\n", "heading1": "Limitations", "source_page_url": "https://gradio.app/guides/client-side-functions", "source_page_title": "Additional Features - Client Side Functions Guide"}, {"text": "Here's a more complete example showing how client side functions can improve the user experience:\n\n$code_todo_list_js\n\n\n", "heading1": "Complete Example", "source_page_url": "https://gradio.app/guides/client-side-functions", "source_page_title": "Additional Features - Client Side Functions Guide"}, {"text": "When you set `js=True`, Gradio:\n\n1. Transpiles your Python function to JavaScript\n\n2. Runs the function directly in the browser\n\n3. Still sends the request to the server (for consistency and to handle any side effects)\n\nThis provides immediate visual feedback while ensuring your application state remains consistent.\n", "heading1": "Behind the Scenes", "source_page_url": "https://gradio.app/guides/client-side-functions", "source_page_title": "Additional Features - Client Side Functions Guide"}, {"text": "As of version 5.36.0, Gradio now comes with a built-in MCP server that can upload files to a running Gradio application. In the `View API` page of the server, you should see the following code snippet if any of the tools require file inputs:\n\n<img src=\"https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/MCPConnectionDocs.png\">\n\nThe command to start the MCP server takes two arguments:\n\n- The URL (or Hugging Face space id) of the gradio application to upload the files to. In this case, `http://127.0.0.1:7860`.\n- The local directory on your computer with which the server is allowed to upload files from (`<UPLOAD_DIRECTORY>`). For security, please make this directory as narrow as possible to prevent unintended file uploads.\n\nAs stated in the image, you need to install [uv](https://docs.astral.sh/uv/getting-started/installation/) (a python package manager that can run python scripts) before connecting from your MCP client. \n\nIf you have gradio installed locally and you don't want to install uv, you can replace the `uvx` command with the path to gradio binary. It should look like this:\n\n```json\n\"upload-files\": {\n    \"command\": \"<absoluate-path-to-gradio>\",\n    \"args\": [\n    \"upload-mcp\",\n    \"http://localhost:7860/\",\n    \"/Users/freddyboulton/Pictures\"\n    ]\n}\n```\n\nAfter connecting to the upload server, your LLM agent will know when to upload files for you automatically!\n\n<img src=\"https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/Ghibliafy.png\">\n\n", "heading1": "Using the File Upload MCP Server", "source_page_url": "https://gradio.app/guides/file-upload-mcp", "source_page_title": "Mcp - File Upload Mcp Guide"}, {"text": "In this guide, we've covered how you can connect to the Upload File MCP Server so that your agent can upload files before using Gradio MCP servers. Remember to set the `<UPLOAD_DIRECTORY>` as small as possible to prevent unintended file uploads!\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/file-upload-mcp", "source_page_title": "Mcp - File Upload Mcp Guide"}, {"text": "An MCP (Model Control Protocol) server is a standardized way to expose tools so that they can be used by  LLMs. A tool can provide an LLM functionality that it does not have natively, such as the ability to generate images or calculate the prime factors of a number. \n\n", "heading1": "What is an MCP Server?", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "LLMs are famously not great at counting the number of letters in a word (e.g. the number of \"r\"-s in \"strawberry\"). But what if we equip them with a tool to help? Let's start by writing a simple Gradio app that counts the number of letters in a word or phrase:\n\n$code_letter_counter\n\nNotice that we have: (1) included a detailed docstring for our function, and (2) set `mcp_server=True` in `.launch()`. This is all that's needed for your Gradio app to serve as an MCP server! Now, when you run this app, it will:\n\n1. Start the regular Gradio web interface\n2. Start the MCP server\n3. Print the MCP server URL in the console\n\nThe MCP server will be accessible at:\n```\nhttp://your-server:port/gradio_api/mcp/sse\n```\n\nGradio automatically converts the `letter_counter` function into an MCP tool that can be used by LLMs. The docstring of the function and the type hints of arguments will be used to generate the description of the tool and its parameters. The name of the function will be used as the name of your tool. Any initial values you provide to your input components (e.g. \"strawberry\" and \"r\" in the `gr.Textbox` components above) will be used as the default values if your LLM doesn't specify a value for that particular input parameter.\n\nNow, all you need to do is add this URL endpoint to your MCP Client (e.g. Claude Desktop, Cursor, or Cline), which typically means pasting this config in the settings:\n\n```\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"http://your-server:port/gradio_api/mcp/sse\"\n    }\n  }\n}\n```\n\n(By the way, you can find the exact config to copy-paste by going to the \"View API\" link in the footer of your Gradio app, and then clicking on \"MCP\").\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-mcp.png)\n\n", "heading1": "Example: Counting Letters in a Word", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "1. **Tool Conversion**: Each API endpoint in your Gradio app is automatically converted into an MCP tool with a corresponding name, description, and input schema. To view the tools and schemas, visit http://your-server:port/gradio_api/mcp/schema or go to the \"View API\" link in the footer of your Gradio app, and then click on \"MCP\".\n\n\n2. **Environment variable support**. There are two ways to enable the MCP server functionality:\n\n*  Using the `mcp_server` parameter, as shown above:\n   ```python\n   demo.launch(mcp_server=True)\n   ```\n\n* Using environment variables:\n   ```bash\n   export GRADIO_MCP_SERVER=True\n   ```\n\n3. **File Handling**: The Gradio MCP server automatically handles file data conversions, including:\n   - Processing image files and returning them in the correct format\n   - Managing temporary file storage\n\n    By default, the Gradio MCP server accepts input images and files as full URLs (\"http://...\" or \"https:/...\"). For convenience, an additional STDIO-based MCP server is also generated, which can be used to upload files to any remote Gradio app and which returns a URL that can be used for subsequent tool calls.\n\n4. **Hosted MCP Servers on \udb40\udc20\ud83e\udd17 Spaces**: You can publish your Gradio application for free on Hugging Face Spaces, which will allow you to have a free hosted MCP server. Here's an example of such a Space: https://huggingface.co/spaces/abidlabs/mcp-tools. Notice that you can add this config to your MCP Client to start using the tools from this Space immediately:\n\n```\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"https://abidlabs-mcp-tools.hf.space/gradio_api/mcp/sse\"\n    }\n  }\n}\n```\n\n<video src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/mcp_guide1.mp4\" style=\"width:100%\" controls preload> </video>\n\n\n", "heading1": "Key features of the Gradio <> MCP Integration", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "If there's an existing Space that you'd like to use an MCP server, you'll need to do three things:\n\n1. First, [duplicate the Space](https://huggingface.co/docs/hub/en/spaces-more-ways-to-createduplicating-a-space) if it is not your own Space. This will allow you to make changes to the app. If the Space requires a GPU, set the hardware of the duplicated Space to be same as the original Space. You can make it either a public Space or a private Space, since it is possible to use either as an MCP server, as described below.\n2. Then, add docstrings to the functions that you'd like the LLM to be able to call as a tool. The docstring should be in the same format as the example code above.\n3. Finally, add `mcp_server=True` in `.launch()`.\n\nThat's it!\n\n", "heading1": "Converting an Existing Space", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "You can use either a public Space or a private Space as an MCP server. If you'd like to use a private Space as an MCP server (or a ZeroGPU Space with your own quota), then you will need to provide your [Hugging Face token](https://huggingface.co/settings/token) when you make your request. To do this, simply add it as a header in your config like this:\n\n```\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"https://abidlabs-mcp-tools.hf.space/gradio_api/mcp/sse\",\n      \"headers\": {\n        \"Authorization\": \"Bearer <YOUR-HUGGING-FACE-TOKEN>\"\n      }\n    }\n  }\n}\n```\n\n", "heading1": "Private Spaces", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "You may wish to authenticate users more precisely or let them provide other kinds of credentials or tokens in order to provide a custom experience for different users. \n\nGradio allows you to access the underlying `starlette.Request` that has made the tool call, which means that you can access headers, originating IP address, or any other information that is part of the network request. To do this, simply add a parameter in your function of the type `gr.Request`, and Gradio will automatically inject the request object as the parameter.\n\nHere's an example:\n\n```py\nimport gradio as gr\n\ndef echo_headers(x, request: gr.Request):\n    return str(dict(request.headers))\n\ngr.Interface(echo_headers, \"textbox\", \"textbox\").launch(mcp_server=True)\n```\n\nThis MCP server will simply ignore the user's input and echo back all of the headers from a user's request. One can build more complex apps using the same idea. See the [docs on `gr.Request`](https://www.gradio.app/main/docs/gradio/request) for more information (note that only the core Starlette attributes of the `gr.Request` object will be present, attributes such as Gradio's `.session_hash` will not be present).\n\nUsing the gr.Header class\n\nA common pattern in MCP server development is to use authentication headers to call services on behalf of your users. Instead of using a `gr.Request` object like in the example above, you can use a `gr.Header` argument. Gradio will automatically extract that header from the incoming request (if it exists) and pass it to your function.\n\nIn the example below, the `X-API-Token` header is extracted from the incoming request and passed in as the `x_api_token` argument to `make_api_request_on_behalf_of_user`.\n\nThe benefit of using `gr.Header` is that the MCP connection docs will automatically display the headers you need to supply when connecting to the server! See the image below:\n\n```python\nimport gradio as gr\n\ndef make_api_request_on_behalf_of_user(prompt: str, x_api_token: gr.Header):\n    \"\"\"M", "heading1": "Authentication and Credentials", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "the headers you need to supply when connecting to the server! See the image below:\n\n```python\nimport gradio as gr\n\ndef make_api_request_on_behalf_of_user(prompt: str, x_api_token: gr.Header):\n    \"\"\"Make a request to everyone's favorite API.\n    Args:\n        prompt: The prompt to send to the API.\n    Returns:\n        The response from the API.\n    Raises:\n        AssertionError: If the API token is not valid.\n    \"\"\"\n    return \"Hello from the API\" if not x_api_token else \"Hello from the API with token!\"\n\n\ndemo = gr.Interface(\n    make_api_request_on_behalf_of_user,\n    [\n        gr.Textbox(label=\"Prompt\"),\n    ],\n    gr.Textbox(label=\"Response\"),\n)\n\ndemo.launch(mcp_server=True)\n```\n\n![MCP Header Connection Page](https://github.com/user-attachments/assets/e264eedf-a91a-476b-880d-5be0d5934134)\n\nSending Progress Updates\n\nThe Gradio MCP server automatically sends progress updates to your MCP Client based on the queue in the Gradio application. If you'd like to send custom progress updates, you can do so using the same mechanism as you would use to display progress updates in the UI of your Gradio app: by using the `gr.Progress` class!\n\nHere's an example of how to do this:\n\n$code_mcp_progress\n\n[Here are the docs](https://www.gradio.app/docs/gradio/progress) for the `gr.Progress` class, which can also automatically track `tqdm` calls.\n\n\n", "heading1": "Authentication and Credentials", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "Gradio automatically sets the tool name based on the name of your function, and the description from the docstring of your function. But you may want to change how the description appears to your LLM. You can do this by using the `api_description` parameter in `Interface`, `ChatInterface`, or any event listener. This parameter takes three different kinds of values:\n\n* `None` (default): the tool description is automatically created from the docstring of the function (or its parent's docstring if it does not have a docstring but inherits from a method that does.)\n* `False`: no tool description appears to the LLM.\n* `str`: an arbitrary string to use as the tool description.\n\nIn addition to modifying the tool descriptions, you can also toggle which tools appear to the LLM. You can do this by setting the `show_api` parameter, which is by default `True`. Setting it to `False` hides the endpoint from the API docs and from the MCP server. If you expose multiple tools, users of your app will also be able to toggle which tools they'd like to add to their MCP server by checking boxes in the \"view MCP or API\" panel.\n\nHere's an example that shows the `api_description` and `show_api` parameters in actions:\n\n$code_mcp_tools\n\n", "heading1": "Modifying Tool Descriptions", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "So far, all of our MCP tools have corresponded to event listeners in the UI. This works well for functions that directly update the UI, but may not work if you wish to expose a \"pure logic\" function that should return raw data (e.g. a JSON object) without directly causing a UI update.\n\nIn order to expose such an MCP tool, you can create a pure Gradio API endpoint using `gr.api` (see [full docs here](https://www.gradio.app/main/docs/gradio/api)). Here's an example of creating an MCP tool that slices a list:\n\n$code_mcp_tool_only\n\nNote that if you use this approach, your function signature must be fully typed, including the return value, as these signature are used to determine the typing information for the MCP tool.\n\n\n", "heading1": "Adding MCP-Only Tools", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "In some cases, you may decide not to use Gradio's built-in integration and instead manually create an FastMCP Server that calls a Gradio app. This approach is useful when you want to:\n\n- Store state / identify users between calls instead of treating every tool call completely independently\n- Start the Gradio app MCP server when a tool is called (if you are running multiple Gradio apps locally and want to save memory / GPU)\n\nThis is very doable thanks to the [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) and the [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)'s `FastMCP` class. Here's an example of creating a custom MCP server that connects to various Gradio apps hosted on [HuggingFace Spaces](https://huggingface.co/spaces) using the `stdio` protocol:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nfrom gradio_client import Client\nimport sys\nimport io\nimport json \n\nmcp = FastMCP(\"gradio-spaces\")\n\nclients = {}\n\ndef get_client(space_id: str) -> Client:\n    \"\"\"Get or create a Gradio client for the specified space.\"\"\"\n    if space_id not in clients:\n        clients[space_id] = Client(space_id)\n    return clients[space_id]\n\n\n@mcp.tool()\nasync def generate_image(prompt: str, space_id: str = \"ysharma/SanaSprint\") -> str:\n    \"\"\"Generate an image using Flux.\n    \n    Args:\n        prompt: Text prompt describing the image to generate\n        space_id: HuggingFace Space ID to use \n    \"\"\"\n    client = get_client(space_id)\n    result = client.predict(\n            prompt=prompt,\n            model_size=\"1.6B\",\n            seed=0,\n            randomize_seed=True,\n            width=1024,\n            height=1024,\n            guidance_scale=4.5,\n            num_inference_steps=2,\n            api_name=\"/infer\"\n    )\n    return result\n\n\n@mcp.tool()\nasync def run_dia_tts(prompt: str, space_id: str = \"ysharma/Dia-1.6B\") -> str:\n    \"\"\"Text-to-Speech Synthesis.\n    \n    Args:\n        prompt: Text prompt describing the co", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "return result\n\n\n@mcp.tool()\nasync def run_dia_tts(prompt: str, space_id: str = \"ysharma/Dia-1.6B\") -> str:\n    \"\"\"Text-to-Speech Synthesis.\n    \n    Args:\n        prompt: Text prompt describing the conversation between speakers S1, S2\n        space_id: HuggingFace Space ID to use \n    \"\"\"\n    client = get_client(space_id)\n    result = client.predict(\n            text_input=f\"\"\"{prompt}\"\"\",\n            audio_prompt_input=None, \n            max_new_tokens=3072,\n            cfg_scale=3,\n            temperature=1.3,\n            top_p=0.95,\n            cfg_filter_top_k=30,\n            speed_factor=0.94,\n            api_name=\"/generate_audio\"\n    )\n    return result\n\n\nif __name__ == \"__main__\":\n    import sys\n    import io\n    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')\n    \n    mcp.run(transport='stdio')\n```\n\nThis server exposes two tools:\n1. `run_dia_tts` - Generates a conversation for the given transcript in the form of `[S1]first-sentence. [S2]second-sentence. [S1]...`\n2. `generate_image` - Generates images using a fast text-to-image model\n\nTo use this MCP Server with Claude Desktop (as MCP Client):\n\n1. Save the code to a file (e.g., `gradio_mcp_server.py`)\n2. Install the required dependencies: `pip install mcp gradio-client`\n3. Configure Claude Desktop to use your server by editing the configuration file at `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\\Claude\\claude_desktop_config.json` (Windows):\n\n```json\n{\n    \"mcpServers\": {\n        \"gradio-spaces\": {\n            \"command\": \"python\",\n            \"args\": [\n                \"/absolute/path/to/gradio_mcp_server.py\"\n            ]\n        }\n    }\n}\n```\n\n4. Restart Claude Desktop\n\nNow, when you ask Claude about generating an image or transcribing audio, it can use your Gradio-powered tools to accomplish these tasks.\n\n\n", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "use your Gradio-powered tools to accomplish these tasks.\n\n\n", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "The MCP protocol is still in its infancy and you might see issues connecting to an MCP Server that you've built. We generally recommend using the [MCP Inspector Tool](https://github.com/modelcontextprotocol/inspector) to try connecting and debugging your MCP Server.\n\nHere are some things that may help:\n\n**1. Ensure that you've provided type hints and valid docstrings for your functions**\n\nAs mentioned earlier, Gradio reads the docstrings for your functions and the type hints of input arguments to generate the description of the tool and parameters. A valid function and docstring looks like this (note the \"Args:\" block with indented parameter names underneath):\n\n```py\ndef image_orientation(image: Image.Image) -> str:\n    \"\"\"\n    Returns whether image is portrait or landscape.\n\n    Args:\n        image (Image.Image): The image to check.\n    \"\"\"\n    return \"Portrait\" if image.height > image.width else \"Landscape\"\n```\n\nNote: You can preview the schema that is created for your MCP server by visiting the `http://your-server:port/gradio_api/mcp/schema` URL.\n\n**2. Try accepting input arguments as `str`**\n\nSome MCP Clients do not recognize parameters that are numeric or other complex types, but all of the MCP Clients that we've tested accept `str` input parameters. When in doubt, change your input parameter to be a `str` and then cast to a specific type in the function, as in this example:\n\n```py\ndef prime_factors(n: str):\n    \"\"\"\n    Compute the prime factorization of a positive integer.\n\n    Args:\n        n (str): The integer to factorize. Must be greater than 1.\n    \"\"\"\n    n_int = int(n)\n    if n_int <= 1:\n        raise ValueError(\"Input must be an integer greater than 1.\")\n\n    factors = []\n    while n_int % 2 == 0:\n        factors.append(2)\n        n_int //= 2\n\n    divisor = 3\n    while divisor * divisor <= n_int:\n        while n_int % divisor == 0:\n            factors.append(divisor)\n            n_int //= divisor\n        divisor += 2\n\n    if n_int > 1:\n        factors.", "heading1": "Troubleshooting your MCP Servers", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "= 3\n    while divisor * divisor <= n_int:\n        while n_int % divisor == 0:\n            factors.append(divisor)\n            n_int //= divisor\n        divisor += 2\n\n    if n_int > 1:\n        factors.append(n_int)\n\n    return factors\n```\n\n**3. Ensure that your MCP Client Supports SSE**\n\nSome MCP Clients, notably [Claude Desktop](https://claude.ai/download), do not yet support SSE-based MCP Servers. In those cases, you can use a tool such as [mcp-remote](https://github.com/geelen/mcp-remote). First install [Node.js](https://nodejs.org/en/download/). Then, add the following to your own MCP Client config:\n\n```\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"command\": \"npx\",\n      \"args\": [\n        \"mcp-remote\",\n        \"http://your-server:port/gradio_api/mcp/sse\"\n      ]\n    }\n  }\n}\n```\n\n**4. Restart your MCP Client and MCP Server**\n\nSome MCP Clients require you to restart them every time you update the MCP configuration. Other times, if the connection between the MCP Client and servers breaks, you might need to restart the MCP server. If all else fails, try restarting both your MCP Client and MCP Servers!\n\n", "heading1": "Troubleshooting your MCP Servers", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "If you're using LLMs in your workflow, adding this server will augment them with just the right context on gradio - which makes your experience a lot faster and smoother. \n\n<video src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/mcp-docs.mp4\" style=\"width:100%\" controls preload> </video>\n\nThe server is running on Spaces and was launched entirely using Gradio, you can see all the code [here](https://huggingface.co/spaces/gradio/docs-mcp). For more on building an mcp server with gradio, see the [previous guide](./building-an-mcp-client-with-gradio). \n\n", "heading1": "Why an MCP Server?", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "For clients that support SSE (e.g. Cursor, Windsurf, Cline), simply add the following configuration to your MCP config:\n\n```json\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/sse\"\n    }\n  }\n}\n```\n\nWe've included step-by-step instructions for Cursor below, but you can consult the docs for Windsurf [here](https://docs.windsurf.com/windsurf/mcp), and Cline [here](https://docs.cline.bot/mcp-servers/configuring-mcp-servers) which are similar to set up. \n\n\n\nCursor \n\n1. Make sure you're using the latest version of Cursor, and go to Cursor > Settings > Cursor Settings > MCP \n2. Click on '+ Add new global MCP server' \n3. Copy paste this json into the file that opens and then save it. \n```json\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/sse\"\n    }\n  }\n}\n```\n4. That's it! You should see the tools load and the status go green in the settings page. You may have to click the refresh icon or wait a few seconds. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/cursor-mcp.png)\n\nClaude Desktop\n\n1. Since Claude Desktop only supports stdio, you will need to [install Node.js](https://nodejs.org/en/download/) to get this to work. \n2. Make sure you're using the latest version of Claude Desktop, and go to Claude > Settings > Developer > Edit Config \n3. Open the file with your favorite editor and copy paste this json, then save the file. \n```json\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"command\": \"npx\",\n      \"args\": [\n        \"mcp-remote\",\n        \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/sse\",\n        \"--transport\",\n        \"sse-only\"\n      ]\n    }\n  }\n}\n```\n4. Quit and re-open Claude Desktop, and you should be good to go. You should see it loaded in the Search and Tools icon or on the developer settings page. \n \n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/claude-deskt", "heading1": "Installing in the Clients", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "You should see it loaded in the Search and Tools icon or on the developer settings page. \n \n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/claude-desktop-mcp.gif)\n\n", "heading1": "Installing in the Clients", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "There are currently only two tools in the server: `gradio_docs_mcp_load_gradio_docs` and `gradio_docs_mcp_search_gradio_docs`. \n\n1. `gradio_docs_mcp_load_gradio_docs`: This tool takes no arguments and will load an /llms.txt style summary of Gradio's latest, full documentation. Very useful context the LLM can parse before answering questions or generating code. \n\n2. `gradio_docs_mcp_search_gradio_docs`: This tool takes a query as an argument and will run embedding search on Gradio's docs, guides, and demos to return the most useful context for the LLM to parse.", "heading1": "Tools", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "The Model Context Protocol (MCP) standardizes how applications provide context to LLMs. It allows Claude to interact with external tools, like image generators, file systems, or APIs, etc.\n\n", "heading1": "What is MCP?", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "- Python 3.10+\n- An Anthropic API key\n- Basic understanding of Python programming\n\n", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "First, install the required packages:\n\n```bash\npip install gradio anthropic mcp\n```\n\nCreate a `.env` file in your project directory and add your Anthropic API key:\n\n```\nANTHROPIC_API_KEY=your_api_key_here\n```\n\n", "heading1": "Setup", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "The server provides tools that Claude can use. In this example, we'll create a server that generates images through [a HuggingFace space](https://huggingface.co/spaces/ysharma/SanaSprint).\n\nCreate a file named `gradio_mcp_server.py`:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nimport json\nimport sys\nimport io\nimport time\nfrom gradio_client import Client\n\nsys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')\nsys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')\n\nmcp = FastMCP(\"huggingface_spaces_image_display\")\n\n@mcp.tool()\nasync def generate_image(prompt: str, width: int = 512, height: int = 512) -> str:\n    \"\"\"Generate an image using SanaSprint model.\n    \n    Args:\n        prompt: Text prompt describing the image to generate\n        width: Image width (default: 512)\n        height: Image height (default: 512)\n    \"\"\"\n    client = Client(\"https://ysharma-sanasprint.hf.space/\")\n    \n    try:\n        result = client.predict(\n            prompt,\n            \"0.6B\",\n            0,\n            True,\n            width,\n            height,\n            4.0,\n            2,\n            api_name=\"/infer\"\n        )\n        \n        if isinstance(result, list) and len(result) >= 1:\n            image_data = result[0]\n            if isinstance(image_data, dict) and \"url\" in image_data:\n                return json.dumps({\n                    \"type\": \"image\",\n                    \"url\": image_data[\"url\"],\n                    \"message\": f\"Generated image for prompt: {prompt}\"\n                })\n        \n        return json.dumps({\n            \"type\": \"error\",\n            \"message\": \"Failed to generate image\"\n        })\n        \n    except Exception as e:\n        return json.dumps({\n            \"type\": \"error\",\n            \"message\": f\"Error generating image: {str(e)}\"\n        })\n\nif __name__ == \"__main__\":\n    mcp.run(transport='stdio')\n```\n\nWhat this server does:\n\n1. It creates an MCP server that exposes a `gene", "heading1": "Part 1: Building the MCP Server", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "  \"message\": f\"Error generating image: {str(e)}\"\n        })\n\nif __name__ == \"__main__\":\n    mcp.run(transport='stdio')\n```\n\nWhat this server does:\n\n1. It creates an MCP server that exposes a `generate_image` tool\n2. The tool connects to the SanaSprint model hosted on HuggingFace Spaces\n3. It handles the asynchronous nature of image generation by polling for results\n4. When an image is ready, it returns the URL in a structured JSON format\n\n", "heading1": "Part 1: Building the MCP Server", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now let's create a Gradio chat interface as MCP Client that connects Claude to our MCP server.\n\nCreate a file named `app.py`:\n\n```python\nimport asyncio\nimport os\nimport json\nfrom typing import List, Dict, Any, Union\nfrom contextlib import AsyncExitStack\n\nimport gradio as gr\nfrom gradio.components.chatbot import ChatMessage\nfrom mcp import ClientSession, StdioServerParameters\nfrom mcp.client.stdio import stdio_client\nfrom anthropic import Anthropic\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nloop = asyncio.new_event_loop()\nasyncio.set_event_loop(loop)\n\nclass MCPClientWrapper:\n    def __init__(self):\n        self.session = None\n        self.exit_stack = None\n        self.anthropic = Anthropic()\n        self.tools = []\n    \n    def connect(self, server_path: str) -> str:\n        return loop.run_until_complete(self._connect(server_path))\n    \n    async def _connect(self, server_path: str) -> str:\n        if self.exit_stack:\n            await self.exit_stack.aclose()\n        \n        self.exit_stack = AsyncExitStack()\n        \n        is_python = server_path.endswith('.py')\n        command = \"python\" if is_python else \"node\"\n        \n        server_params = StdioServerParameters(\n            command=command,\n            args=[server_path],\n            env={\"PYTHONIOENCODING\": \"utf-8\", \"PYTHONUNBUFFERED\": \"1\"}\n        )\n        \n        stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))\n        self.stdio, self.write = stdio_transport\n        \n        self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))\n        await self.session.initialize()\n        \n        response = await self.session.list_tools()\n        self.tools = [{ \n            \"name\": tool.name,\n            \"description\": tool.description,\n            \"input_schema\": tool.inputSchema\n        } for tool in response.tools]\n        \n        tool_names = [tool[\"name\"] for tool in self.tools]\n        return f\"Connected to MCP server.", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "iption,\n            \"input_schema\": tool.inputSchema\n        } for tool in response.tools]\n        \n        tool_names = [tool[\"name\"] for tool in self.tools]\n        return f\"Connected to MCP server. Available tools: {', '.join(tool_names)}\"\n    \n    def process_message(self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]) -> tuple:\n        if not self.session:\n            return history + [\n                {\"role\": \"user\", \"content\": message}, \n                {\"role\": \"assistant\", \"content\": \"Please connect to an MCP server first.\"}\n            ], gr.Textbox(value=\"\")\n        \n        new_messages = loop.run_until_complete(self._process_query(message, history))\n        return history + [{\"role\": \"user\", \"content\": message}] + new_messages, gr.Textbox(value=\"\")\n    \n    async def _process_query(self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]):\n        claude_messages = []\n        for msg in history:\n            if isinstance(msg, ChatMessage):\n                role, content = msg.role, msg.content\n            else:\n                role, content = msg.get(\"role\"), msg.get(\"content\")\n            \n            if role in [\"user\", \"assistant\", \"system\"]:\n                claude_messages.append({\"role\": role, \"content\": content})\n        \n        claude_messages.append({\"role\": \"user\", \"content\": message})\n        \n        response = self.anthropic.messages.create(\n            model=\"claude-3-5-sonnet-20241022\",\n            max_tokens=1000,\n            messages=claude_messages,\n            tools=self.tools\n        )\n\n        result_messages = []\n        \n        for content in response.content:\n            if content.type == 'text':\n                result_messages.append({\n                    \"role\": \"assistant\", \n                    \"content\": content.text\n                })\n                \n            elif content.type == 'tool_use':\n                tool_name = content.name\n                tool_args = content.input\n               ", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "ntent\": content.text\n                })\n                \n            elif content.type == 'tool_use':\n                tool_name = content.name\n                tool_args = content.input\n                \n                result_messages.append({\n                    \"role\": \"assistant\",\n                    \"content\": f\"I'll use the {tool_name} tool to help answer your question.\",\n                    \"metadata\": {\n                        \"title\": f\"Using tool: {tool_name}\",\n                        \"log\": f\"Parameters: {json.dumps(tool_args, ensure_ascii=True)}\",\n                        \"status\": \"pending\",\n                        \"id\": f\"tool_call_{tool_name}\"\n                    }\n                })\n                \n                result_messages.append({\n                    \"role\": \"assistant\",\n                    \"content\": \"```json\\n\" + json.dumps(tool_args, indent=2, ensure_ascii=True) + \"\\n```\",\n                    \"metadata\": {\n                        \"parent_id\": f\"tool_call_{tool_name}\",\n                        \"id\": f\"params_{tool_name}\",\n                        \"title\": \"Tool Parameters\"\n                    }\n                })\n                \n                result = await self.session.call_tool(tool_name, tool_args)\n                \n                if result_messages and \"metadata\" in result_messages[-2]:\n                    result_messages[-2][\"metadata\"][\"status\"] = \"done\"\n                \n                result_messages.append({\n                    \"role\": \"assistant\",\n                    \"content\": \"Here are the results from the tool:\",\n                    \"metadata\": {\n                        \"title\": f\"Tool Result for {tool_name}\",\n                        \"status\": \"done\",\n                        \"id\": f\"result_{tool_name}\"\n                    }\n                })\n                \n                result_content = result.content\n                if isinstance(result_content, list):\n                    result_content = \"\\n\".join(str(item) for item in re", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "        })\n                \n                result_content = result.content\n                if isinstance(result_content, list):\n                    result_content = \"\\n\".join(str(item) for item in result_content)\n                \n                try:\n                    result_json = json.loads(result_content)\n                    if isinstance(result_json, dict) and \"type\" in result_json:\n                        if result_json[\"type\"] == \"image\" and \"url\" in result_json:\n                            result_messages.append({\n                                \"role\": \"assistant\",\n                                \"content\": {\"path\": result_json[\"url\"], \"alt_text\": result_json.get(\"message\", \"Generated image\")},\n                                \"metadata\": {\n                                    \"parent_id\": f\"result_{tool_name}\",\n                                    \"id\": f\"image_{tool_name}\",\n                                    \"title\": \"Generated Image\"\n                                }\n                            })\n                        else:\n                            result_messages.append({\n                                \"role\": \"assistant\",\n                                \"content\": \"```\\n\" + result_content + \"\\n```\",\n                                \"metadata\": {\n                                    \"parent_id\": f\"result_{tool_name}\",\n                                    \"id\": f\"raw_result_{tool_name}\",\n                                    \"title\": \"Raw Output\"\n                                }\n                            })\n                except:\n                    result_messages.append({\n                        \"role\": \"assistant\",\n                        \"content\": \"```\\n\" + result_content + \"\\n```\",\n                        \"metadata\": {\n                            \"parent_id\": f\"result_{tool_name}\",\n                            \"id\": f\"raw_result_{tool_name}\",\n                            \"title\": \"Raw Output\"\n                        }\n                    })\n   ", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": " \"parent_id\": f\"result_{tool_name}\",\n                            \"id\": f\"raw_result_{tool_name}\",\n                            \"title\": \"Raw Output\"\n                        }\n                    })\n                \n                claude_messages.append({\"role\": \"user\", \"content\": f\"Tool result for {tool_name}: {result_content}\"})\n                next_response = self.anthropic.messages.create(\n                    model=\"claude-3-5-sonnet-20241022\",\n                    max_tokens=1000,\n                    messages=claude_messages,\n                )\n                \n                if next_response.content and next_response.content[0].type == 'text':\n                    result_messages.append({\n                        \"role\": \"assistant\",\n                        \"content\": next_response.content[0].text\n                    })\n\n        return result_messages\n\nclient = MCPClientWrapper()\n\ndef gradio_interface():\n    with gr.Blocks(title=\"MCP Weather Client\") as demo:\n        gr.Markdown(\"MCP Weather Assistant\")\n        gr.Markdown(\"Connect to your MCP weather server and chat with the assistant\")\n        \n        with gr.Row(equal_height=True):\n            with gr.Column(scale=4):\n                server_path = gr.Textbox(\n                    label=\"Server Script Path\",\n                    placeholder=\"Enter path to server script (e.g., weather.py)\",\n                    value=\"gradio_mcp_server.py\"\n                )\n            with gr.Column(scale=1):\n                connect_btn = gr.Button(\"Connect\")\n        \n        status = gr.Textbox(label=\"Connection Status\", interactive=False)\n        \n        chatbot = gr.Chatbot(\n            value=[], \n            height=500,\n            type=\"messages\",\n            show_copy_button=True,\n            avatar_images=(\"\ud83d\udc64\", \"\ud83e\udd16\")\n        )\n        \n        with gr.Row(equal_height=True):\n            msg = gr.Textbox(\n                label=\"Your Question\",\n                placeholder=\"Ask about weather or alerts (e.g., What's the weath", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": ")\n        \n        with gr.Row(equal_height=True):\n            msg = gr.Textbox(\n                label=\"Your Question\",\n                placeholder=\"Ask about weather or alerts (e.g., What's the weather in New York?)\",\n                scale=4\n            )\n            clear_btn = gr.Button(\"Clear Chat\", scale=1)\n        \n        connect_btn.click(client.connect, inputs=server_path, outputs=status)\n        msg.submit(client.process_message, [msg, chatbot], [chatbot, msg])\n        clear_btn.click(lambda: [], None, chatbot)\n        \n    return demo\n\nif __name__ == \"__main__\":\n    if not os.getenv(\"ANTHROPIC_API_KEY\"):\n        print(\"Warning: ANTHROPIC_API_KEY not found in environment. Please set it in your .env file.\")\n    \n    interface = gradio_interface()\n    interface.launch(debug=True)\n```\n\nWhat this MCP Client does:\n\n- Creates a friendly Gradio chat interface for user interaction\n- Connects to the MCP server you specify\n- Handles conversation history and message formatting\n- Makes call to Claude API with tool definitions\n- Processes tool usage requests from Claude\n- Displays images and other tool outputs in the chat\n- Sends tool results back to Claude for interpretation\n\n", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "To run your MCP application:\n\n- Start a terminal window and run the MCP Client:\n   ```bash\n   python app.py\n   ```\n- Open the Gradio interface at the URL shown (typically http://127.0.0.1:7860)\n- In the Gradio interface, you'll see a field for the MCP Server path. It should default to `gradio_mcp_server.py`.\n- Click \"Connect\" to establish the connection to the MCP server.\n- You should see a message indicating the server connection was successful.\n\n", "heading1": "Running the Application", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now you can chat with Claude and it will be able to generate images based on your descriptions.\n\nTry prompts like:\n- \"Can you generate an image of a mountain landscape at sunset?\"\n- \"Create an image of a cool tabby cat\"\n- \"Generate a picture of a panda wearing sunglasses\"\n\nClaude will recognize these as image generation requests and automatically use the `generate_image` tool from your MCP server.\n\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Here's the high-level flow of what happens during a chat session:\n\n1. Your prompt enters the Gradio interface\n2. The client forwards your prompt to Claude\n3. Claude analyzes the prompt and decides to use the `generate_image` tool\n4. The client sends the tool call to the MCP server\n5. The server calls the external image generation API\n6. The image URL is returned to the client\n7. The client sends the image URL back to Claude\n8. Claude provides a response that references the generated image\n9. The Gradio chat interface displays both Claude's response and the image\n\n\n", "heading1": "How it Works", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now that you have a working MCP system, here are some ideas to extend it:\n\n- Add more tools to your server\n- Improve error handling \n- Add private Huggingface Spaces with authentication for secure tool access\n- Create custom tools that connect to your own APIs or services\n- Implement streaming responses for better user experience\n\n", "heading1": "Next Steps", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Congratulations! You've successfully built an MCP Client and Server that allows Claude to generate images based on text prompts. This is just the beginning of what you can do with Gradio and MCP. This guide enables you to build complex AI applications that can use Claude or any other powerful LLM to interact with virtually any external tool or service.\n\nRead our other Guide on using [Gradio apps as MCP Servers](./building-mcp-server-with-gradio).\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "This guide explains how you can run background tasks from your gradio app.\nBackground tasks are operations that you'd like to perform outside the request-response\nlifecycle of your app either once or on a periodic schedule.\nExamples of background tasks include periodically synchronizing data to an external database or\nsending a report of model predictions via email.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "We will be creating a simple \"Google-forms-style\" application to gather feedback from users of the gradio library.\nWe will use a local sqlite database to store our data, but we will periodically synchronize the state of the database\nwith a [HuggingFace Dataset](https://huggingface.co/datasets) so that our user reviews are always backed up.\nThe synchronization will happen in a background task running every 60 seconds.\n\nAt the end of the demo, you'll have a fully working application like this one:\n\n<gradio-app space=\"freddyaboulton/gradio-google-forms\"> </gradio-app>\n\n", "heading1": "Overview", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "Our application will store the name of the reviewer, their rating of gradio on a scale of 1 to 5, as well as\nany comments they want to share about the library. Let's write some code that creates a database table to\nstore this data. We'll also write some functions to insert a review into that table and fetch the latest 10 reviews.\n\nWe're going to use the `sqlite3` library to connect to our sqlite database but gradio will work with any library.\n\nThe code will look like this:\n\n```python\nDB_FILE = \"./reviews.db\"\ndb = sqlite3.connect(DB_FILE)\n\nCreate table if it doesn't already exist\ntry:\n    db.execute(\"SELECT * FROM reviews\").fetchall()\n    db.close()\nexcept sqlite3.OperationalError:\n    db.execute(\n        '''\n        CREATE TABLE reviews (id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n                              created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL,\n                              name TEXT, review INTEGER, comments TEXT)\n        ''')\n    db.commit()\n    db.close()\n\ndef get_latest_reviews(db: sqlite3.Connection):\n    reviews = db.execute(\"SELECT * FROM reviews ORDER BY id DESC limit 10\").fetchall()\n    total_reviews = db.execute(\"Select COUNT(id) from reviews\").fetchone()[0]\n    reviews = pd.DataFrame(reviews, columns=[\"id\", \"date_created\", \"name\", \"review\", \"comments\"])\n    return reviews, total_reviews\n\n\ndef add_review(name: str, review: int, comments: str):\n    db = sqlite3.connect(DB_FILE)\n    cursor = db.cursor()\n    cursor.execute(\"INSERT INTO reviews(name, review, comments) VALUES(?,?,?)\", [name, review, comments])\n    db.commit()\n    reviews, total_reviews = get_latest_reviews(db)\n    db.close()\n    return reviews, total_reviews\n```\n\nLet's also write a function to load the latest reviews when the gradio application loads:\n\n```python\ndef load_data():\n    db = sqlite3.connect(DB_FILE)\n    reviews, total_reviews = get_latest_reviews(db)\n    db.close()\n    return reviews, total_reviews\n```\n\n", "heading1": "Step 1 - Write your database logic \ud83d\udcbe", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "Now that we have our database logic defined, we can use gradio create a dynamic web page to ask our users for feedback!\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Row():\n        with gr.Column():\n            name = gr.Textbox(label=\"Name\", placeholder=\"What is your name?\")\n            review = gr.Radio(label=\"How satisfied are you with using gradio?\", choices=[1, 2, 3, 4, 5])\n            comments = gr.Textbox(label=\"Comments\", lines=10, placeholder=\"Do you have any feedback on gradio?\")\n            submit = gr.Button(value=\"Submit Feedback\")\n        with gr.Column():\n            data = gr.Dataframe(label=\"Most recently created 10 rows\")\n            count = gr.Number(label=\"Total number of reviews\")\n    submit.click(add_review, [name, review, comments], [data, count])\n    demo.load(load_data, None, [data, count])\n```\n\n", "heading1": "Step 2 - Create a gradio app \u26a1", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "We could call `demo.launch()` after step 2 and have a fully functioning application. However,\nour data would be stored locally on our machine. If the sqlite file were accidentally deleted, we'd lose all of our reviews!\nLet's back up our data to a dataset on the HuggingFace hub.\n\nCreate a dataset [here](https://huggingface.co/datasets) before proceeding.\n\nNow at the **top** of our script, we'll use the [huggingface hub client library](https://huggingface.co/docs/huggingface_hub/index)\nto connect to our dataset and pull the latest backup.\n\n```python\nTOKEN = os.environ.get('HUB_TOKEN')\nrepo = huggingface_hub.Repository(\n    local_dir=\"data\",\n    repo_type=\"dataset\",\n    clone_from=\"<name-of-your-dataset>\",\n    use_auth_token=TOKEN\n)\nrepo.git_pull()\n\nshutil.copyfile(\"./data/reviews.db\", DB_FILE)\n```\n\nNote that you'll have to get an access token from the \"Settings\" tab of your HuggingFace for the above code to work.\nIn the script, the token is securely accessed via an environment variable.\n\n![access_token](https://github.com/gradio-app/gradio/blob/main/guides/assets/access_token.png?raw=true)\n\nNow we will create a background task to synch our local database to the dataset hub every 60 seconds.\nWe will use the [AdvancedPythonScheduler](https://apscheduler.readthedocs.io/en/3.x/) to handle the scheduling.\nHowever, this is not the only task scheduling library available. Feel free to use whatever you are comfortable with.\n\nThe function to back up our data will look like this:\n\n```python\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\ndef backup_db():\n    shutil.copyfile(DB_FILE, \"./data/reviews.db\")\n    db = sqlite3.connect(DB_FILE)\n    reviews = db.execute(\"SELECT * FROM reviews\").fetchall()\n    pd.DataFrame(reviews).to_csv(\"./data/reviews.csv\", index=False)\n    print(\"updating db\")\n    repo.push_to_hub(blocking=False, commit_message=f\"Updating data at {datetime.datetime.now()}\")\n\n\nscheduler = BackgroundScheduler()\nscheduler.add_job(func=backup_db, trigge", "heading1": "Step 3 - Synchronize with HuggingFace Datasets \ud83e\udd17", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "    print(\"updating db\")\n    repo.push_to_hub(blocking=False, commit_message=f\"Updating data at {datetime.datetime.now()}\")\n\n\nscheduler = BackgroundScheduler()\nscheduler.add_job(func=backup_db, trigger=\"interval\", seconds=60)\nscheduler.start()\n```\n\n", "heading1": "Step 3 - Synchronize with HuggingFace Datasets \ud83e\udd17", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "You can use the HuggingFace [Spaces](https://huggingface.co/spaces) platform to deploy this application for free \u2728\n\nIf you haven't used Spaces before, follow the previous guide [here](/using_hugging_face_integrations).\nYou will have to use the `HUB_TOKEN` environment variable as a secret in the Guides.\n\n", "heading1": "Step 4 (Bonus) - Deployment to HuggingFace Spaces", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "Congratulations! You know how to run background tasks from your gradio app on a schedule \u23f2\ufe0f.\n\nCheckout the application running on Spaces [here](https://huggingface.co/spaces/freddyaboulton/gradio-google-forms).\nThe complete code is [here](https://huggingface.co/spaces/freddyaboulton/gradio-google-forms/blob/main/app.py)\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "It seems that cryptocurrencies, [NFTs](https://www.nytimes.com/interactive/2022/03/18/technology/nft-guide.html), and the web3 movement are all the rage these days! Digital assets are being listed on marketplaces for astounding amounts of money, and just about every celebrity is debuting their own NFT collection. While your crypto assets [may be taxable, such as in Canada](https://www.canada.ca/en/revenue-agency/programs/about-canada-revenue-agency-cra/compliance/digital-currency/cryptocurrency-guide.html), today we'll explore some fun and tax-free ways to generate your own assortment of procedurally generated [CryptoPunks](https://www.larvalabs.com/cryptopunks).\n\nGenerative Adversarial Networks, often known just as _GANs_, are a specific class of deep-learning models that are designed to learn from an input dataset to create (_generate!_) new material that is convincingly similar to elements of the original training set. Famously, the website [thispersondoesnotexist.com](https://thispersondoesnotexist.com/) went viral with lifelike, yet synthetic, images of people generated with a model called StyleGAN2. GANs have gained traction in the machine learning world, and are now being used to generate all sorts of images, text, and even [music](https://salu133445.github.io/musegan/)!\n\nToday we'll briefly look at the high-level intuition behind GANs, and then we'll build a small demo around a pre-trained GAN to see what all the fuss is about. Here's a [peek](https://nimaboscarino-cryptopunks.hf.space) at what we're going to be putting together.\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). To use the pretrained model, also install `torch` and `torchvision`.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Originally proposed in [Goodfellow et al. 2014](https://arxiv.org/abs/1406.2661), GANs are made up of neural networks which compete with the intention of outsmarting each other. One network, known as the _generator_, is responsible for generating images. The other network, the _discriminator_, receives an image at a time from the generator along with a **real** image from the training data set. The discriminator then has to guess: which image is the fake?\n\nThe generator is constantly training to create images which are trickier for the discriminator to identify, while the discriminator raises the bar for the generator every time it correctly detects a fake. As the networks engage in this competitive (_adversarial!_) relationship, the images that get generated improve to the point where they become indistinguishable to human eyes!\n\nFor a more in-depth look at GANs, you can take a look at [this excellent post on Analytics Vidhya](https://www.analyticsvidhya.com/blog/2021/06/a-detailed-explanation-of-gan-with-implementation-using-tensorflow-and-keras/) or this [PyTorch tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html). For now, though, we'll dive into a demo!\n\n", "heading1": "GANs: a very brief introduction", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "To generate new images with a GAN, you only need the generator model. There are many different architectures that the generator could use, but for this demo we'll use a pretrained GAN generator model with the following architecture:\n\n```python\nfrom torch import nn\n\nclass Generator(nn.Module):\n    Refer to the link below for explanations about nc, nz, and ngf\n    https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.htmlinputs\n    def __init__(self, nc=4, nz=100, ngf=64):\n        super(Generator, self).__init__()\n        self.network = nn.Sequential(\n            nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),\n            nn.BatchNorm2d(ngf * 4),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),\n            nn.BatchNorm2d(ngf * 2),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),\n            nn.BatchNorm2d(ngf),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n            nn.Tanh(),\n        )\n\n    def forward(self, input):\n        output = self.network(input)\n        return output\n```\n\nWe're taking the generator from [this repo by @teddykoker](https://github.com/teddykoker/cryptopunks-gan/blob/main/train.pyL90), where you can also see the original discriminator model structure.\n\nAfter instantiating the model, we'll load in the weights from the Hugging Face Hub, stored at [nateraw/cryptopunks-gan](https://huggingface.co/nateraw/cryptopunks-gan):\n\n```python\nfrom huggingface_hub import hf_hub_download\nimport torch\n\nmodel = Generator()\nweights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')\nmodel.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) Use 'cuda' if you have a GPU available\n```\n\n", "heading1": "Step 1 \u2014 Create the Generator model", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "The `predict` function is the key to making Gradio work! Whatever inputs we choose through the Gradio interface will get passed through our `predict` function, which should operate on the inputs and generate outputs that we can display with Gradio output components. For GANs it's common to pass random noise into our model as the input, so we'll generate a tensor of random numbers and pass that through the model. We can then use `torchvision`'s `save_image` function to save the output of the model as a `png` file, and return the file name:\n\n```python\nfrom torchvision.utils import save_image\n\ndef predict(seed):\n    num_punks = 4\n    torch.manual_seed(seed)\n    z = torch.randn(num_punks, 100, 1, 1)\n    punks = model(z)\n    save_image(punks, \"punks.png\", normalize=True)\n    return 'punks.png'\n```\n\nWe're giving our `predict` function a `seed` parameter, so that we can fix the random tensor generation with a seed. We'll then be able to reproduce punks if we want to see them again by passing in the same seed.\n\n_Note!_ Our model needs an input tensor of dimensions 100x1x1 to do a single inference, or (BatchSize)x100x1x1 for generating a batch of images. In this demo we'll start by generating 4 punks at a time.\n\n", "heading1": "Step 2 \u2014 Defining a `predict` function", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "At this point you can even run the code you have with `predict(<SOME_NUMBER>)`, and you'll find your freshly generated punks in your file system at `./punks.png`. To make a truly interactive demo, though, we'll build out a simple interface with Gradio. Our goals here are to:\n\n- Set a slider input so users can choose the \"seed\" value\n- Use an image component for our output to showcase the generated punks\n- Use our `predict()` to take the seed and generate the images\n\nWith `gr.Interface()`, we can define all of that with a single function call:\n\n```python\nimport gradio as gr\n\ngr.Interface(\n    predict,\n    inputs=[\n        gr.Slider(0, 1000, label='Seed', default=42),\n    ],\n    outputs=\"image\",\n).launch()\n```\n\n\n", "heading1": "Step 3 \u2014 Creating a Gradio interface", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Generating 4 punks at a time is a good start, but maybe we'd like to control how many we want to make each time. Adding more inputs to our Gradio interface is as simple as adding another item to the `inputs` list that we pass to `gr.Interface`:\n\n```python\ngr.Interface(\n    predict,\n    inputs=[\n        gr.Slider(0, 1000, label='Seed', default=42),\n        gr.Slider(4, 64, label='Number of Punks', step=1, default=10), Adding another slider!\n    ],\n    outputs=\"image\",\n).launch()\n```\n\nThe new input will be passed to our `predict()` function, so we have to make some changes to that function to accept a new parameter:\n\n```python\ndef predict(seed, num_punks):\n    torch.manual_seed(seed)\n    z = torch.randn(num_punks, 100, 1, 1)\n    punks = model(z)\n    save_image(punks, \"punks.png\", normalize=True)\n    return 'punks.png'\n```\n\nWhen you relaunch your interface, you should see a second slider that'll let you control the number of punks!\n\n", "heading1": "Step 4 \u2014 Even more punks!", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Your Gradio app is pretty much good to go, but you can add a few extra things to really make it ready for the spotlight \u2728\n\nWe can add some examples that users can easily try out by adding this to the `gr.Interface`:\n\n```python\ngr.Interface(\n    ...\n    keep everything as it is, and then add\n    examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],\n).launch(cache_examples=True) cache_examples is optional\n```\n\nThe `examples` parameter takes a list of lists, where each item in the sublists is ordered in the same order that we've listed the `inputs`. So in our case, `[seed, num_punks]`. Give it a try!\n\nYou can also try adding a `title`, `description`, and `article` to the `gr.Interface`. Each of those parameters accepts a string, so try it out and see what happens \ud83d\udc40 `article` will also accept HTML, as [explored in a previous guide](/guides/key-features/descriptive-content)!\n\nWhen you're all done, you may end up with something like [this](https://nimaboscarino-cryptopunks.hf.space).\n\nFor reference, here is our full code:\n\n```python\nimport torch\nfrom torch import nn\nfrom huggingface_hub import hf_hub_download\nfrom torchvision.utils import save_image\nimport gradio as gr\n\nclass Generator(nn.Module):\n    Refer to the link below for explanations about nc, nz, and ngf\n    https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.htmlinputs\n    def __init__(self, nc=4, nz=100, ngf=64):\n        super(Generator, self).__init__()\n        self.network = nn.Sequential(\n            nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),\n            nn.BatchNorm2d(ngf * 4),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),\n            nn.BatchNorm2d(ngf * 2),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),\n            nn.BatchNorm2d(ngf),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n            nn.Tanh(),\n        )\n\n    def forward(self,", "heading1": "Step 5 - Polishing it up", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": " 4, 2, 0, bias=False),\n            nn.BatchNorm2d(ngf),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n            nn.Tanh(),\n        )\n\n    def forward(self, input):\n        output = self.network(input)\n        return output\n\nmodel = Generator()\nweights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')\nmodel.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) Use 'cuda' if you have a GPU available\n\ndef predict(seed, num_punks):\n    torch.manual_seed(seed)\n    z = torch.randn(num_punks, 100, 1, 1)\n    punks = model(z)\n    save_image(punks, \"punks.png\", normalize=True)\n    return 'punks.png'\n\ngr.Interface(\n    predict,\n    inputs=[\n        gr.Slider(0, 1000, label='Seed', default=42),\n        gr.Slider(4, 64, label='Number of Punks', step=1, default=10),\n    ],\n    outputs=\"image\",\n    examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],\n).launch(cache_examples=True)\n```\n\n---\n\nCongratulations! You've built out your very own GAN-powered CryptoPunks generator, with a fancy Gradio interface that makes it easy for anyone to use. Now you can [scour the Hub for more GANs](https://huggingface.co/models?other=gan) (or train your own) and continue making even more awesome demos \ud83e\udd17\n", "heading1": "Step 5 - Polishing it up", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Image classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from autonomous vehicles to medical imaging.\n\nSuch models are perfect to use with Gradio's _image_ input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in Python, and it will look like the demo on the bottom of the page.\n\nLet's get started!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). We will be using a pretrained image classification model, so you should also have `torch` installed.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "First, we will need an image classification model. For this tutorial, we will use a pretrained Resnet-18 model, as it is easily downloadable from [PyTorch Hub](https://pytorch.org/hub/pytorch_vision_resnet/). You can use a different pretrained model or train your own.\n\n```python\nimport torch\n\nmodel = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()\n```\n\nBecause we will be using the model for inference, we have called the `.eval()` method.\n\n", "heading1": "Step 1 \u2014 Setting up the Image Classification Model", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "Next, we will need to define a function that takes in the _user input_, which in this case is an image, and returns the prediction. The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities. We will load the class names from this [text file](https://git.io/JJkYN).\n\nIn the case of our pretrained model, it will look like this:\n\n```python\nimport requests\nfrom PIL import Image\nfrom torchvision import transforms\n\nDownload human-readable labels for ImageNet.\nresponse = requests.get(\"https://git.io/JJkYN\")\nlabels = response.text.split(\"\\n\")\n\ndef predict(inp):\n  inp = transforms.ToTensor()(inp).unsqueeze(0)\n  with torch.no_grad():\n    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}\n  return confidences\n```\n\nLet's break this down. The function takes one parameter:\n\n- `inp`: the input image as a `PIL` image\n\nThen, the function converts the image to a PIL Image and then eventually a PyTorch `tensor`, passes it through the model, and returns:\n\n- `confidences`: the predictions, as a dictionary whose keys are class labels and whose values are confidence probabilities\n\n", "heading1": "Step 2 \u2014 Defining a `predict` function", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "Now that we have our predictive function set up, we can create a Gradio Interface around it.\n\nIn this case, the input component is a drag-and-drop image component. To create this input, we use `Image(type=\"pil\")` which creates the component and handles the preprocessing to convert that to a `PIL` image.\n\nThe output component will be a `Label`, which displays the top labels in a nice form. Since we don't want to show all 1,000 class labels, we will customize it to show only the top 3 images by constructing it as `Label(num_top_classes=3)`.\n\nFinally, we'll add one more parameter, the `examples`, which allows us to prepopulate our interfaces with a few predefined examples. The code for Gradio looks like this:\n\n```python\nimport gradio as gr\n\ngr.Interface(fn=predict,\n             inputs=gr.Image(type=\"pil\"),\n             outputs=gr.Label(num_top_classes=3),\n             examples=[\"lion.jpg\", \"cheetah.jpg\"]).launch()\n```\n\nThis produces the following interface, which you can try right here in your browser (try uploading your own examples!):\n\n<gradio-app space=\"gradio/pytorch-image-classifier\">\n\n\n---\n\nAnd you're done! That's all the code you need to build a web demo for an image classifier. If you'd like to share with others, try setting `share=True` when you `launch()` the Interface!\n", "heading1": "Step 3 \u2014 Creating a Gradio Interface", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "This guide explains how you can use Gradio to plot geographical data on a map using the `gradio.Plot` component. The Gradio `Plot` component works with Matplotlib, Bokeh and Plotly. Plotly is what we will be working with in this guide. Plotly allows developers to easily create all sorts of maps with their geographical data. Take a look [here](https://plotly.com/python/maps/) for some examples.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "We will be using the New York City Airbnb dataset, which is hosted on kaggle [here](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data). I've uploaded it to the Hugging Face Hub as a dataset [here](https://huggingface.co/datasets/gradio/NYC-Airbnb-Open-Data) for easier use and download. Using this data we will plot Airbnb locations on a map output and allow filtering based on price and location. Below is the demo that we will be building. \u26a1\ufe0f\n\n$demo_map_airbnb\n\n", "heading1": "Overview", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Let's start by loading the Airbnb NYC data from the Hugging Face Hub.\n\n```python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"gradio/NYC-Airbnb-Open-Data\", split=\"train\")\ndf = dataset.to_pandas()\n\ndef filter_map(min_price, max_price, boroughs):\n    new_df = df[(df['neighbourhood_group'].isin(boroughs)) &\n            (df['price'] > min_price) & (df['price'] < max_price)]\n    names = new_df[\"name\"].tolist()\n    prices = new_df[\"price\"].tolist()\n    text_list = [(names[i], prices[i]) for i in range(0, len(names))]\n```\n\nIn the code above, we first load the csv data into a pandas dataframe. Let's begin by defining a function that we will use as the prediction function for the gradio app. This function will accept the minimum price and maximum price range as well as the list of boroughs to filter the resulting map. We can use the passed in values (`min_price`, `max_price`, and list of `boroughs`) to filter the dataframe and create `new_df`. Next we will create `text_list` of the names and prices of each Airbnb to use as labels on the map.\n\n", "heading1": "Step 1 - Loading CSV data \ud83d\udcbe", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Plotly makes it easy to work with maps. Let's take a look below how we can create a map figure.\n\n```python\nimport plotly.graph_objects as go\n\nfig = go.Figure(go.Scattermapbox(\n            customdata=text_list,\n            lat=new_df['latitude'].tolist(),\n            lon=new_df['longitude'].tolist(),\n            mode='markers',\n            marker=go.scattermapbox.Marker(\n                size=6\n            ),\n            hoverinfo=\"text\",\n            hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}'\n        ))\n\nfig.update_layout(\n    mapbox_style=\"open-street-map\",\n    hovermode='closest',\n    mapbox=dict(\n        bearing=0,\n        center=go.layout.mapbox.Center(\n            lat=40.67,\n            lon=-73.90\n        ),\n        pitch=0,\n        zoom=9\n    ),\n)\n```\n\nAbove, we create a scatter plot on mapbox by passing it our list of latitudes and longitudes to plot markers. We also pass in our custom data of names and prices for additional info to appear on every marker we hover over. Next we use `update_layout` to specify other map settings such as zoom, and centering.\n\nMore info [here](https://plotly.com/python/scattermapbox/) on scatter plots using Mapbox and Plotly.\n\n", "heading1": "Step 2 - Map Figure \ud83c\udf10", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "We will use two `gr.Number` components and a `gr.CheckboxGroup` to allow users of our app to specify price ranges and borough locations. We will then use the `gr.Plot` component as an output for our Plotly + Mapbox map we created earlier.\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Column():\n        with gr.Row():\n            min_price = gr.Number(value=250, label=\"Minimum Price\")\n            max_price = gr.Number(value=1000, label=\"Maximum Price\")\n        boroughs = gr.CheckboxGroup(choices=[\"Queens\", \"Brooklyn\", \"Manhattan\", \"Bronx\", \"Staten Island\"], value=[\"Queens\", \"Brooklyn\"], label=\"Select Boroughs:\")\n        btn = gr.Button(value=\"Update Filter\")\n        map = gr.Plot()\n    demo.load(filter_map, [min_price, max_price, boroughs], map)\n    btn.click(filter_map, [min_price, max_price, boroughs], map)\n```\n\nWe layout these components using the `gr.Column` and `gr.Row` and we'll also add event triggers for when the demo first loads and when our \"Update Filter\" button is clicked in order to trigger the map to update with our new filters.\n\nThis is what the full demo code looks like:\n\n$code_map_airbnb\n\n", "heading1": "Step 3 - Gradio App \u26a1\ufe0f", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "If you run the code above, your app will start running locally.\nYou can even get a temporary shareable link by passing the `share=True` parameter to `launch`.\n\nBut what if you want to a permanent deployment solution?\nLet's deploy our Gradio app to the free HuggingFace Spaces platform.\n\nIf you haven't used Spaces before, follow the previous guide [here](/using_hugging_face_integrations).\n\n", "heading1": "Step 4 - Deployment \ud83e\udd17", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "And you're all done! That's all the code you need to build a map demo.\n\nHere's a link to the demo [Map demo](https://huggingface.co/spaces/gradio/map_airbnb) and [complete code](https://huggingface.co/spaces/gradio/map_airbnb/blob/main/run.py) (on Hugging Face Spaces)\n", "heading1": "Conclusion \ud83c\udf89", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Named-entity recognition (NER), also known as token classification or text tagging, is the task of taking a sentence and classifying every word (or \"token\") into different categories, such as names of people or names of locations, or different parts of speech.\n\nFor example, given the sentence:\n\n> Does Chicago have any Pakistani restaurants?\n\nA named-entity recognition algorithm may identify:\n\n- \"Chicago\" as a **location**\n- \"Pakistani\" as an **ethnicity**\n\nand so on.\n\nUsing `gradio` (specifically the `HighlightedText` component), you can easily build a web demo of your NER model and share that with the rest of your team.\n\nHere is an example of a demo that you'll be able to build:\n\n$demo_ner_pipeline\n\nThis tutorial will show how to take a pretrained NER model and deploy it with a Gradio interface. We will show two different ways to use the `HighlightedText` component -- depending on your NER model, either of these two ways may be easier to learn!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). You will also need a pretrained named-entity recognition model. You can use your own, while in this tutorial, we will use one from the `transformers` library.\n\nApproach 1: List of Entity Dictionaries\n\nMany named-entity recognition models output a list of dictionaries. Each dictionary consists of an _entity_, a \"start\" index, and an \"end\" index. This is, for example, how NER models in the `transformers` library operate:\n\n```py\nfrom transformers import pipeline\nner_pipeline = pipeline(\"ner\")\nner_pipeline(\"Does Chicago have any Pakistani restaurants\")\n```\n\nOutput:\n\n```bash\n[{'entity': 'I-LOC',\n  'score': 0.9988978,\n  'index': 2,\n  'word': 'Chicago',\n  'start': 5,\n  'end': 12},\n {'entity': 'I-MISC',\n  'score': 0.9958592,\n  'index': 5,\n  'word': 'Pakistani',\n  'start': 22,\n  'end': 31}]\n```\n\nIf you have such a model, it is very easy to hook it up to Gradio's `HighlightedText` component. All you need to do is pass in this ", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/named-entity-recognition", "source_page_title": "Other Tutorials - Named Entity Recognition Guide"}, {"text": "index': 5,\n  'word': 'Pakistani',\n  'start': 22,\n  'end': 31}]\n```\n\nIf you have such a model, it is very easy to hook it up to Gradio's `HighlightedText` component. All you need to do is pass in this **list of entities**, along with the **original text** to the model, together as dictionary, with the keys being `\"entities\"` and `\"text\"` respectively.\n\nHere is a complete example:\n\n$code_ner_pipeline\n$demo_ner_pipeline\n\nApproach 2: List of Tuples\n\nAn alternative way to pass data into the `HighlightedText` component is a list of tuples. The first element of each tuple should be the word or words that are being classified into a particular entity. The second element should be the entity label (or `None` if they should be unlabeled). The `HighlightedText` component automatically strings together the words and labels to display the entities.\n\nIn some cases, this can be easier than the first approach. Here is a demo showing this approach using Spacy's parts-of-speech tagger:\n\n$code_text_analysis\n$demo_text_analysis\n\n---\n\nAnd you're done! That's all you need to know to build a web-based GUI for your NER model.\n\nFun tip: you can share your NER demo instantly with others simply by setting `share=True` in `launch()`.\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/named-entity-recognition", "source_page_title": "Other Tutorials - Named Entity Recognition Guide"}, {"text": "The Hugging Face Hub is a central platform that has hundreds of thousands of [models](https://huggingface.co/models), [datasets](https://huggingface.co/datasets) and [demos](https://huggingface.co/spaces) (also known as Spaces). \n\nGradio has multiple features that make it extremely easy to leverage existing models and Spaces on the Hub. This guide walks through these features.\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Hugging Face has a service called [Serverless Inference Endpoints](https://huggingface.co/docs/api-inference/index), which allows you to send HTTP requests to models on the Hub. The API includes a generous free tier, and you can switch to [dedicated Inference Endpoints](https://huggingface.co/inference-endpoints/dedicated) when you want to use it in production. Gradio integrates directly with Serverless Inference Endpoints so that you can create a demo simply by specifying a model's name (e.g. `Helsinki-NLP/opus-mt-en-es`), like this:\n\n```python\nimport gradio as gr\n\ndemo = gr.load(\"Helsinki-NLP/opus-mt-en-es\", src=\"models\")\n\ndemo.launch()\n```\n\nFor any Hugging Face model supported in Inference Endpoints, Gradio automatically infers the expected input and output and make the underlying server calls, so you don't have to worry about defining the prediction function. \n\nNotice that we just put specify the model name and state that the `src` should be `models` (Hugging Face's Model Hub). There is no need to install any dependencies (except `gradio`) since you are not loading the model on your computer.\n\nYou might notice that the first inference takes a little bit longer. This happens since the Inference Endpoints is loading the model in the server. You get some benefits afterward:\n\n- The inference will be much faster.\n- The server caches your requests.\n- You get built-in automatic scaling.\n\n", "heading1": "Demos with the Hugging Face Inference Endpoints", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "[Hugging Face Spaces](https://hf.co/spaces) allows anyone to host their Gradio demos freely, and uploading your Gradio demos take a couple of minutes. You can head to [hf.co/new-space](https://huggingface.co/new-space), select the Gradio SDK, create an `app.py` file, and voila! You have a demo you can share with anyone else. To learn more, read [this guide how to host on Hugging Face Spaces using the website](https://huggingface.co/blog/gradio-spaces).\n\nAlternatively, you can create a Space programmatically, making use of the [huggingface_hub client library](https://huggingface.co/docs/huggingface_hub/index) library. Here's an example:\n\n```python\nfrom huggingface_hub import (\n    create_repo,\n    get_full_repo_name,\n    upload_file,\n)\ncreate_repo(name=target_space_name, token=hf_token, repo_type=\"space\", space_sdk=\"gradio\")\nrepo_name = get_full_repo_name(model_id=target_space_name, token=hf_token)\nfile_url = upload_file(\n    path_or_fileobj=\"file.txt\",\n    path_in_repo=\"app.py\",\n    repo_id=repo_name,\n    repo_type=\"space\",\n    token=hf_token,\n)\n```\n\nHere, `create_repo` creates a gradio repo with the target name under a specific account using that account's Write Token. `repo_name` gets the full repo name of the related repo. Finally `upload_file` uploads a file inside the repo with the name `app.py`.\n\n\n", "heading1": "Hosting your Gradio demos on Spaces", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "You can also use and remix existing Gradio demos on Hugging Face Spaces. For example, you could take two existing Gradio demos on Spaces and put them as separate tabs and create a new demo. You can run this new demo locally, or upload it to Spaces, allowing endless possibilities to remix and create new demos!\n\nHere's an example that does exactly that:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n  with gr.Tab(\"Translate to Spanish\"):\n    gr.load(\"gradio/en2es\", src=\"spaces\")\n  with gr.Tab(\"Translate to French\"):\n    gr.load(\"abidlabs/en2fr\", src=\"spaces\")\n\ndemo.launch()\n```\n\nNotice that we use `gr.load()`, the same method we used to load models using Inference Endpoints. However, here we specify that the `src` is `spaces` (Hugging Face Spaces). \n\nNote: loading a Space in this way may result in slight differences from the original Space. In particular, any attributes that apply to the entire Blocks, such as the theme or custom CSS/JS, will not be loaded. You can copy these properties from the Space you are loading into your own `Blocks` object. \n\n", "heading1": "Loading demos from Spaces", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Hugging Face's popular `transformers` library has a very easy-to-use abstraction, [`pipeline()`](https://huggingface.co/docs/transformers/v4.16.2/en/main_classes/pipelinestransformers.pipeline) that handles most of the complex code to offer a simple API for common tasks. By specifying the task and an (optional) model, you can build a demo around an existing model with few lines of Python:\n\n```python\nimport gradio as gr\n\nfrom transformers import pipeline\n\npipe = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-es\")\n\ndef predict(text):\n  return pipe(text)[0][\"translation_text\"]\n\ndemo = gr.Interface(\n  fn=predict,\n  inputs='text',\n  outputs='text',\n)\n\ndemo.launch()\n```\n\nBut `gradio` actually makes it even easier to convert a `pipeline` to a demo, simply by using the `gradio.Interface.from_pipeline` methods, which skips the need to specify the input and output components:\n\n```python\nfrom transformers import pipeline\nimport gradio as gr\n\npipe = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-es\")\n\ndemo = gr.Interface.from_pipeline(pipe)\ndemo.launch()\n```\n\nThe previous code produces the following interface, which you can try right here in your browser:\n\n<gradio-app space=\"gradio/en2es\"></gradio-app>\n\n\n", "heading1": "Demos with the `Pipeline` in `transformers`", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "That's it! Let's recap the various ways Gradio and Hugging Face work together:\n\n1. You can build a demo around Inference Endpoints without having to load the model, by using `gr.load()`.\n2. You host your Gradio demo on Hugging Face Spaces, either using the GUI or entirely in Python.\n3. You can load demos from Hugging Face Spaces to remix and create new Gradio demos using `gr.load()`.\n4. You can convert a `transformers` pipeline into a Gradio demo using `from_pipeline()`.\n\n\ud83e\udd17\n", "heading1": "Recap", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Let's go through a simple example to understand how to containerize a Gradio app using Docker.\n\nStep 1: Create Your Gradio App\n\nFirst, we need a simple Gradio app. Let's create a Python file named `app.py` with the following content:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n    return f\"Hello {name}!\"\n\niface = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\").launch()\n```\n\nThis app creates a simple interface that greets the user by name.\n\nStep 2: Create a Dockerfile\n\nNext, we'll create a Dockerfile to specify how our app should be built and run in a Docker container. Create a file named `Dockerfile` in the same directory as your app with the following content:\n\n```dockerfile\nFROM python:3.10-slim\n\nWORKDIR /usr/src/app\nCOPY . .\nRUN pip install --no-cache-dir gradio\nEXPOSE 7860\nENV GRADIO_SERVER_NAME=\"0.0.0.0\"\n\nCMD [\"python\", \"app.py\"]\n```\n\nThis Dockerfile performs the following steps:\n- Starts from a Python 3.10 slim image.\n- Sets the working directory and copies the app into the container.\n- Installs Gradio (you should install all other requirements as well).\n- Exposes port 7860 (Gradio's default port).\n- Sets the `GRADIO_SERVER_NAME` environment variable to ensure Gradio listens on all network interfaces.\n- Specifies the command to run the app.\n\nStep 3: Build and Run Your Docker Container\n\nWith the Dockerfile in place, you can build and run your container:\n\n```bash\ndocker build -t gradio-app .\ndocker run -p 7860:7860 gradio-app\n```\n\nYour Gradio app should now be accessible at `http://localhost:7860`.\n\n", "heading1": "How to Dockerize a Gradio App", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-docker", "source_page_title": "Other Tutorials - Deploying Gradio With Docker Guide"}, {"text": "When running Gradio applications in Docker, there are a few important things to keep in mind:\n\nRunning the Gradio app on `\"0.0.0.0\"` and exposing port 7860\n\nIn the Docker environment, setting `GRADIO_SERVER_NAME=\"0.0.0.0\"` as an environment variable (or directly in your Gradio app's `launch()` function) is crucial for allowing connections from outside the container. And the `EXPOSE 7860` directive in the Dockerfile tells Docker to expose Gradio's default port on the container to enable external access to the Gradio app. \n\nEnable Stickiness for Multiple Replicas\n\nWhen deploying Gradio apps with multiple replicas, such as on AWS ECS, it's important to enable stickiness with `sessionAffinity: ClientIP`. This ensures that all requests from the same user are routed to the same instance. This is important because Gradio's communication protocol requires multiple separate connections from the frontend to the backend in order for events to be processed correctly. (If you use Terraform, you'll want to add a [stickiness block](https://registry.terraform.io/providers/hashicorp/aws/3.14.1/docs/resources/lb_target_groupstickiness) into your target group definition.)\n\nDeploying Behind a Proxy\n\nIf you're deploying your Gradio app behind a proxy, like Nginx, it's essential to configure the proxy correctly. Gradio provides a [Guide that walks through the necessary steps](https://www.gradio.app/guides/running-gradio-on-your-web-server-with-nginx). This setup ensures your app is accessible and performs well in production environments.\n\n", "heading1": "Important Considerations", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-docker", "source_page_title": "Other Tutorials - Deploying Gradio With Docker Guide"}, {"text": "Let\u2019s start with a simple example of integrating a C++ program into a Gradio app. Suppose we have the following C++ program that adds two numbers:\n\n```cpp\n// add.cpp\ninclude <iostream>\n\nint main() {\n    double a, b;\n    std::cin >> a >> b;\n    std::cout << a + b << std::endl;\n    return 0;\n}\n```\n\nThis program reads two numbers from standard input, adds them, and outputs the result.\n\nWe can build a Gradio interface around this C++ program using Python's `subprocess` module. Here\u2019s the corresponding Python code:\n\n```python\nimport gradio as gr\nimport subprocess\n\ndef add_numbers(a, b):\n    process = subprocess.Popen(\n        ['./add'], \n        stdin=subprocess.PIPE, \n        stdout=subprocess.PIPE, \n        stderr=subprocess.PIPE\n    )\n    output, error = process.communicate(input=f\"{a} {b}\\n\".encode())\n    \n    if error:\n        return f\"Error: {error.decode()}\"\n    return float(output.decode().strip())\n\ndemo = gr.Interface(\n    fn=add_numbers, \n    inputs=[gr.Number(label=\"Number 1\"), gr.Number(label=\"Number 2\")], \n    outputs=gr.Textbox(label=\"Result\")\n)\n\ndemo.launch()\n```\n\nHere, `subprocess.Popen` is used to execute the compiled C++ program (`add`), pass the input values, and capture the output. You can compile the C++ program by running:\n\n```bash\ng++ -o add add.cpp\n```\n\nThis example shows how easy it is to call C++ from Python using `subprocess` and build a Gradio interface around it.\n\n", "heading1": "Using Gradio with C++", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Now, let\u2019s move to another example: calling a Rust program to apply a sepia filter to an image. The Rust code could look something like this:\n\n```rust\n// sepia.rs\nextern crate image;\n\nuse image::{GenericImageView, ImageBuffer, Rgba};\n\nfn sepia_filter(input: &str, output: &str) {\n    let img = image::open(input).unwrap();\n    let (width, height) = img.dimensions();\n    let mut img_buf = ImageBuffer::new(width, height);\n\n    for (x, y, pixel) in img.pixels() {\n        let (r, g, b, a) = (pixel[0] as f32, pixel[1] as f32, pixel[2] as f32, pixel[3]);\n        let tr = (0.393 * r + 0.769 * g + 0.189 * b).min(255.0);\n        let tg = (0.349 * r + 0.686 * g + 0.168 * b).min(255.0);\n        let tb = (0.272 * r + 0.534 * g + 0.131 * b).min(255.0);\n        img_buf.put_pixel(x, y, Rgba([tr as u8, tg as u8, tb as u8, a]));\n    }\n\n    img_buf.save(output).unwrap();\n}\n\nfn main() {\n    let args: Vec<String> = std::env::args().collect();\n    if args.len() != 3 {\n        eprintln!(\"Usage: sepia <input_file> <output_file>\");\n        return;\n    }\n    sepia_filter(&args[1], &args[2]);\n}\n```\n\nThis Rust program applies a sepia filter to an image. It takes two command-line arguments: the input image path and the output image path. You can compile this program using:\n\n```bash\ncargo build --release\n```\n\nNow, we can call this Rust program from Python and use Gradio to build the interface:\n\n```python\nimport gradio as gr\nimport subprocess\n\ndef apply_sepia(input_path):\n    output_path = \"output.png\"\n    \n    process = subprocess.Popen(\n        ['./target/release/sepia', input_path, output_path], \n        stdout=subprocess.PIPE, \n        stderr=subprocess.PIPE\n    )\n    process.wait()\n    \n    return output_path\n\ndemo = gr.Interface(\n    fn=apply_sepia, \n    inputs=gr.Image(type=\"filepath\", label=\"Input Image\"), \n    outputs=gr.Image(label=\"Sepia Image\")\n)\n\ndemo.launch()\n```\n\nHere, when a user uploads an image and clicks submit, Gradio calls the Rust binary (`sepia`) to process the image, and re", "heading1": "Using Gradio with Rust", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "nput Image\"), \n    outputs=gr.Image(label=\"Sepia Image\")\n)\n\ndemo.launch()\n```\n\nHere, when a user uploads an image and clicks submit, Gradio calls the Rust binary (`sepia`) to process the image, and returns the sepia-filtered output to Gradio.\n\nThis setup showcases how you can integrate performance-critical or specialized code written in Rust into a Gradio interface.\n\n", "heading1": "Using Gradio with Rust", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Integrating Gradio with R is particularly straightforward thanks to the `reticulate` package, which allows you to run Python code directly in R. Let\u2019s walk through an example of using Gradio in R. \n\n**Installation**\n\nFirst, you need to install the `reticulate` package in R:\n\n```r\ninstall.packages(\"reticulate\")\n```\n\n\nOnce installed, you can use the package to run Gradio directly from within an R script.\n\n\n```r\nlibrary(reticulate)\n\npy_install(\"gradio\", pip = TRUE)\n\ngr <- import(\"gradio\") import gradio as gr\n```\n\n**Building a Gradio Application**\n\nWith gradio installed and imported, we now have access to gradio's app building methods. Let's build a simple app for an R function that returns a greeting\n\n```r\ngreeting <- \\(name) paste(\"Hello\", name)\n\napp <- gr$Interface(\n  fn = greeting,\n  inputs = gr$Text(label = \"Name\"),\n  outputs = gr$Text(label = \"Greeting\"),\n  title = \"Hello! &128515 &128075\"\n)\n\napp$launch(server_name = \"localhost\", \n           server_port = as.integer(3000))\n```\n\nCredit to [@IfeanyiIdiaye](https://github.com/Ifeanyi55) for contributing this section. You can see more examples [here](https://github.com/Ifeanyi55/Gradio-in-R/tree/main/Code), including using Gradio Blocks to build a machine learning application in R.\n", "heading1": "Using Gradio with R (via `reticulate`)", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Image classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from facial recognition to manufacturing quality control.\n\nState-of-the-art image classifiers are based on the _transformers_ architectures, originally popularized for NLP tasks. Such architectures are typically called vision transformers (ViT). Such models are perfect to use with Gradio's _image_ input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in a **single line of Python**, and it will look like the demo on the bottom of the page.\n\nLet's get started!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "First, we will need an image classification model. For this tutorial, we will use a model from the [Hugging Face Model Hub](https://huggingface.co/models?pipeline_tag=image-classification). The Hub contains thousands of models covering dozens of different machine learning tasks.\n\nExpand the Tasks category on the left sidebar and select \"Image Classification\" as our task of interest. You will then see all of the models on the Hub that are designed to classify images.\n\nAt the time of writing, the most popular one is `google/vit-base-patch16-224`, which has been trained on ImageNet images at a resolution of 224x224 pixels. We will use this model for our demo.\n\n", "heading1": "Step 1 \u2014 Choosing a Vision Image Classification Model", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "When using a model from the Hugging Face Hub, we do not need to define the input or output components for the demo. Similarly, we do not need to be concerned with the details of preprocessing or postprocessing.\nAll of these are automatically inferred from the model tags.\n\nBesides the import statement, it only takes a single line of Python to load and launch the demo.\n\nWe use the `gr.Interface.load()` method and pass in the path to the model including the `huggingface/` to designate that it is from the Hugging Face Hub.\n\n```python\nimport gradio as gr\n\ngr.Interface.load(\n             \"huggingface/google/vit-base-patch16-224\",\n             examples=[\"alligator.jpg\", \"laptop.jpg\"]).launch()\n```\n\nNotice that we have added one more parameter, the `examples`, which allows us to prepopulate our interfaces with a few predefined examples.\n\nThis produces the following interface, which you can try right here in your browser. When you input an image, it is automatically preprocessed and sent to the Hugging Face Hub API, where it is passed through the model and returned as a human-interpretable prediction. Try uploading your own image!\n\n<gradio-app space=\"gradio/vision-transformer\">\n\n---\n\nAnd you're done! In one line of code, you have built a web demo for an image classifier. If you'd like to share with others, try setting `share=True` when you `launch()` the Interface!\n", "heading1": "Step 2 \u2014 Loading the Vision Transformer Model with Gradio", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "To use Gradio with BigQuery, you will need to obtain your BigQuery credentials and use them with the [BigQuery Python client](https://pypi.org/project/google-cloud-bigquery/). If you already have BigQuery credentials (as a `.json` file), you can skip this section. If not, you can do this for free in just a couple of minutes.\n\n1. First, log in to your Google Cloud account and go to the Google Cloud Console (https://console.cloud.google.com/)\n\n2. In the Cloud Console, click on the hamburger menu in the top-left corner and select \"APIs & Services\" from the menu. If you do not have an existing project, you will need to create one.\n\n3. Then, click the \"+ Enabled APIs & services\" button, which allows you to enable specific services for your project. Search for \"BigQuery API\", click on it, and click the \"Enable\" button. If you see the \"Manage\" button, then the BigQuery is already enabled, and you're all set.\n\n4. In the APIs & Services menu, click on the \"Credentials\" tab and then click on the \"Create credentials\" button.\n\n5. In the \"Create credentials\" dialog, select \"Service account key\" as the type of credentials to create, and give it a name. Also grant the service account permissions by giving it a role such as \"BigQuery User\", which will allow you to run queries.\n\n6. After selecting the service account, select the \"JSON\" key type and then click on the \"Create\" button. This will download the JSON key file containing your credentials to your computer. It will look something like this:\n\n```json\n{\n\t\"type\": \"service_account\",\n\t\"project_id\": \"your project\",\n\t\"private_key_id\": \"your private key id\",\n\t\"private_key\": \"private key\",\n\t\"client_email\": \"email\",\n\t\"client_id\": \"client id\",\n\t\"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n\t\"token_uri\": \"https://accounts.google.com/o/oauth2/token\",\n\t\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n\t\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/email_id\"\n}\n```\n\n", "heading1": "Setting up your BigQuery Credentials", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-bigquery-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Bigquery Data Guide"}, {"text": "Once you have the credentials, you will need to use the BigQuery Python client to authenticate using your credentials. To do this, you will need to install the BigQuery Python client by running the following command in the terminal:\n\n```bash\npip install google-cloud-bigquery[pandas]\n```\n\nYou'll notice that we've installed the pandas add-on, which will be helpful for processing the BigQuery dataset as a pandas dataframe. Once the client is installed, you can authenticate using your credentials by running the following code:\n\n```py\nfrom google.cloud import bigquery\n\nclient = bigquery.Client.from_service_account_json(\"path/to/key.json\")\n```\n\nWith your credentials authenticated, you can now use the BigQuery Python client to interact with your BigQuery datasets.\n\nHere is an example of a function which queries the `covid19_nyt.us_counties` dataset in BigQuery to show the top 20 counties with the most confirmed cases as of the current day:\n\n```py\nimport numpy as np\n\nQUERY = (\n    'SELECT * FROM `bigquery-public-data.covid19_nyt.us_counties` '\n    'ORDER BY date DESC,confirmed_cases DESC '\n    'LIMIT 20')\n\ndef run_query():\n    query_job = client.query(QUERY)\n    query_result = query_job.result()\n    df = query_result.to_dataframe()\n    Select a subset of columns\n    df = df[[\"confirmed_cases\", \"deaths\", \"county\", \"state_name\"]]\n    Convert numeric columns to standard numpy types\n    df = df.astype({\"deaths\": np.int64, \"confirmed_cases\": np.int64})\n    return df\n```\n\n", "heading1": "Using the BigQuery Client", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-bigquery-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Bigquery Data Guide"}, {"text": "Once you have a function to query the data, you can use the `gr.DataFrame` component from the Gradio library to display the results in a tabular format. This is a useful way to inspect the data and make sure that it has been queried correctly.\n\nHere is an example of how to use the `gr.DataFrame` component to display the results. By passing in the `run_query` function to `gr.DataFrame`, we instruct Gradio to run the function as soon as the page loads and show the results. In addition, you also pass in the keyword `every` to tell the dashboard to refresh every hour (60\\*60 seconds).\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.DataFrame(run_query, every=gr.Timer(60*60))\n\ndemo.launch()\n```\n\nPerhaps you'd like to add a visualization to our dashboard. You can use the `gr.ScatterPlot()` component to visualize the data in a scatter plot. This allows you to see the relationship between different variables such as case count and case deaths in the dataset and can be useful for exploring the data and gaining insights. Again, we can do this in real-time\nby passing in the `every` parameter.\n\nHere is a complete example showing how to use the `gr.ScatterPlot` to visualize in addition to displaying data with the `gr.DataFrame`\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"\ud83d\udc89 Covid Dashboard (Updated Hourly)\")\n    with gr.Row():\n        gr.DataFrame(run_query, every=gr.Timer(60*60))\n        gr.ScatterPlot(run_query, every=gr.Timer(60*60), x=\"confirmed_cases\",\n                        y=\"deaths\", tooltip=\"county\", width=500, height=500)\n\ndemo.queue().launch()  Run the demo with queuing enabled\n```\n", "heading1": "Building the Real-Time Dashboard", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-bigquery-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Bigquery Data Guide"}, {"text": "Data visualization is a crucial aspect of data analysis and machine learning. The Gradio `DataFrame` component is a popular way to display tabular data within a web application. \n\nBut what if you want to stylize the table of data? What if you want to add background colors, partially highlight cells, or change the display precision of numbers? This Guide is for you!\n\n\n\nLet's dive in!\n\n**Prerequisites**: We'll be using the `gradio.Blocks` class in our examples.\nYou can [read the Guide to Blocks first](https://gradio.app/blocks-and-event-listeners) if you are not already familiar with it. Also please make sure you are using the **latest version** version of Gradio: `pip install --upgrade gradio`.\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "The Gradio `DataFrame` component now supports values of the type `Styler` from the `pandas` class. This allows us to reuse the rich existing API and documentation of the `Styler` class instead of inventing a new style format on our own. Here's a complete example of how it looks:\n\n```python\nimport pandas as pd \nimport gradio as gr\n\nCreating a sample dataframe\ndf = pd.DataFrame({\n    \"A\" : [14, 4, 5, 4, 1], \n    \"B\" : [5, 2, 54, 3, 2], \n    \"C\" : [20, 20, 7, 3, 8], \n    \"D\" : [14, 3, 6, 2, 6], \n    \"E\" : [23, 45, 64, 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n    gr.DataFrame(styler)\n    \ndemo.launch()\n```\n\nThe Styler class can be used to apply conditional formatting and styling to dataframes, making them more visually appealing and interpretable. You can highlight certain values, apply gradients, or even use custom CSS to style the DataFrame. The Styler object is applied to a DataFrame and it returns a new object with the relevant styling properties, which can then be previewed directly, or rendered dynamically in a Gradio interface.\n\nTo read more about the Styler object, read the official `pandas` documentation at: https://pandas.pydata.org/docs/user_guide/style.html\n\nBelow, we'll explore a few examples:\n\nHighlighting Cells\n\nOk, so let's revisit the previous example. We start by creating a `pd.DataFrame` object and then highlight the highest value in each row with a light green color:\n\n```python\nimport pandas as pd \n\nCreating a sample dataframe\ndf = pd.DataFrame({\n    \"A\" : [14, 4, 5, 4, 1], \n    \"B\" : [5, 2, 54, 3, 2], \n    \"C\" : [20, 20, 7, 3, 8], \n    \"D\" : [14, 3, 6, 2, 6], \n    \"E\" : [23, 45, 64, 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n```\n\nNow, we simply pass this object into the Gradio `DataFra", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": ", 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n```\n\nNow, we simply pass this object into the Gradio `DataFrame` and we can visualize our colorful table of data in 4 lines of python:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Dataframe(styler)\n    \ndemo.launch()\n```\n\nHere's how it looks:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-highlight.png)\n\nFont Colors\n\nApart from highlighting cells, you might want to color specific text within the cells. Here's how you can change text colors for certain columns:\n\n```python\nimport pandas as pd \nimport gradio as gr\n\nCreating a sample dataframe\ndf = pd.DataFrame({\n    \"A\" : [14, 4, 5, 4, 1], \n    \"B\" : [5, 2, 54, 3, 2], \n    \"C\" : [20, 20, 7, 3, 8], \n    \"D\" : [14, 3, 6, 2, 6], \n    \"E\" : [23, 45, 64, 32, 23]\n}) \n\nFunction to apply text color\ndef highlight_cols(x): \n    df = x.copy() \n    df.loc[:, :] = 'color: purple'\n    df[['B', 'C', 'E']] = 'color: green'\n    return df \n\nApplying the style function\ns = df.style.apply(highlight_cols, axis = None)\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n    gr.DataFrame(s)\n    \ndemo.launch()\n```\n\nIn this script, we define a custom function highlight_cols that changes the text color to purple for all cells, but overrides this for columns B, C, and E with green. Here's how it looks:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-color.png)\n\nDisplay Precision \n\nSometimes, the data you are dealing with might have long floating numbers, and you may want to display only a fixed number of decimals for simplicity. The pandas Styler object allows you to format the precision of numbers displayed. Here's how you can do this:\n\n```python\nimport pandas as pd\nimport gradio as gr\n\nCreating a sample dataframe with floating numbers\ndf = pd.DataFrame({\n    \"A\" : [14.12345, 4.", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "on of numbers displayed. Here's how you can do this:\n\n```python\nimport pandas as pd\nimport gradio as gr\n\nCreating a sample dataframe with floating numbers\ndf = pd.DataFrame({\n    \"A\" : [14.12345, 4.23456, 5.34567, 4.45678, 1.56789], \n    \"B\" : [5.67891, 2.78912, 54.89123, 3.91234, 2.12345], \n    ... other columns\n}) \n\nSetting the precision of numbers to 2 decimal places\ns = df.style.format(\"{:.2f}\")\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n    gr.DataFrame(s)\n    \ndemo.launch()\n```\n\nIn this script, the format method of the Styler object is used to set the precision of numbers to two decimal places. Much cleaner now:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-precision.png)\n\n\n\n", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "So far, we've been restricting ourselves to styling that is supported by the Pandas `Styler` class. But what if you want to create custom styles like partially highlighting cells based on their values:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/dataframe_custom_styling.png)\n\n\nThis isn't possible with `Styler`, but you can do this by creating your own **`styling`** array, which is a 2D array the same size and shape as your data. Each element in this list should be a CSS style string (e.g. `\"background-color: green\"`) that applies to the `<td>` element containing the cell value (or an empty string if no custom CSS should be applied). Similarly, you can create a **`display_value`** array which controls the value that is displayed in each cell (which can be different the underlying value which is the one that is used for searching/sorting).\n\nHere's the complete code for how to can use custom styling with `gr.Dataframe` as in the screenshot above:\n\n$code_dataframe_custom_styling\n\n\n", "heading1": "Custom Styling", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "One thing to keep in mind is that the gradio `DataFrame` component only accepts custom styling objects when it is non-interactive (i.e. in \"static\" mode). If the `DataFrame` component is interactive, then the styling information is ignored and instead the raw table values are shown instead. \n\nThe `DataFrame` component is by default non-interactive, unless it is used as an input to an event. In which case, you can force the component to be non-interactive by setting the `interactive` prop like this:\n\n```python\nc = gr.DataFrame(styler, interactive=False)\n```\n\n", "heading1": "Note about Interactivity", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "This is just a taste of what's possible using the `gradio.DataFrame` component with the `Styler` class from `pandas`. Try it out and let us know what you think!", "heading1": "Conclusion \ud83c\udf89", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "First of all, we need some data to visualize. Following this [excellent guide](https://supabase.com/blog/loading-data-supabase-python), we'll create fake commerce data and put it in Supabase.\n\n1\\. Start by creating a new project in Supabase. Once you're logged in, click the \"New Project\" button\n\n2\\. Give your project a name and database password. You can also choose a pricing plan (for our purposes, the Free Tier is sufficient!)\n\n3\\. You'll be presented with your API keys while the database spins up (can take up to 2 minutes).\n\n4\\. Click on \"Table Editor\" (the table icon) in the left pane to create a new table. We'll create a single table called `Product`, with the following schema:\n\n<center>\n<table>\n<tr><td>product_id</td><td>int8</td></tr>\n<tr><td>inventory_count</td><td>int8</td></tr>\n<tr><td>price</td><td>float8</td></tr>\n<tr><td>product_name</td><td>varchar</td></tr>\n</table>\n</center>\n\n5\\. Click Save to save the table schema.\n\nOur table is now ready!\n\n", "heading1": "Create a table in Supabase", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-supabase-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Supabase Data Guide"}, {"text": "The next step is to write data to a Supabase dataset. We will use the Supabase Python library to do this.\n\n6\\. Install `supabase` by running the following command in your terminal:\n\n```bash\npip install supabase\n```\n\n7\\. Get your project URL and API key. Click the Settings (gear icon) on the left pane and click 'API'. The URL is listed in the Project URL box, while the API key is listed in Project API keys (with the tags `service_role`, `secret`)\n\n8\\. Now, run the following Python script to write some fake data to the table (note you have to put the values of `SUPABASE_URL` and `SUPABASE_SECRET_KEY` from step 7):\n\n```python\nimport supabase\n\nInitialize the Supabase client\nclient = supabase.create_client('SUPABASE_URL', 'SUPABASE_SECRET_KEY')\n\nDefine the data to write\nimport random\n\nmain_list = []\nfor i in range(10):\n    value = {'product_id': i,\n             'product_name': f\"Item {i}\",\n             'inventory_count': random.randint(1, 100),\n             'price': random.random()*100\n            }\n    main_list.append(value)\n\nWrite the data to the table\ndata = client.table('Product').insert(main_list).execute()\n```\n\nReturn to your Supabase dashboard and refresh the page, you should now see 10 rows populated in the `Product` table!\n\n", "heading1": "Write data to Supabase", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-supabase-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Supabase Data Guide"}, {"text": "Finally, we will read the data from the Supabase dataset using the same `supabase` Python library and create a realtime dashboard using `gradio`.\n\nNote: We repeat certain steps in this section (like creating the Supabase client) in case you did not go through the previous sections. As described in Step 7, you will need the project URL and API Key for your database.\n\n9\\. Write a function that loads the data from the `Product` table and returns it as a pandas Dataframe:\n\n```python\nimport supabase\nimport pandas as pd\n\nclient = supabase.create_client('SUPABASE_URL', 'SUPABASE_SECRET_KEY')\n\ndef read_data():\n    response = client.table('Product').select(\"*\").execute()\n    df = pd.DataFrame(response.data)\n    return df\n```\n\n10\\. Create a small Gradio Dashboard with 2 Barplots that plots the prices and inventories of all of the items every minute and updates in real-time:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as dashboard:\n    with gr.Row():\n        gr.BarPlot(read_data, x=\"product_id\", y=\"price\", title=\"Prices\", every=gr.Timer(60))\n        gr.BarPlot(read_data, x=\"product_id\", y=\"inventory_count\", title=\"Inventory\", every=gr.Timer(60))\n\ndashboard.queue().launch()\n```\n\nNotice that by passing in a function to `gr.BarPlot()`, we have the BarPlot query the database as soon as the web app loads (and then again every 60 seconds because of the `every` parameter). Your final dashboard should look something like this:\n\n<gradio-app space=\"gradio/supabase\"></gradio-app>\n\n", "heading1": "Visualize the Data in a Real-Time Gradio Dashboard", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-supabase-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Supabase Data Guide"}, {"text": "That's it! In this tutorial, you learned how to write data to a Supabase dataset, and then read that data and plot the results as bar plots. If you update the data in the Supabase database, you'll notice that the Gradio dashboard will update within a minute.\n\nTry adding more plots and visualizations to this example (or with a different dataset) to build a more complex dashboard!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-supabase-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Supabase Data Guide"}, {"text": "A virtual environment in Python is a self-contained directory that holds a Python installation for a particular version of Python, along with a number of additional packages. This environment is isolated from the main Python installation and other virtual environments. Each environment can have its own independent set of installed Python packages, which allows you to maintain different versions of libraries for different projects without conflicts.\n\n\nUsing virtual environments ensures that you can work on multiple Python projects on the same machine without any conflicts. This is particularly useful when different projects require different versions of the same library. It also simplifies dependency management and enhances reproducibility, as you can easily share the requirements of your project with others.\n\n\n", "heading1": "Virtual Environments", "source_page_url": "https://gradio.app/guides/installing-gradio-in-a-virtual-environment", "source_page_title": "Other Tutorials - Installing Gradio In A Virtual Environment Guide"}, {"text": "To install Gradio on a Windows system in a virtual environment, follow these steps:\n\n1. **Install Python**: Ensure you have Python 3.10 or higher installed. You can download it from [python.org](https://www.python.org/). You can verify the installation by running `python --version` or `python3 --version` in Command Prompt.\n\n\n2. **Create a Virtual Environment**:\n   Open Command Prompt and navigate to your project directory. Then create a virtual environment using the following command:\n\n   ```bash\n   python -m venv gradio-env\n   ```\n\n   This command creates a new directory `gradio-env` in your project folder, containing a fresh Python installation.\n\n3. **Activate the Virtual Environment**:\n   To activate the virtual environment, run:\n\n   ```bash\n   .\\gradio-env\\Scripts\\activate\n   ```\n\n   Your command prompt should now indicate that you are working inside `gradio-env`. Note: you can choose a different name than `gradio-env` for your virtual environment in this step.\n\n\n4. **Install Gradio**:\n   Now, you can install Gradio using pip:\n\n   ```bash\n   pip install gradio\n   ```\n\n5. **Verification**:\n   To verify the installation, run `python` and then type:\n\n   ```python\n   import gradio as gr\n   print(gr.__version__)\n   ```\n\n   This will display the installed version of Gradio.\n\n", "heading1": "Installing Gradio on Windows", "source_page_url": "https://gradio.app/guides/installing-gradio-in-a-virtual-environment", "source_page_title": "Other Tutorials - Installing Gradio In A Virtual Environment Guide"}, {"text": "The installation steps on MacOS and Linux are similar to Windows but with some differences in commands.\n\n1. **Install Python**:\n   Python usually comes pre-installed on MacOS and most Linux distributions. You can verify the installation by running `python --version` in the terminal (note that depending on how Python is installed, you might have to use `python3` instead of `python` throughout these steps). \n   \n   Ensure you have Python 3.10 or higher installed. If you do not have it installed, you can download it from [python.org](https://www.python.org/). \n\n2. **Create a Virtual Environment**:\n   Open Terminal and navigate to your project directory. Then create a virtual environment using:\n\n   ```bash\n   python -m venv gradio-env\n   ```\n\n   Note: you can choose a different name than `gradio-env` for your virtual environment in this step.\n\n3. **Activate the Virtual Environment**:\n   To activate the virtual environment on MacOS/Linux, use:\n\n   ```bash\n   source gradio-env/bin/activate\n   ```\n\n4. **Install Gradio**:\n   With the virtual environment activated, install Gradio using pip:\n\n   ```bash\n   pip install gradio\n   ```\n\n5. **Verification**:\n   To verify the installation, run `python` and then type:\n\n   ```python\n   import gradio as gr\n   print(gr.__version__)\n   ```\n\n   This will display the installed version of Gradio.\n\nBy following these steps, you can successfully install Gradio in a virtual environment on your operating system, ensuring a clean and managed workspace for your Python projects.", "heading1": "Installing Gradio on MacOS/Linux", "source_page_url": "https://gradio.app/guides/installing-gradio-in-a-virtual-environment", "source_page_title": "Other Tutorials - Installing Gradio In A Virtual Environment Guide"}, {"text": "When you are building a Gradio demo, particularly out of Blocks, you may find it cumbersome to keep re-running your code to test your changes.\n\nTo make it faster and more convenient to write your code, we've made it easier to \"reload\" your Gradio apps instantly when you are developing in a **Python IDE** (like VS Code, Sublime Text, PyCharm, or so on) or generally running your Python code from the terminal. We've also developed an analogous \"magic command\" that allows you to re-run cells faster if you use **Jupyter Notebooks** (or any similar environment like Colab).\n\nThis short Guide will cover both of these methods, so no matter how you write Python, you'll leave knowing how to build Gradio apps faster.\n\n", "heading1": "Why Hot Reloading?", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "If you are building Gradio Blocks using a Python IDE, your file of code (let's name it `run.py`) might look something like this:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Greetings from Gradio!\")\n    inp = gr.Textbox(placeholder=\"What is your name?\")\n    out = gr.Textbox()\n\n    inp.change(fn=lambda x: f\"Welcome, {x}!\",\n               inputs=inp,\n               outputs=out)\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\nThe problem is that anytime that you want to make a change to your layout, events, or components, you have to close and rerun your app by writing `python run.py`.\n\nInstead of doing this, you can run your code in **reload mode** by changing 1 word: `python` to `gradio`:\n\nIn the terminal, run `gradio run.py`. That's it!\n\nNow, you'll see that after you'll see something like this:\n\n```bash\nWatching: '/Users/freddy/sources/gradio/gradio', '/Users/freddy/sources/gradio/demo/'\n\nRunning on local URL:  http://127.0.0.1:7860\n```\n\nThe important part here is the line that says `Watching...` What's happening here is that Gradio will be observing the directory where `run.py` file lives, and if the file changes, it will automatically rerun the file for you. So you can focus on writing your code, and your Gradio demo will refresh automatically \ud83e\udd73\n\nTip: the `gradio` command does not detect the parameters passed to the `launch()` methods because the `launch()` method is never called in reload mode. For example, setting `auth`, or `show_error` in `launch()` will not be reflected in the app.\n\nThere is one important thing to keep in mind when using the reload mode: Gradio specifically looks for a Gradio Blocks/Interface demo called `demo` in your code. If you have named your demo something else, you will need to pass in the name of your demo as the 2nd parameter in your code. So if your `run.py` file looked like this:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as my_demo:\n    gr.Markdown(\"Greetings from Gradio!\")\n    inp = gr.", "heading1": "Python IDE Reload \ud83d\udd25", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "emo as the 2nd parameter in your code. So if your `run.py` file looked like this:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as my_demo:\n    gr.Markdown(\"Greetings from Gradio!\")\n    inp = gr.Textbox(placeholder=\"What is your name?\")\n    out = gr.Textbox()\n\n    inp.change(fn=lambda x: f\"Welcome, {x}!\",\n               inputs=inp,\n               outputs=out)\n\nif __name__ == \"__main__\":\n    my_demo.launch()\n```\n\nThen you would launch it in reload mode like this: `gradio run.py --demo-name=my_demo`.\n\nBy default, the Gradio use UTF-8 encoding for scripts. **For reload mode**, If you are using encoding formats other than UTF-8 (such as cp1252), make sure you've done like this:\n\n1. Configure encoding declaration of python script, for example: `-*- coding: cp1252 -*-`\n2. Confirm that your code editor has identified that encoding format. \n3. Run like this: `gradio run.py --encoding cp1252`\n\n\ud83d\udd25 If your application accepts command line arguments, you can pass them in as well. Here's an example:\n\n```python\nimport gradio as gr\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--name\", type=str, default=\"User\")\nargs, unknown = parser.parse_known_args()\n\nwith gr.Blocks() as demo:\n    gr.Markdown(f\"Greetings {args.name}!\")\n    inp = gr.Textbox()\n    out = gr.Textbox()\n\n    inp.change(fn=lambda x: x, inputs=inp, outputs=out)\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\nWhich you could run like this: `gradio run.py --name Gretel`\n\nAs a small aside, this auto-reloading happens if you change your `run.py` source code or the Gradio source code. Meaning that this can be useful if you decide to [contribute to Gradio itself](https://github.com/gradio-app/gradio/blob/main/CONTRIBUTING.md) \u2705\n\n\n", "heading1": "Python IDE Reload \ud83d\udd25", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "By default, reload mode will re-run your entire script for every change you make.\nBut there are some cases where this is not desirable.\nFor example, loading a machine learning model should probably only happen once to save time. There are also some Python libraries that use C or Rust extensions that throw errors when they are reloaded, like `numpy` and `tiktoken`.\n\nIn these situations, you can place code that you do not want to be re-run inside an `if gr.NO_RELOAD:`  codeblock. Here's an example of how you can use it to only load a transformers model once during the development process.\n\nTip: The value of `gr.NO_RELOAD` is `True`. So you don't have to change your script when you are done developing and want to run it in production. Simply run the file with `python` instead of `gradio`.\n\n```python\nimport gradio as gr\n\nif gr.NO_RELOAD:\n\tfrom transformers import pipeline\n\tpipe = pipeline(\"text-classification\", model=\"cardiffnlp/twitter-roberta-base-sentiment-latest\")\n\ndemo = gr.Interface(lambda s: {d[\"label\"]: d[\"score\"] for d in pipe(s)}, gr.Textbox(), gr.Label())\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\n", "heading1": "Controlling the Reload \ud83c\udf9b\ufe0f", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "You can also enable Gradio's **Vibe Mode**, which, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. To enable this, simply run use the `--vibe` flag with Gradio, e.g. `gradio --vibe app.py`.\n\nVibe Mode lets you describe commands using natural language and have an LLM write or edit the code in your Gradio app. The LLM is powered by Hugging Face's [Inference Providers](https://huggingface.co/docs/inference-providers/en/index), so you must be logged into Hugging Face locally to use this. \n\nNote: When Vibe Mode is enabled, anyone who can access the Gradio endpoint can modify files and run arbitrary code on the host machine. Use only for local development.\n\n", "heading1": "Vibe Mode", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "What about if you use Jupyter Notebooks (or Colab Notebooks, etc.) to develop code? We got something for you too!\n\nWe've developed a **magic command** that will create and run a Blocks demo for you. To use this, load the gradio extension at the top of your notebook:\n\n`%load_ext gradio`\n\nThen, in the cell that you are developing your Gradio demo, simply write the magic command **`%%blocks`** at the top, and then write the layout and components like you would normally:\n\n```py\n%%blocks\n\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(f\"Greetings {args.name}!\")\n    inp = gr.Textbox()\n    out = gr.Textbox()\n\n    inp.change(fn=lambda x: x, inputs=inp, outputs=out)\n```\n\nNotice that:\n\n- You do not need to launch your demo \u2014 Gradio does that for you automatically!\n\n- Every time you rerun the cell, Gradio will re-render your app on the same port and using the same underlying web server. This means you'll see your changes _much, much faster_ than if you were rerunning the cell normally.\n\nHere's what it looks like in a jupyter notebook:\n\n![](https://gradio-builds.s3.amazonaws.com/demo-files/jupyter_reload.gif)\n\n\ud83e\ude84 This works in colab notebooks too! [Here's a colab notebook](https://colab.research.google.com/drive/1zAuWoiTIb3O2oitbtVb2_ekv1K6ggtC1?usp=sharing) where you can see the Blocks magic in action. Try making some changes and re-running the cell with the Gradio code!\n\nTip: You may have to use `%%blocks --share` in Colab to get the demo to appear in the cell.\n\nThe Notebook Magic is now the author's preferred way of building Gradio demos. Regardless of how you write Python code, we hope either of these methods will give you a much better development experience using Gradio.\n\n---\n\n", "heading1": "Jupyter Notebook Magic \ud83d\udd2e", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "Now that you know how to develop quickly using Gradio, start building your own!\n\nIf you are looking for inspiration, try exploring demos other people have built with Gradio, [browse public Hugging Face Spaces](http://hf.space/) \ud83e\udd17\n", "heading1": "Next Steps", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "Tabular data science is the most widely used domain of machine learning, with problems ranging from customer segmentation to churn prediction. Throughout various stages of the tabular data science workflow, communicating your work to stakeholders or clients can be cumbersome; which prevents data scientists from focusing on what matters, such as data analysis and model building. Data scientists can end up spending hours building a dashboard that takes in dataframe and returning plots, or returning a prediction or plot of clusters in a dataset. In this guide, we'll go through how to use `gradio` to improve your data science workflows. We will also talk about how to use `gradio` and [skops](https://skops.readthedocs.io/en/stable/) to build interfaces with only one line of code!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-gradio-for-tabular-workflows", "source_page_title": "Other Tutorials - Using Gradio For Tabular Workflows Guide"}, {"text": "We will take a look at how we can create a simple UI that predicts failures based on product information.\n\n```python\nimport gradio as gr\nimport pandas as pd\nimport joblib\nimport datasets\n\n\ninputs = [gr.Dataframe(row_count = (2, \"dynamic\"), col_count=(4,\"dynamic\"), label=\"Input Data\", interactive=1)]\n\noutputs = [gr.Dataframe(row_count = (2, \"dynamic\"), col_count=(1, \"fixed\"), label=\"Predictions\", headers=[\"Failures\"])]\n\nmodel = joblib.load(\"model.pkl\")\n\nwe will give our dataframe as example\ndf = datasets.load_dataset(\"merve/supersoaker-failures\")\ndf = df[\"train\"].to_pandas()\n\ndef infer(input_dataframe):\n  return pd.DataFrame(model.predict(input_dataframe))\n\ngr.Interface(fn = infer, inputs = inputs, outputs = outputs, examples = [[df.head(2)]]).launch()\n```\n\nLet's break down above code.\n\n- `fn`: the inference function that takes input dataframe and returns predictions.\n- `inputs`: the component we take our input with. We define our input as dataframe with 2 rows and 4 columns, which initially will look like an empty dataframe with the aforementioned shape. When the `row_count` is set to `dynamic`, you don't have to rely on the dataset you're inputting to pre-defined component.\n- `outputs`: The dataframe component that stores outputs. This UI can take single or multiple samples to infer, and returns 0 or 1 for each sample in one column, so we give `row_count` as 2 and `col_count` as 1 above. `headers` is a list made of header names for dataframe.\n- `examples`: You can either pass the input by dragging and dropping a CSV file, or a pandas DataFrame through examples, which headers will be automatically taken by the interface.\n\nWe will now create an example for a minimal data visualization dashboard. You can find a more comprehensive version in the related Spaces.\n\n<gradio-app space=\"gradio/tabular-playground\"></gradio-app>\n\n```python\nimport gradio as gr\nimport pandas as pd\nimport datasets\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndf = datasets.load_dataset", "heading1": "Let's Create a Simple Interface!", "source_page_url": "https://gradio.app/guides/using-gradio-for-tabular-workflows", "source_page_title": "Other Tutorials - Using Gradio For Tabular Workflows Guide"}, {"text": "app space=\"gradio/tabular-playground\"></gradio-app>\n\n```python\nimport gradio as gr\nimport pandas as pd\nimport datasets\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndf = datasets.load_dataset(\"merve/supersoaker-failures\")\ndf = df[\"train\"].to_pandas()\ndf.dropna(axis=0, inplace=True)\n\ndef plot(df):\n  plt.scatter(df.measurement_13, df.measurement_15, c = df.loading,alpha=0.5)\n  plt.savefig(\"scatter.png\")\n  df['failure'].value_counts().plot(kind='bar')\n  plt.savefig(\"bar.png\")\n  sns.heatmap(df.select_dtypes(include=\"number\").corr())\n  plt.savefig(\"corr.png\")\n  plots = [\"corr.png\",\"scatter.png\", \"bar.png\"]\n  return plots\n\ninputs = [gr.Dataframe(label=\"Supersoaker Production Data\")]\noutputs = [gr.Gallery(label=\"Profiling Dashboard\", columns=(1,3))]\n\ngr.Interface(plot, inputs=inputs, outputs=outputs, examples=[df.head(100)], title=\"Supersoaker Failures Analysis Dashboard\").launch()\n```\n\n<gradio-app space=\"gradio/gradio-analysis-dashboard-minimal\"></gradio-app>\n\nWe will use the same dataset we used to train our model, but we will make a dashboard to visualize it this time.\n\n- `fn`: The function that will create plots based on data.\n- `inputs`: We use the same `Dataframe` component we used above.\n- `outputs`: The `Gallery` component is used to keep our visualizations.\n- `examples`: We will have the dataset itself as the example.\n\n", "heading1": "Let's Create a Simple Interface!", "source_page_url": "https://gradio.app/guides/using-gradio-for-tabular-workflows", "source_page_title": "Other Tutorials - Using Gradio For Tabular Workflows Guide"}, {"text": "`skops` is a library built on top of `huggingface_hub` and `sklearn`. With the recent `gradio` integration of `skops`, you can build tabular data interfaces with one line of code!\n\n```python\nimport gradio as gr\n\ntitle and description are optional\ntitle = \"Supersoaker Defective Product Prediction\"\ndescription = \"This model predicts Supersoaker production line failures. Drag and drop any slice from dataset or edit values as you wish in below dataframe component.\"\n\ngr.load(\"huggingface/scikit-learn/tabular-playground\", title=title, description=description).launch()\n```\n\n<gradio-app space=\"gradio/gradio-skops-integration\"></gradio-app>\n\n`sklearn` models pushed to Hugging Face Hub using `skops` include a `config.json` file that contains an example input with column names, the task being solved (that can either be `tabular-classification` or `tabular-regression`). From the task type, `gradio` constructs the `Interface` and consumes column names and the example input to build it. You can [refer to skops documentation on hosting models on Hub](https://skops.readthedocs.io/en/latest/auto_examples/plot_hf_hub.htmlsphx-glr-auto-examples-plot-hf-hub-py) to learn how to push your models to Hub using `skops`.\n", "heading1": "Easily load tabular data interfaces with one line of code using skops", "source_page_url": "https://gradio.app/guides/using-gradio-for-tabular-workflows", "source_page_title": "Other Tutorials - Using Gradio For Tabular Workflows Guide"}, {"text": "In this Guide, we'll walk you through:\n\n- Introduction of ONNX, ONNX model zoo, Gradio, and Hugging Face Spaces\n- How to setup a Gradio demo for EfficientNet-Lite4\n- How to contribute your own Gradio demos for the ONNX organization on Hugging Face\n\nHere's an [example](https://onnx-efficientnet-lite4.hf.space/) of an ONNX model.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "Open Neural Network Exchange ([ONNX](https://onnx.ai/)) is an open standard format for representing machine learning models. ONNX is supported by a community of partners who have implemented it in many frameworks and tools. For example, if you have trained a model in TensorFlow or PyTorch, you can convert it to ONNX easily, and from there run it on a variety of devices using an engine/compiler like ONNX Runtime.\n\nThe [ONNX Model Zoo](https://github.com/onnx/models) is a collection of pre-trained, state-of-the-art models in the ONNX format contributed by community members. Accompanying each model are Jupyter notebooks for model training and running inference with the trained model. The notebooks are written in Python and include links to the training dataset as well as references to the original paper that describes the model architecture.\n\n", "heading1": "What is the ONNX Model Zoo?", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "Gradio\n\nGradio lets users demo their machine learning models as a web app all in python code. Gradio wraps a python function into a user interface and the demos can be launched inside jupyter notebooks, colab notebooks, as well as embedded in your own website and hosted on Hugging Face Spaces for free.\n\nGet started [here](https://gradio.app/getting_started)\n\nHugging Face Spaces\n\nHugging Face Spaces is a free hosting option for Gradio demos. Spaces comes with 3 SDK options: Gradio, Streamlit and Static HTML demos. Spaces can be public or private and the workflow is similar to github repos. There are over 2000+ spaces currently on Hugging Face. Learn more about spaces [here](https://huggingface.co/spaces/launch).\n\nHugging Face Models\n\nHugging Face Model Hub also supports ONNX models and ONNX models can be filtered through the [ONNX tag](https://huggingface.co/models?library=onnx&sort=downloads)\n\n", "heading1": "What are Hugging Face Spaces & Gradio?", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "There are a lot of Jupyter notebooks in the ONNX Model Zoo for users to test models. Previously, users needed to download the models themselves and run those notebooks locally for testing. With Hugging Face, the testing process can be much simpler and more user-friendly. Users can easily try certain ONNX Model Zoo model on Hugging Face Spaces and run a quick demo powered by Gradio with ONNX Runtime, all on cloud without downloading anything locally. Note, there are various runtimes for ONNX, e.g., [ONNX Runtime](https://github.com/microsoft/onnxruntime), [MXNet](https://github.com/apache/incubator-mxnet).\n\n", "heading1": "How did Hugging Face help the ONNX Model Zoo?", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "ONNX Runtime is a cross-platform inference and training machine-learning accelerator. It makes live Gradio demos with ONNX Model Zoo model on Hugging Face possible.\n\nONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. For more information please see the [official website](https://onnxruntime.ai/).\n\n", "heading1": "What is the role of ONNX Runtime?", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite models. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU. To learn more read the [model card](https://github.com/onnx/models/tree/main/vision/classification/efficientnet-lite4)\n\nHere we walk through setting up a example demo for EfficientNet-Lite4 using Gradio\n\nFirst we import our dependencies and download and load the efficientnet-lite4 model from the onnx model zoo. Then load the labels from the labels_map.txt file. We then setup our preprocessing functions, load the model for inference, and setup the inference function. Finally, the inference function is wrapped into a gradio interface for a user to interact with. See the full code below.\n\n```python\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport cv2\nimport json\nimport gradio as gr\nfrom huggingface_hub import hf_hub_download\nfrom onnx import hub\nimport onnxruntime as ort\n\nloads ONNX model from ONNX Model Zoo\nmodel = hub.load(\"efficientnet-lite4\")\nloads the labels text file\nlabels = json.load(open(\"labels_map.txt\", \"r\"))\n\nsets image file dimensions to 224x224 by resizing and cropping image from center\ndef pre_process_edgetpu(img, dims):\n    output_height, output_width, _ = dims\n    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)\n    img = center_crop(img, output_height, output_width)\n    img = np.asarray(img, dtype='float32')\n    converts jpg pixel value from [0 - 255] to float array [-1.0 - 1.0]\n    img -= [127.0, 127.0, 127.0]\n    img /= [128.0, 128.0, 128.0]\n    return img\n\nresizes the image with a proportional scale\ndef resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):\n    height, width, _ = img.shape\n    new_height = int(100. * out_he", "heading1": "Setting up a Gradio Demo for EfficientNet-Lite4", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "the image with a proportional scale\ndef resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):\n    height, width, _ = img.shape\n    new_height = int(100. * out_height / scale)\n    new_width = int(100. * out_width / scale)\n    if height > width:\n        w = new_width\n        h = int(new_height * height / width)\n    else:\n        h = new_height\n        w = int(new_width * width / height)\n    img = cv2.resize(img, (w, h), interpolation=inter_pol)\n    return img\n\ncrops the image around the center based on given height and width\ndef center_crop(img, out_height, out_width):\n    height, width, _ = img.shape\n    left = int((width - out_width) / 2)\n    right = int((width + out_width) / 2)\n    top = int((height - out_height) / 2)\n    bottom = int((height + out_height) / 2)\n    img = img[top:bottom, left:right]\n    return img\n\n\nsess = ort.InferenceSession(model)\n\ndef inference(img):\n  img = cv2.imread(img)\n  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n  img = pre_process_edgetpu(img, (224, 224, 3))\n\n  img_batch = np.expand_dims(img, axis=0)\n\n  results = sess.run([\"Softmax:0\"], {\"images:0\": img_batch})[0]\n  result = reversed(results[0].argsort()[-5:])\n  resultdic = {}\n  for r in result:\n      resultdic[labels[str(r)]] = float(results[0][r])\n  return resultdic\n\ntitle = \"EfficientNet-Lite4\"\ndescription = \"EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU.\"\nexamples = [['catonnx.jpg']]\ngr.Interface(inference, gr.Image(type=\"filepath\"), \"label\", title=title, description=description, examples=examples).launch()\n```\n\n", "heading1": "Setting up a Gradio Demo for EfficientNet-Lite4", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": " examples=examples).launch()\n```\n\n", "heading1": "Setting up a Gradio Demo for EfficientNet-Lite4", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "- Add model to the [onnx model zoo](https://github.com/onnx/models/blob/main/.github/PULL_REQUEST_TEMPLATE.md)\n- Create an account on Hugging Face [here](https://huggingface.co/join).\n- See list of models left to add to ONNX organization, please refer to the table with the [Models list](https://github.com/onnx/modelsmodels)\n- Add Gradio Demo under your username, see this [blog post](https://huggingface.co/blog/gradio-spaces) for setting up Gradio Demo on Hugging Face.\n- Request to join ONNX Organization [here](https://huggingface.co/onnx).\n- Once approved transfer model from your username to ONNX organization\n- Add a badge for model in model table, see examples in [Models list](https://github.com/onnx/modelsmodels)\n", "heading1": "How to contribute Gradio demos on HF spaces using ONNX models", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "3D models are becoming more popular in machine learning and make for some of the most fun demos to experiment with. Using `gradio`, you can easily build a demo of your 3D image model and share it with anyone. The Gradio 3D Model component accepts 3 file types including: _.obj_, _.glb_, & _.gltf_.\n\nThis guide will show you how to build a demo for your 3D image model in a few lines of code; like the one below. Play around with 3D object by clicking around, dragging and zooming:\n\n<gradio-app space=\"gradio/Model3D\"> </gradio-app>\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](https://gradio.app/guides/quickstart).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/how-to-use-3D-model-component", "source_page_title": "Other Tutorials - How To Use 3D Model Component Guide"}, {"text": "Let's take a look at how to create the minimal interface above. The prediction function in this case will just return the original 3D model mesh, but you can change this function to run inference on your machine learning model. We'll take a look at more complex examples below.\n\n```python\nimport gradio as gr\nimport os\n\n\ndef load_mesh(mesh_file_name):\n    return mesh_file_name\n\n\ndemo = gr.Interface(\n    fn=load_mesh,\n    inputs=gr.Model3D(),\n    outputs=gr.Model3D(\n            clear_color=[0.0, 0.0, 0.0, 0.0],  label=\"3D Model\"),\n    examples=[\n        [os.path.join(os.path.dirname(__file__), \"files/Bunny.obj\")],\n        [os.path.join(os.path.dirname(__file__), \"files/Duck.glb\")],\n        [os.path.join(os.path.dirname(__file__), \"files/Fox.gltf\")],\n        [os.path.join(os.path.dirname(__file__), \"files/face.obj\")],\n    ],\n)\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\nLet's break down the code above:\n\n`load_mesh`: This is our 'prediction' function and for simplicity, this function will take in the 3D model mesh and return it.\n\nCreating the Interface:\n\n- `fn`: the prediction function that is used when the user clicks submit. In our case this is the `load_mesh` function.\n- `inputs`: create a model3D input component. The input expects an uploaded file as a {str} filepath.\n- `outputs`: create a model3D output component. The output component also expects a file as a {str} filepath.\n  - `clear_color`: this is the background color of the 3D model canvas. Expects RGBa values.\n  - `label`: the label that appears on the top left of the component.\n- `examples`: list of 3D model files. The 3D model component can accept _.obj_, _.glb_, & _.gltf_ file types.\n- `cache_examples`: saves the predicted output for the examples, to save time on inference.\n\n", "heading1": "Taking a Look at the Code", "source_page_url": "https://gradio.app/guides/how-to-use-3D-model-component", "source_page_title": "Other Tutorials - How To Use 3D Model Component Guide"}, {"text": "Below is a demo that uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object. Take a look at the [app.py](https://huggingface.co/spaces/gradio/dpt-depth-estimation-3d-obj/blob/main/app.py) file for a peek into the code and the model prediction function.\n<gradio-app space=\"gradio/dpt-depth-estimation-3d-obj\"> </gradio-app>\n\n---\n\nAnd you're done! That's all the code you need to build an interface for your Model3D model. Here are some references that you may find useful:\n\n- Gradio's [\"Getting Started\" guide](https://gradio.app/getting_started/)\n- The first [3D Model Demo](https://huggingface.co/spaces/gradio/Model3D) and [complete code](https://huggingface.co/spaces/gradio/Model3D/tree/main) (on Hugging Face Spaces)\n", "heading1": "Exploring a more complex Model3D Demo:", "source_page_url": "https://gradio.app/guides/how-to-use-3D-model-component", "source_page_title": "Other Tutorials - How To Use 3D Model Component Guide"}, {"text": "Building a dashboard from a public Google Sheet is very easy, thanks to the [`pandas` library](https://pandas.pydata.org/):\n\n1\\. Get the URL of the Google Sheets that you want to use. To do this, simply go to the Google Sheets, click on the \"Share\" button in the top-right corner, and then click on the \"Get shareable link\" button. This will give you a URL that looks something like this:\n\n```html\nhttps://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\n```\n\n2\\. Now, let's modify this URL and then use it to read the data from the Google Sheets into a Pandas DataFrame. (In the code below, replace the `URL` variable with the URL of your public Google Sheet):\n\n```python\nimport pandas as pd\n\nURL = \"https://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\"\ncsv_url = URL.replace('/editgid=', '/export?format=csv&gid=')\n\ndef get_data():\n    return pd.read_csv(csv_url)\n```\n\n3\\. The data query is a function, which means that it's easy to display it real-time using the `gr.DataFrame` component, or plot it real-time using the `gr.LinePlot` component (of course, depending on the data, a different plot may be appropriate). To do this, just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) you would like the component to refresh. Here's the Gradio code:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"\ud83d\udcc8 Real-Time Line Plot\")\n    with gr.Row():\n        with gr.Column():\n            gr.DataFrame(get_data, every=gr.Timer(5))\n        with gr.Column():\n            gr.LinePlot(get_data, every=gr.Timer(5), x=\"Date\", y=\"Sales\", y_title=\"Sales ($ millions)\", overlay_point=True, width=500, height=500)\n\ndemo.queue().launch()  Run the demo with queuing enabled\n```\n\nAnd that's it! You have a dashboard that refreshes every 5 seconds, pulling the data from your Google Sheet.\n\n", "heading1": "Public Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "For private Google Sheets, the process requires a little more work, but not that much! The key difference is that now, you must authenticate yourself to authorize access to the private Google Sheets.\n\nAuthentication\n\nTo authenticate yourself, obtain credentials from Google Cloud. Here's [how to set up google cloud credentials](https://developers.google.com/workspace/guides/create-credentials):\n\n1\\. First, log in to your Google Cloud account and go to the Google Cloud Console (https://console.cloud.google.com/)\n\n2\\. In the Cloud Console, click on the hamburger menu in the top-left corner and select \"APIs & Services\" from the menu. If you do not have an existing project, you will need to create one.\n\n3\\. Then, click the \"+ Enabled APIs & services\" button, which allows you to enable specific services for your project. Search for \"Google Sheets API\", click on it, and click the \"Enable\" button. If you see the \"Manage\" button, then Google Sheets is already enabled, and you're all set.\n\n4\\. In the APIs & Services menu, click on the \"Credentials\" tab and then click on the \"Create credentials\" button.\n\n5\\. In the \"Create credentials\" dialog, select \"Service account key\" as the type of credentials to create, and give it a name. **Note down the email of the service account**\n\n6\\. After selecting the service account, select the \"JSON\" key type and then click on the \"Create\" button. This will download the JSON key file containing your credentials to your computer. It will look something like this:\n\n```json\n{\n\t\"type\": \"service_account\",\n\t\"project_id\": \"your project\",\n\t\"private_key_id\": \"your private key id\",\n\t\"private_key\": \"private key\",\n\t\"client_email\": \"email\",\n\t\"client_id\": \"client id\",\n\t\"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n\t\"token_uri\": \"https://accounts.google.com/o/oauth2/token\",\n\t\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n\t\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/email_id\"\n}\n```\n\n", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "google.com/o/oauth2/token\",\n\t\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n\t\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/email_id\"\n}\n```\n\nQuerying\n\nOnce you have the credentials `.json` file, you can use the following steps to query your Google Sheet:\n\n1\\. Click on the \"Share\" button in the top-right corner of the Google Sheet. Share the Google Sheets with the email address of the service from Step 5 of authentication subsection (this step is important!). Then click on the \"Get shareable link\" button. This will give you a URL that looks something like this:\n\n```html\nhttps://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\n```\n\n2\\. Install the [`gspread` library](https://docs.gspread.org/en/v5.7.0/), which makes it easy to work with the [Google Sheets API](https://developers.google.com/sheets/api/guides/concepts) in Python by running in the terminal: `pip install gspread`\n\n3\\. Write a function to load the data from the Google Sheet, like this (replace the `URL` variable with the URL of your private Google Sheet):\n\n```python\nimport gspread\nimport pandas as pd\n\nAuthenticate with Google and get the sheet\nURL = 'https://docs.google.com/spreadsheets/d/1_91Vps76SKOdDQ8cFxZQdgjTJiz23375sAT7vPvaj4k/editgid=0'\n\ngc = gspread.service_account(\"path/to/key.json\")\nsh = gc.open_by_url(URL)\nworksheet = sh.sheet1\n\ndef get_data():\n    values = worksheet.get_all_values()\n    df = pd.DataFrame(values[1:], columns=values[0])\n    return df\n\n```\n\n4\\. The data query is a function, which means that it's easy to display it real-time using the `gr.DataFrame` component, or plot it real-time using the `gr.LinePlot` component (of course, depending on the data, a different plot may be appropriate). To do this, we just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) we would like the component to refresh. Here's the Gradio cod", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": ". To do this, we just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) we would like the component to refresh. Here's the Gradio code:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"\ud83d\udcc8 Real-Time Line Plot\")\n    with gr.Row():\n        with gr.Column():\n            gr.DataFrame(get_data, every=gr.Timer(5))\n        with gr.Column():\n            gr.LinePlot(get_data, every=gr.Timer(5), x=\"Date\", y=\"Sales\", y_title=\"Sales ($ millions)\", overlay_point=True, width=500, height=500)\n\ndemo.queue().launch()  Run the demo with queuing enabled\n```\n\nYou now have a Dashboard that refreshes every 5 seconds, pulling the data from your Google Sheet.\n\n", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "And that's all there is to it! With just a few lines of code, you can use `gradio` and other libraries to read data from a public or private Google Sheet and then display and plot the data in a real-time dashboard.\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "In this Guide, we'll walk you through:\n\n- Introduction of Gradio, and Hugging Face Spaces, and Wandb\n- How to setup a Gradio demo using the Wandb integration for JoJoGAN\n- How to contribute your own Gradio demos after tracking your experiments on wandb to the Wandb organization on Hugging Face\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Weights and Biases (W&B) allows data scientists and machine learning scientists to track their machine learning experiments at every stage, from training to production. Any metric can be aggregated over samples and shown in panels in a customizable and searchable dashboard, like below:\n\n<img alt=\"Screen Shot 2022-08-01 at 5 54 59 PM\" src=\"https://user-images.githubusercontent.com/81195143/182252755-4a0e1ca8-fd25-40ff-8c91-c9da38aaa9ec.png\">\n\n", "heading1": "What is Wandb?", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Gradio\n\nGradio lets users demo their machine learning models as a web app, all in a few lines of Python. Gradio wraps any Python function (such as a machine learning model's inference function) into a user interface and the demos can be launched inside jupyter notebooks, colab notebooks, as well as embedded in your own website and hosted on Hugging Face Spaces for free.\n\nGet started [here](https://gradio.app/getting_started)\n\nHugging Face Spaces\n\nHugging Face Spaces is a free hosting option for Gradio demos. Spaces comes with 3 SDK options: Gradio, Streamlit and Static HTML demos. Spaces can be public or private and the workflow is similar to github repos. There are over 2000+ spaces currently on Hugging Face. Learn more about spaces [here](https://huggingface.co/spaces/launch).\n\n", "heading1": "What are Hugging Face Spaces & Gradio?", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Now, let's walk you through how to do this on your own. We'll make the assumption that you're new to W&B and Gradio for the purposes of this tutorial.\n\nLet's get started!\n\n1. Create a W&B account\n\n   Follow [these quick instructions](https://app.wandb.ai/login) to create your free account if you don\u2019t have one already. It shouldn't take more than a couple minutes. Once you're done (or if you've already got an account), next, we'll run a quick colab.\n\n2. Open Colab Install Gradio and W&B\n\n   We'll be following along with the colab provided in the JoJoGAN repo with some minor modifications to use Wandb and Gradio more effectively.\n\n   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mchong6/JoJoGAN/blob/main/stylize.ipynb)\n\n   Install Gradio and Wandb at the top:\n\n   ```sh\n   pip install gradio wandb\n   ```\n\n3. Finetune StyleGAN and W&B experiment tracking\n\n   This next step will open a W&B dashboard to track your experiments and a gradio panel showing pretrained models to choose from a drop down menu from a Gradio Demo hosted on Huggingface Spaces. Here's the code you need for that:\n\n   ```python\n   alpha =  1.0\n   alpha = 1-alpha\n\n   preserve_color = True\n   num_iter = 100\n   log_interval = 50\n\n   samples = []\n   column_names = [\"Reference (y)\", \"Style Code(w)\", \"Real Face Image(x)\"]\n\n   wandb.init(project=\"JoJoGAN\")\n   config = wandb.config\n   config.num_iter = num_iter\n   config.preserve_color = preserve_color\n   wandb.log(\n   {\"Style reference\": [wandb.Image(transforms.ToPILImage()(target_im))]},\n   step=0)\n\n   load discriminator for perceptual loss\n   discriminator = Discriminator(1024, 2).eval().to(device)\n   ckpt = torch.load('models/stylegan2-ffhq-config-f.pt', map_location=lambda storage, loc: storage)\n   discriminator.load_state_dict(ckpt[\"d\"], strict=False)\n\n   reset generator\n   del generator\n   generator = deepcopy(original_generator)\n\n   g_optim = optim.Adam(generator.parameters(),", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": ": storage)\n   discriminator.load_state_dict(ckpt[\"d\"], strict=False)\n\n   reset generator\n   del generator\n   generator = deepcopy(original_generator)\n\n   g_optim = optim.Adam(generator.parameters(), lr=2e-3, betas=(0, 0.99))\n\n   Which layers to swap for generating a family of plausible real images -> fake image\n   if preserve_color:\n       id_swap = [9,11,15,16,17]\n   else:\n       id_swap = list(range(7, generator.n_latent))\n\n   for idx in tqdm(range(num_iter)):\n       mean_w = generator.get_latent(torch.randn([latents.size(0), latent_dim]).to(device)).unsqueeze(1).repeat(1, generator.n_latent, 1)\n       in_latent = latents.clone()\n       in_latent[:, id_swap] = alpha*latents[:, id_swap] + (1-alpha)*mean_w[:, id_swap]\n\n       img = generator(in_latent, input_is_latent=True)\n\n       with torch.no_grad():\n           real_feat = discriminator(targets)\n       fake_feat = discriminator(img)\n\n       loss = sum([F.l1_loss(a, b) for a, b in zip(fake_feat, real_feat)])/len(fake_feat)\n\n       wandb.log({\"loss\": loss}, step=idx)\n       if idx % log_interval == 0:\n           generator.eval()\n           my_sample = generator(my_w, input_is_latent=True)\n           generator.train()\n           my_sample = transforms.ToPILImage()(utils.make_grid(my_sample, normalize=True, range=(-1, 1)))\n           wandb.log(\n           {\"Current stylization\": [wandb.Image(my_sample)]},\n           step=idx)\n       table_data = [\n               wandb.Image(transforms.ToPILImage()(target_im)),\n               wandb.Image(img),\n               wandb.Image(my_sample),\n           ]\n       samples.append(table_data)\n\n       g_optim.zero_grad()\n       loss.backward()\n       g_optim.step()\n\n   out_table = wandb.Table(data=samples, columns=column_names)\n   wandb.log({\"Current Samples\": out_table})\n   ```\n4. Save, Download, and Load Model\n\n    Here's how to save and download your model.\n\n   ```python\n   from PIL import Image\n   import torch\n   torch.backends.cudnn.benchmark = True\n   from torchvision impor", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "ave, Download, and Load Model\n\n    Here's how to save and download your model.\n\n   ```python\n   from PIL import Image\n   import torch\n   torch.backends.cudnn.benchmark = True\n   from torchvision import transforms, utils\n   from util import *\n   import math\n   import random\n   import numpy as np\n   from torch import nn, autograd, optim\n   from torch.nn import functional as F\n   from tqdm import tqdm\n   import lpips\n   from model import *\n   from e4e_projection import projection as e4e_projection\n   \n   from copy import deepcopy\n   import imageio\n   \n   import os\n   import sys\n   import torchvision.transforms as transforms\n   from argparse import Namespace\n   from e4e.models.psp import pSp\n   from util import *\n   from huggingface_hub import hf_hub_download\n   from google.colab import files\n   \n   torch.save({\"g\": generator.state_dict()}, \"your-model-name.pt\")\n   \n   files.download('your-model-name.pt')\n   \n   latent_dim = 512\n   device=\"cuda\"\n   model_path_s = hf_hub_download(repo_id=\"akhaliq/jojogan-stylegan2-ffhq-config-f\", filename=\"stylegan2-ffhq-config-f.pt\")\n   original_generator = Generator(1024, latent_dim, 8, 2).to(device)\n   ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage)\n   original_generator.load_state_dict(ckpt[\"g_ema\"], strict=False)\n   mean_latent = original_generator.mean_latent(10000)\n   \n   generator = deepcopy(original_generator)\n   \n   ckpt = torch.load(\"/content/JoJoGAN/your-model-name.pt\", map_location=lambda storage, loc: storage)\n   generator.load_state_dict(ckpt[\"g\"], strict=False)\n   generator.eval()\n   \n   plt.rcParams['figure.dpi'] = 150\n   \n   transform = transforms.Compose(\n       [\n           transforms.Resize((1024, 1024)),\n           transforms.ToTensor(),\n           transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n       ]\n   )\n   \n   def inference(img):\n       img.save('out.jpg')\n       aligned_face = align_face('out.jpg')\n   \n       my_w = e4e_projection(aligned_face, \"out.pt\", device).unsqueeze(0)", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": ".5, 0.5)),\n       ]\n   )\n   \n   def inference(img):\n       img.save('out.jpg')\n       aligned_face = align_face('out.jpg')\n   \n       my_w = e4e_projection(aligned_face, \"out.pt\", device).unsqueeze(0)\n       with torch.no_grad():\n           my_sample = generator(my_w, input_is_latent=True)\n   \n       npimage = my_sample[0].cpu().permute(1, 2, 0).detach().numpy()\n       imageio.imwrite('filename.jpeg', npimage)\n       return 'filename.jpeg'\n   ````\n\n5. Build a Gradio Demo\n\n   ```python\n   import gradio as gr\n   \n   title = \"JoJoGAN\"\n   description = \"Gradio Demo for JoJoGAN: One Shot Face Stylization. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\"\n   \n   demo = gr.Interface(\n       inference,\n       gr.Image(type=\"pil\"),\n       gr.Image(type=\"file\"),\n       title=title,\n       description=description\n   )\n   \n   demo.launch(share=True)\n   ```\n\n6. Integrate Gradio into your W&B Dashboard\n\n   The last step\u2014integrating your Gradio demo with your W&B dashboard\u2014is just one extra line:\n\n   ```python\n   demo.integrate(wandb=wandb)\n   ```\n\n   Once you call integrate, a demo will be created and you can integrate it into your dashboard or report.\n\n   Outside of W&B with Web components, using the `gradio-app` tags, anyone can embed Gradio demos on HF spaces directly into their blogs, websites, documentation, etc.:\n   \n   ```html\n   <gradio-app space=\"akhaliq/JoJoGAN\"> </gradio-app>\n   ```\n\n7. (Optional) Embed W&B plots in your Gradio App\n\n   It's also possible to embed W&B plots within Gradio apps. To do so, you can create a W&B Report of your plots and\n   embed them within your Gradio app within a `gr.HTML` block.\n\n   The Report will need to be public and you will need to wrap the URL within an iFrame like this:\n\n   ```python\n   import gradio as gr\n   \n   def wandb_report(url):\n       iframe = f'<iframe src={url} style=\"border:none;height:1024px;width:100%\">'\n       return gr.HTML(iframe)\n   \n   with gr.Blocks() a", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "``python\n   import gradio as gr\n   \n   def wandb_report(url):\n       iframe = f'<iframe src={url} style=\"border:none;height:1024px;width:100%\">'\n       return gr.HTML(iframe)\n   \n   with gr.Blocks() as demo:\n       report_url = 'https://wandb.ai/_scott/pytorch-sweeps-demo/reports/loss-22-10-07-16-00-17---VmlldzoyNzU2NzAx'\n       report = wandb_report(report_url)\n   \n   demo.launch(share=True)\n   ```\n\n", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "We hope you enjoyed this brief demo of embedding a Gradio demo to a W&B report! Thanks for making it to the end. To recap:\n\n- Only one single reference image is needed for fine-tuning JoJoGAN which usually takes about 1 minute on a GPU in colab. After training, style can be applied to any input image. Read more in the paper.\n\n- W&B tracks experiments with just a few lines of code added to a colab and you can visualize, sort, and understand your experiments in a single, centralized dashboard.\n\n- Gradio, meanwhile, demos the model in a user friendly interface to share anywhere on the web.\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "- Create an account on Hugging Face [here](https://huggingface.co/join).\n- Add Gradio Demo under your username, see this [course](https://huggingface.co/course/chapter9/4?fw=pt) for setting up Gradio Demo on Hugging Face.\n- Request to join wandb organization [here](https://huggingface.co/wandb).\n- Once approved transfer model from your username to Wandb organization\n", "heading1": "How to contribute Gradio demos on HF spaces on the Wandb organization", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "In this guide we will demonstrate some of the ways you can use Gradio with Comet. We will cover the basics of using Comet with Gradio and show you some of the ways that you can leverage Gradio's advanced features such as [Embedding with iFrames](https://www.gradio.app/guides/sharing-your-app/embedding-with-iframes) and [State](https://www.gradio.app/docs/state) to build some amazing model evaluation workflows.\n\nHere is a list of the topics covered in this guide.\n\n1. Logging Gradio UI's to your Comet Experiments\n2. Embedding Gradio Applications directly into your Comet Projects\n3. Embedding Hugging Face Spaces directly into your Comet Projects\n4. Logging Model Inferences from your Gradio Application to Comet\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "[Comet](https://www.comet.com?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs) is an MLOps Platform that is designed to help Data Scientists and Teams build better models faster! Comet provides tooling to Track, Explain, Manage, and Monitor your models in a single place! It works with Jupyter Notebooks and Scripts and most importantly it's 100% free!\n\n", "heading1": "What is Comet?", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "First, install the dependencies needed to run these examples\n\n```shell\npip install comet_ml torch torchvision transformers gradio shap requests Pillow\n```\n\nNext, you will need to [sign up for a Comet Account](https://www.comet.com/signup?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs). Once you have your account set up, [grab your API Key](https://www.comet.com/docs/v2/guides/getting-started/quickstart/get-an-api-key?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs) and configure your Comet credentials\n\nIf you're running these examples as a script, you can either export your credentials as environment variables\n\n```shell\nexport COMET_API_KEY=\"<Your API Key>\"\nexport COMET_WORKSPACE=\"<Your Workspace Name>\"\nexport COMET_PROJECT_NAME=\"<Your Project Name>\"\n```\n\nor set them in a `.comet.config` file in your working directory. You file should be formatted in the following way.\n\n```shell\n[comet]\napi_key=<Your API Key>\nworkspace=<Your Workspace Name>\nproject_name=<Your Project Name>\n```\n\nIf you are using the provided Colab Notebooks to run these examples, please run the cell with the following snippet before starting the Gradio UI. Running this cell allows you to interactively add your API key to the notebook.\n\n```python\nimport comet_ml\ncomet_ml.init()\n```\n\n", "heading1": "Setup", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-evaluation/gradio/notebooks/Gradio_and_Comet.ipynb)\n\nIn this example, we will go over how to log your Gradio Applications to Comet and interact with them using the Gradio Custom Panel.\n\nLet's start by building a simple Image Classification example using `resnet18`.\n\n```python\nimport comet_ml\n\nimport requests\nimport torch\nfrom PIL import Image\nfrom torchvision import transforms\n\ntorch.hub.download_url_to_file(\"https://github.com/pytorch/hub/raw/master/images/dog.jpg\", \"dog.jpg\")\n\nif torch.cuda.is_available():\n    device = \"cuda\"\nelse:\n    device = \"cpu\"\n\nmodel = torch.hub.load(\"pytorch/vision:v0.6.0\", \"resnet18\", pretrained=True).eval()\nmodel = model.to(device)\n\nDownload human-readable labels for ImageNet.\nresponse = requests.get(\"https://git.io/JJkYN\")\nlabels = response.text.split(\"\\n\")\n\n\ndef predict(inp):\n    inp = Image.fromarray(inp.astype(\"uint8\"), \"RGB\")\n    inp = transforms.ToTensor()(inp).unsqueeze(0)\n    with torch.no_grad():\n        prediction = torch.nn.functional.softmax(model(inp.to(device))[0], dim=0)\n    return {labels[i]: float(prediction[i]) for i in range(1000)}\n\n\ninputs = gr.Image()\noutputs = gr.Label(num_top_classes=3)\n\nio = gr.Interface(\n    fn=predict, inputs=inputs, outputs=outputs, examples=[\"dog.jpg\"]\n)\nio.launch(inline=False, share=True)\n\nexperiment = comet_ml.Experiment()\nexperiment.add_tag(\"image-classifier\")\n\nio.integrate(comet_ml=experiment)\n```\n\nThe last line in this snippet will log the URL of the Gradio Application to your Comet Experiment. You can find the URL in the Text Tab of your Experiment.\n\n<video width=\"560\" height=\"315\" controls>\n    <source src=\"https://user-images.githubusercontent.com/7529846/214328034-09369d4d-8b94-4c4a-aa3c-25e3ed8394c4.mp4\"></source>\n</video>\n\nAdd the Gradio Panel to your Experiment to interact with your application.\n\n<video width=", "heading1": "1. Logging Gradio UI's to your Comet Experiments", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "r-images.githubusercontent.com/7529846/214328034-09369d4d-8b94-4c4a-aa3c-25e3ed8394c4.mp4\"></source>\n</video>\n\nAdd the Gradio Panel to your Experiment to interact with your application.\n\n<video width=\"560\" height=\"315\" controls>\n    <source src=\"https://user-images.githubusercontent.com/7529846/214328194-95987f83-c180-4929-9bed-c8a0d3563ed7.mp4\"></source>\n</video>\n\n", "heading1": "1. Logging Gradio UI's to your Comet Experiments", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/KZnpH7msPq0?start=9\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\nIf you are permanently hosting your Gradio application, you can embed the UI using the Gradio Panel Extended custom Panel.\n\nGo to your Comet Project page, and head over to the Panels tab. Click the `+ Add` button to bring up the Panels search page.\n\n<img width=\"560\" alt=\"adding-panels\" src=\"https://user-images.githubusercontent.com/7529846/214329314-70a3ff3d-27fb-408c-a4d1-4b58892a3854.jpeg\">\n\nNext, search for Gradio Panel Extended in the Public Panels section and click `Add`.\n\n<img width=\"560\" alt=\"gradio-panel-extended\" src=\"https://user-images.githubusercontent.com/7529846/214325577-43226119-0292-46be-a62a-0c7a80646ebb.png\">\n\nOnce you have added your Panel, click `Edit` to access to the Panel Options page and paste in the URL of your Gradio application.\n\n![Edit-Gradio-Panel-Options](https://user-images.githubusercontent.com/7529846/214573001-23814b5a-ca65-4ace-a8a5-b27cdda70f7a.gif)\n\n<img width=\"560\" alt=\"Edit-Gradio-Panel-URL\" src=\"https://user-images.githubusercontent.com/7529846/214334843-870fe726-0aa1-4b21-bbc6-0c48f56c48d8.png\">\n\n", "heading1": "2. Embedding Gradio Applications directly into your Comet Projects", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/KZnpH7msPq0?start=107\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\nYou can also embed Gradio Applications that are hosted on Hugging Faces Spaces into your Comet Projects using the Hugging Face Spaces Panel.\n\nGo to your Comet Project page, and head over to the Panels tab. Click the `+ Add` button to bring up the Panels search page. Next, search for the Hugging Face Spaces Panel in the Public Panels section and click `Add`.\n\n<img width=\"560\" height=\"315\" alt=\"huggingface-spaces-panel\" src=\"https://user-images.githubusercontent.com/7529846/214325606-99aa3af3-b284-4026-b423-d3d238797e12.png\">\n\nOnce you have added your Panel, click Edit to access to the Panel Options page and paste in the path of your Hugging Face Space e.g. `pytorch/ResNet`\n\n<img width=\"560\" height=\"315\" alt=\"Edit-HF-Space\" src=\"https://user-images.githubusercontent.com/7529846/214335868-c6f25dee-13db-4388-bcf5-65194f850b02.png\">\n\n", "heading1": "3. Embedding Hugging Face Spaces directly into your Comet Projects", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/KZnpH7msPq0?start=176\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-evaluation/gradio/notebooks/Logging_Model_Inferences_with_Comet_and_Gradio.ipynb)\n\nIn the previous examples, we demonstrated the various ways in which you can interact with a Gradio application through the Comet UI. Additionally, you can also log model inferences, such as SHAP plots, from your Gradio application to Comet.\n\nIn the following snippet, we're going to log inferences from a Text Generation model. We can persist an Experiment across multiple inference calls using Gradio's [State](https://www.gradio.app/docs/state) object. This will allow you to log multiple inferences from a model to a single Experiment.\n\n```python\nimport comet_ml\nimport gradio as gr\nimport shap\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nif torch.cuda.is_available():\n    device = \"cuda\"\nelse:\n    device = \"cpu\"\n\nMODEL_NAME = \"gpt2\"\n\nmodel = AutoModelForCausalLM.from_pretrained(MODEL_NAME)\n\nset model decoder to true\nmodel.config.is_decoder = True\nset text-generation params under task_specific_params\nmodel.config.task_specific_params[\"text-generation\"] = {\n    \"do_sample\": True,\n    \"max_length\": 50,\n    \"temperature\": 0.7,\n    \"top_k\": 50,\n    \"no_repeat_ngram_size\": 2,\n}\nmodel = model.to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\nexplainer = shap.Explainer(model, tokenizer)\n\n\ndef start_experiment():\n    \"\"\"Returns an APIExperiment object that is thread safe\n    and can be used to log inferences to a single Experiment\n    \"\"\"\n    try:\n        api = comet_ml.API()\n        workspace = api.get_default_", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "  \"\"\"Returns an APIExperiment object that is thread safe\n    and can be used to log inferences to a single Experiment\n    \"\"\"\n    try:\n        api = comet_ml.API()\n        workspace = api.get_default_workspace()\n        project_name = comet_ml.config.get_config()[\"comet.project_name\"]\n\n        experiment = comet_ml.APIExperiment(\n            workspace=workspace, project_name=project_name\n        )\n        experiment.log_other(\"Created from\", \"gradio-inference\")\n\n        message = f\"Started Experiment: [{experiment.name}]({experiment.url})\"\n\n        return (experiment, message)\n\n    except Exception as e:\n        return None, None\n\n\ndef predict(text, state, message):\n    experiment = state\n\n    shap_values = explainer([text])\n    plot = shap.plots.text(shap_values, display=False)\n\n    if experiment is not None:\n        experiment.log_other(\"message\", message)\n        experiment.log_html(plot)\n\n    return plot\n\n\nwith gr.Blocks() as demo:\n    start_experiment_btn = gr.Button(\"Start New Experiment\")\n    experiment_status = gr.Markdown()\n\n    Log a message to the Experiment to provide more context\n    experiment_message = gr.Textbox(label=\"Experiment Message\")\n    experiment = gr.State()\n\n    input_text = gr.Textbox(label=\"Input Text\", lines=5, interactive=True)\n    submit_btn = gr.Button(\"Submit\")\n\n    output = gr.HTML(interactive=True)\n\n    start_experiment_btn.click(\n        start_experiment, outputs=[experiment, experiment_status]\n    )\n    submit_btn.click(\n        predict, inputs=[input_text, experiment, experiment_message], outputs=[output]\n    )\n```\n\nInferences from this snippet will be saved in the HTML tab of your experiment.\n\n<video width=\"560\" height=\"315\" controls>\n    <source src=\"https://user-images.githubusercontent.com/7529846/214328610-466e5c81-4814-49b9-887c-065aca14dd30.mp4\"></source>\n</video>\n\n", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "887c-065aca14dd30.mp4\"></source>\n</video>\n\n", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "We hope you found this guide useful and that it provides some inspiration to help you build awesome model evaluation workflows with Comet and Gradio.\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "- Create an account on Hugging Face [here](https://huggingface.co/join).\n- Add Gradio Demo under your username, see this [course](https://huggingface.co/course/chapter9/4?fw=pt) for setting up Gradio Demo on Hugging Face.\n- Request to join the Comet organization [here](https://huggingface.co/Comet).\n\n", "heading1": "How to contribute Gradio demos on HF spaces on the Comet organization", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "- [Comet Documentation](https://www.comet.com/docs/v2/?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs)\n", "heading1": "Additional Resources", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "Gradio features a built-in theming engine that lets you customize the look and feel of your app. You can choose from a variety of themes, or create your own. To do so, pass the `theme=` kwarg to the `Blocks` or `Interface` constructor. For example:\n\n```python\nwith gr.Blocks(theme=gr.themes.Soft()) as demo:\n    ...\n```\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-soft.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nGradio comes with a set of prebuilt themes which you can load from `gr.themes.*`. These are:\n\n\n* `gr.themes.Base()` - the `\"base\"` theme sets the primary color to blue but otherwise has minimal styling, making it particularly useful as a base for creating new, custom themes.\n* `gr.themes.Default()` - the `\"default\"` Gradio 5 theme, with a vibrant orange primary color and gray secondary color.\n* `gr.themes.Origin()` - the `\"origin\"` theme is most similar to Gradio 4 styling. Colors, especially in light mode, are more subdued than the Gradio 5 default theme.\n* `gr.themes.Citrus()` - the `\"citrus\"` theme uses a yellow primary color, highlights form elements that are in focus, and includes fun 3D effects when buttons are clicked.\n* `gr.themes.Monochrome()` - the `\"monochrome\"` theme uses a black primary and white secondary color, and uses serif-style fonts, giving the appearance of a black-and-white newspaper. \n* `gr.themes.Soft()` - the `\"soft\"` theme uses a purple primary color and white secondary color. It also increases the border radius around buttons and form elements and highlights labels.\n* `gr.themes.Glass()` - the `\"glass\"` theme has a blue primary color and a transclucent gray secondary color. The theme also uses vertical gradients to create a glassy effect.\n* `gr.themes.Ocean()` - the `\"ocean\"` theme has a blue-green primary color and gray secondary color. The theme also uses horizontal gradients, especially for buttons and some form elements.\n\n\nEach of these themes set values for hundreds of CSS variables. You can use preb", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "lor and gray secondary color. The theme also uses horizontal gradients, especially for buttons and some form elements.\n\n\nEach of these themes set values for hundreds of CSS variables. You can use prebuilt themes as a starting point for your own custom themes, or you can create your own themes from scratch. Let's take a look at each approach.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "The easiest way to build a theme is using the Theme Builder. To launch the Theme Builder locally, run the following code:\n\n```python\nimport gradio as gr\n\ngr.themes.builder()\n```\n\n$demo_theme_builder\n\nYou can use the Theme Builder running on Spaces above, though it runs much faster when you launch it locally via `gr.themes.builder()`.\n\nAs you edit the values in the Theme Builder, the app will preview updates in real time. You can download the code to generate the theme you've created so you can use it in any Gradio app.\n\nIn the rest of the guide, we will cover building themes programmatically.\n\n", "heading1": "Using the Theme Builder", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Although each theme has hundreds of CSS variables, the values for most these variables are drawn from 8 core variables which can be set through the constructor of each prebuilt theme. Modifying these 8 arguments allows you to quickly change the look and feel of your app.\n\nCore Colors\n\nThe first 3 constructor arguments set the colors of the theme and are `gradio.themes.Color` objects. Internally, these Color objects hold brightness values for the palette of a single hue, ranging from 50, 100, 200..., 800, 900, 950. Other CSS variables are derived from these 3 colors.\n\nThe 3 color constructor arguments are:\n\n- `primary_hue`: This is the color draws attention in your theme. In the default theme, this is set to `gradio.themes.colors.orange`.\n- `secondary_hue`: This is the color that is used for secondary elements in your theme. In the default theme, this is set to `gradio.themes.colors.blue`.\n- `neutral_hue`: This is the color that is used for text and other neutral elements in your theme. In the default theme, this is set to `gradio.themes.colors.gray`.\n\nYou could modify these values using their string shortcuts, such as\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(primary_hue=\"red\", secondary_hue=\"pink\")) as demo:\n    ...\n```\n\nor you could use the `Color` objects directly, like this:\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.pink)) as demo:\n    ...\n```\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-extended-step-1.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nPredefined colors are:\n\n- `slate`\n- `gray`\n- `zinc`\n- `neutral`\n- `stone`\n- `red`\n- `orange`\n- `amber`\n- `yellow`\n- `lime`\n- `green`\n- `emerald`\n- `teal`\n- `cyan`\n- `sky`\n- `blue`\n- `indigo`\n- `violet`\n- `purple`\n- `fuchsia`\n- `pink`\n- `rose`\n\nYou could also create your own custom `Color` objects and pass them in.\n\nCore Sizing\n\nThe next 3 constructor arguments set the sizing of the theme and are `gradio.", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "`\n- `fuchsia`\n- `pink`\n- `rose`\n\nYou could also create your own custom `Color` objects and pass them in.\n\nCore Sizing\n\nThe next 3 constructor arguments set the sizing of the theme and are `gradio.themes.Size` objects. Internally, these Size objects hold pixel size values that range from `xxs` to `xxl`. Other CSS variables are derived from these 3 sizes.\n\n- `spacing_size`: This sets the padding within and spacing between elements. In the default theme, this is set to `gradio.themes.sizes.spacing_md`.\n- `radius_size`: This sets the roundedness of corners of elements. In the default theme, this is set to `gradio.themes.sizes.radius_md`.\n- `text_size`: This sets the font size of text. In the default theme, this is set to `gradio.themes.sizes.text_md`.\n\nYou could modify these values using their string shortcuts, such as\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(spacing_size=\"sm\", radius_size=\"none\")) as demo:\n    ...\n```\n\nor you could use the `Size` objects directly, like this:\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(spacing_size=gr.themes.sizes.spacing_sm, radius_size=gr.themes.sizes.radius_none)) as demo:\n    ...\n```\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-extended-step-2.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nThe predefined size objects are:\n\n- `radius_none`\n- `radius_sm`\n- `radius_md`\n- `radius_lg`\n- `spacing_sm`\n- `spacing_md`\n- `spacing_lg`\n- `text_sm`\n- `text_md`\n- `text_lg`\n\nYou could also create your own custom `Size` objects and pass them in.\n\nCore Fonts\n\nThe final 2 constructor arguments set the fonts of the theme. You can pass a list of fonts to each of these arguments to specify fallbacks. If you provide a string, it will be loaded as a system font. If you provide a `gradio.themes.GoogleFont`, the font will be loaded from Google Fonts.\n\n- `font`: This sets the primary font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Sans\")`.\n- `font_mono`: This sets th", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "font will be loaded from Google Fonts.\n\n- `font`: This sets the primary font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Sans\")`.\n- `font_mono`: This sets the monospace font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Mono\")`.\n\nYou could modify these values such as the following:\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont(\"Inconsolata\"), \"Arial\", \"sans-serif\"])) as demo:\n    ...\n```\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-extended-step-3.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\n", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "You can also modify the values of CSS variables after the theme has been loaded. To do so, use the `.set()` method of the theme object to get access to the CSS variables. For example:\n\n```python\ntheme = gr.themes.Default(primary_hue=\"blue\").set(\n    loader_color=\"FF0000\",\n    slider_color=\"FF0000\",\n)\n\nwith gr.Blocks(theme=theme) as demo:\n    ...\n```\n\nIn the example above, we've set the `loader_color` and `slider_color` variables to `FF0000`, despite the overall `primary_color` using the blue color palette. You can set any CSS variable that is defined in the theme in this manner.\n\nYour IDE type hinting should help you navigate these variables. Since there are so many CSS variables, let's take a look at how these variables are named and organized.\n\nCSS Variable Naming Conventions\n\nCSS variable names can get quite long, like `button_primary_background_fill_hover_dark`! However they follow a common naming convention that makes it easy to understand what they do and to find the variable you're looking for. Separated by underscores, the variable name is made up of:\n\n1. The target element, such as `button`, `slider`, or `block`.\n2. The target element type or sub-element, such as `button_primary`, or `block_label`.\n3. The property, such as `button_primary_background_fill`, or `block_label_border_width`.\n4. Any relevant state, such as `button_primary_background_fill_hover`.\n5. If the value is different in dark mode, the suffix `_dark`. For example, `input_border_color_focus_dark`.\n\nOf course, many CSS variable names are shorter than this, such as `table_border_color`, or `input_shadow`.\n\nCSS Variable Organization\n\nThough there are hundreds of CSS variables, they do not all have to have individual values. They draw their values by referencing a set of core variables and referencing each other. This allows us to only have to modify a few variables to change the look and feel of the entire theme, while also getting finer control of individual elements that we may wan", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "d referencing each other. This allows us to only have to modify a few variables to change the look and feel of the entire theme, while also getting finer control of individual elements that we may want to modify.\n\nReferencing Core Variables\n\nTo reference one of the core constructor variables, precede the variable name with an asterisk. To reference a core color, use the `*primary_`, `*secondary_`, or `*neutral_` prefix, followed by the brightness value. For example:\n\n```python\ntheme = gr.themes.Default(primary_hue=\"blue\").set(\n    button_primary_background_fill=\"*primary_200\",\n    button_primary_background_fill_hover=\"*primary_300\",\n)\n```\n\nIn the example above, we've set the `button_primary_background_fill` and `button_primary_background_fill_hover` variables to `*primary_200` and `*primary_300`. These variables will be set to the 200 and 300 brightness values of the blue primary color palette, respectively.\n\nSimilarly, to reference a core size, use the `*spacing_`, `*radius_`, or `*text_` prefix, followed by the size value. For example:\n\n```python\ntheme = gr.themes.Default(radius_size=\"md\").set(\n    button_primary_border_radius=\"*radius_xl\",\n)\n```\n\nIn the example above, we've set the `button_primary_border_radius` variable to `*radius_xl`. This variable will be set to the `xl` setting of the medium radius size range.\n\nReferencing Other Variables\n\nVariables can also reference each other. For example, look at the example below:\n\n```python\ntheme = gr.themes.Default().set(\n    button_primary_background_fill=\"FF0000\",\n    button_primary_background_fill_hover=\"FF0000\",\n    button_primary_border=\"FF0000\",\n)\n```\n\nHaving to set these values to a common color is a bit tedious. Instead, we can reference the `button_primary_background_fill` variable in the `button_primary_background_fill_hover` and `button_primary_border` variables, using a `*` prefix.\n\n```python\ntheme = gr.themes.Default().set(\n    button_primary_background_fill=\"FF0000\",\n    button_primary_back", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "mary_background_fill_hover` and `button_primary_border` variables, using a `*` prefix.\n\n```python\ntheme = gr.themes.Default().set(\n    button_primary_background_fill=\"FF0000\",\n    button_primary_background_fill_hover=\"*button_primary_background_fill\",\n    button_primary_border=\"*button_primary_background_fill\",\n)\n```\n\nNow, if we change the `button_primary_background_fill` variable, the `button_primary_background_fill_hover` and `button_primary_border` variables will automatically update as well.\n\nThis is particularly useful if you intend to share your theme - it makes it easy to modify the theme without having to change every variable.\n\nNote that dark mode variables automatically reference each other. For example:\n\n```python\ntheme = gr.themes.Default().set(\n    button_primary_background_fill=\"FF0000\",\n    button_primary_background_fill_dark=\"AAAAAA\",\n    button_primary_border=\"*button_primary_background_fill\",\n    button_primary_border_dark=\"*button_primary_background_fill_dark\",\n)\n```\n\n`button_primary_border_dark` will draw its value from `button_primary_background_fill_dark`, because dark mode always draw from the dark version of the variable.\n\n", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Let's say you want to create a theme from scratch! We'll go through it step by step - you can also see the source of prebuilt themes in the gradio source repo for reference - [here's the source](https://github.com/gradio-app/gradio/blob/main/gradio/themes/monochrome.py) for the Monochrome theme.\n\nOur new theme class will inherit from `gradio.themes.Base`, a theme that sets a lot of convenient defaults. Let's make a simple demo that creates a dummy theme called Seafoam, and make a simple app that uses it.\n\n$code_theme_new_step_1\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-new-step-1.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nThe Base theme is very barebones, and uses `gr.themes.Blue` as it primary color - you'll note the primary button and the loading animation are both blue as a result. Let's change the defaults core arguments of our app. We'll overwrite the constructor and pass new defaults for the core constructor arguments.\n\nWe'll use `gr.themes.Emerald` as our primary color, and set secondary and neutral hues to `gr.themes.Blue`. We'll make our text larger using `text_lg`. We'll use `Quicksand` as our default font, loaded from Google Fonts.\n\n$code_theme_new_step_2\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-new-step-2.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nSee how the primary button and the loading animation are now green? These CSS variables are tied to the `primary_hue` variable.\n\nLet's modify the theme a bit more directly. We'll call the `set()` method to overwrite CSS variable values explicitly. We can use any CSS logic, and reference our core constructor arguments using the `*` prefix.\n\n$code_theme_new_step_3\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-new-step-3.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nLook how fun our theme looks now! With just a few variable changes, our theme looks completely different.\n\nYou may find it helpful to explore the [source code ", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "ght\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nLook how fun our theme looks now! With just a few variable changes, our theme looks completely different.\n\nYou may find it helpful to explore the [source code of the other prebuilt themes](https://github.com/gradio-app/gradio/blob/main/gradio/themes) to see how they modified the base theme. You can also find your browser's Inspector useful to select elements from the UI and see what CSS variables are being used in the styles panel.\n\n", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Once you have created a theme, you can upload it to the HuggingFace Hub to let others view it, use it, and build off of it!\n\nUploading a Theme\n\nThere are two ways to upload a theme, via the theme class instance or the command line. We will cover both of them with the previously created `seafoam` theme.\n\n- Via the class instance\n\nEach theme instance has a method called `push_to_hub` we can use to upload a theme to the HuggingFace hub.\n\n```python\nseafoam.push_to_hub(repo_name=\"seafoam\",\n                    version=\"0.0.1\",\n\t\t\t\t\thf_token=\"<token>\")\n```\n\n- Via the command line\n\nFirst save the theme to disk\n\n```python\nseafoam.dump(filename=\"seafoam.json\")\n```\n\nThen use the `upload_theme` command:\n\n```bash\nupload_theme\\\n\"seafoam.json\"\\\n\"seafoam\"\\\n--version \"0.0.1\"\\\n--hf_token \"<token>\"\n```\n\nIn order to upload a theme, you must have a HuggingFace account and pass your [Access Token](https://huggingface.co/docs/huggingface_hub/quick-startlogin)\nas the `hf_token` argument. However, if you log in via the [HuggingFace command line](https://huggingface.co/docs/huggingface_hub/quick-startlogin) (which comes installed with `gradio`),\nyou can omit the `hf_token` argument.\n\nThe `version` argument lets you specify a valid [semantic version](https://www.geeksforgeeks.org/introduction-semantic-versioning/) string for your theme.\nThat way your users are able to specify which version of your theme they want to use in their apps. This also lets you publish updates to your theme without worrying\nabout changing how previously created apps look. The `version` argument is optional. If omitted, the next patch version is automatically applied.\n\nTheme Previews\n\nBy calling `push_to_hub` or `upload_theme`, the theme assets will be stored in a [HuggingFace space](https://huggingface.co/docs/hub/spaces-overview).\n\nFor example, the theme preview for the calm seafoam theme is here: [calm seafoam preview](https://huggingface.co/spaces/shivalikasingh/calm_seafoam).\n\n<div class=\"wrapper\">\n<ifr", "heading1": "Sharing Themes", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "hub/spaces-overview).\n\nFor example, the theme preview for the calm seafoam theme is here: [calm seafoam preview](https://huggingface.co/spaces/shivalikasingh/calm_seafoam).\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://shivalikasingh-calm-seafoam.hf.space/?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nDiscovering Themes\n\nThe [Theme Gallery](https://huggingface.co/spaces/gradio/theme-gallery) shows all the public gradio themes. After publishing your theme,\nit will automatically show up in the theme gallery after a couple of minutes.\n\nYou can sort the themes by the number of likes on the space and from most to least recently created as well as toggling themes between light and dark mode.\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-gallery.static.hf.space\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nDownloading\n\nTo use a theme from the hub, use the `from_hub` method on the `ThemeClass` and pass it to your app:\n\n```python\nmy_theme = gr.Theme.from_hub(\"gradio/seafoam\")\n\nwith gr.Blocks(theme=my_theme) as demo:\n    ....\n```\n\nYou can also pass the theme string directly to `Blocks` or `Interface` (`gr.Blocks(theme=\"gradio/seafoam\")`)\n\nYou can pin your app to an upstream theme version by using semantic versioning expressions.\n\nFor example, the following would ensure the theme we load from the `seafoam` repo was between versions `0.0.1` and `0.1.0`:\n\n```python\nwith gr.Blocks(theme=\"gradio/seafoam@>=0.0.1,<0.1.0\") as demo:\n    ....\n```\n\nEnjoy creating your own themes! If you make one you're proud of, please share it with the world by uploading it to the hub!\nIf you tag us on [Twitter](https://twitter.com/gradio) we can give your theme a shout out!\n\n<style>\n.wrapper {\n    position: relative;\n    padding-bottom: 56.25%;\n    padding-top: 25px;\n    height: 0;\n}\n.wrapper iframe {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n}\n</style>\n", "heading1": "Sharing Themes", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "er iframe {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n}\n</style>\n", "heading1": "Sharing Themes", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "By default, every Gradio demo includes a built-in queuing system that scales to thousands of requests. When a user of your app submits a request (i.e. submits an input to your function), Gradio adds the request to the queue, and requests are processed in order, generally speaking (this is not exactly true, as discussed below). When the user's request has finished processing, the Gradio server returns the result back to the user using server-side events (SSE). The SSE protocol has several advantages over simply using HTTP POST requests: \n\n(1) They do not time out -- most browsers raise a timeout error if they do not get a response to a POST request after a short period of time (e.g. 1 min). This can be a problem if your inference function takes longer than 1 minute to run or if many people are trying out your demo at the same time, resulting in increased latency.\n\n(2) They allow the server to send multiple updates to the frontend. This means, for example, that the server can send a real-time ETA of how long your prediction will take to complete.\n\nTo configure the queue, simply call the `.queue()` method before launching an `Interface`, `TabbedInterface`, `ChatInterface` or any `Blocks`. Here's an example:\n\n```py\nimport gradio as gr\n\napp = gr.Interface(lambda x:x, \"image\", \"image\")\napp.queue()  <-- Sets up a queue with default parameters\napp.launch()\n```\n\n**How Requests are Processed from the Queue**\n\nWhen a Gradio server is launched, a pool of threads is used to execute requests from the queue. By default, the maximum size of this thread pool is `40` (which is the default inherited from FastAPI, on which the Gradio server is based). However, this does *not* mean that 40 requests are always processed in parallel from the queue. \n\nInstead, Gradio uses a **single-function-single-worker** model by default. This means that each worker thread is only assigned a single function from among all of the functions that could be part of your Gradio app. This ensures that you do", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "-single-worker** model by default. This means that each worker thread is only assigned a single function from among all of the functions that could be part of your Gradio app. This ensures that you do not see, for example, out-of-memory errors, due to multiple workers calling a machine learning model at the same time. Suppose you have 3 functions in your Gradio app: A, B, and C. And you see the following sequence of 7 requests come in from users using your app:\n\n```\n1 2 3 4 5 6 7\n-------------\nA B A A C B A\n```\n\nInitially, 3 workers will get dispatched to handle requests 1, 2, and 5 (corresponding to functions: A, B, C). As soon as any of these workers finish, they will start processing the next function in the queue of the same function type, e.g. the worker that finished processing request 1 will start processing request 3, and so on.\n\nIf you want to change this behavior, there are several parameters that can be used to configure the queue and help reduce latency. Let's go through them one-by-one.\n\n\nThe `default_concurrency_limit` parameter in `queue()`\n\nThe first parameter we will explore is the `default_concurrency_limit` parameter in `queue()`. This controls how many workers can execute the same event. By default, this is set to `1`, but you can set it to a higher integer: `2`, `10`, or even `None` (in the last case, there is no limit besides the total number of available workers). \n\nThis is useful, for example, if your Gradio app does not call any resource-intensive functions. If your app only queries external APIs, then you can set the `default_concurrency_limit` much higher. Increasing this parameter can **linearly multiply the capacity of your server to handle requests**.\n\nSo why not set this parameter much higher all the time? Keep in mind that since requests are processed in parallel, each request will consume memory to store the data and weights for processing. This means that you might get out-of-memory errors if you increase the `default_concurrenc", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "sts are processed in parallel, each request will consume memory to store the data and weights for processing. This means that you might get out-of-memory errors if you increase the `default_concurrency_limit` too high. You may also start to get diminishing returns if the `default_concurrency_limit` is too high because of costs of switching between different worker threads.\n\n**Recommendation**: Increase the `default_concurrency_limit` parameter as high as you can while you continue to see performance gains or until you hit memory limits on your machine. You can [read about Hugging Face Spaces machine specs here](https://huggingface.co/docs/hub/spaces-overview).\n\n\nThe `concurrency_limit` parameter in events\n\nYou can also set the number of requests that can be processed in parallel for each event individually. These take priority over the  `default_concurrency_limit` parameter described previously.\n\nTo do this, set the `concurrency_limit` parameter of any event listener, e.g. `btn.click(..., concurrency_limit=20)` or in the `Interface` or `ChatInterface` classes: e.g. `gr.Interface(..., concurrency_limit=20)`. By default, this parameter is set to the global `default_concurrency_limit`.\n\n\nThe `max_threads` parameter in `launch()`\n\nIf your demo uses non-async functions, e.g. `def` instead of `async def`, they will be run in a threadpool. This threadpool has a size of 40 meaning that only 40 threads can be created to run your non-async functions. If you are running into this limit, you can increase the threadpool size with `max_threads`. The default value is 40.\n\nTip: You should use async functions whenever possible to increase the number of concurrent requests your app can handle. Quick functions that are not CPU-bound are good candidates to be written as `async`. This [guide](https://fastapi.tiangolo.com/async/) is a good primer on the concept.\n\n\nThe `max_size` parameter in `queue()`\n\nA more blunt way to reduce the wait times is simply to prevent too many pe", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "is [guide](https://fastapi.tiangolo.com/async/) is a good primer on the concept.\n\n\nThe `max_size` parameter in `queue()`\n\nA more blunt way to reduce the wait times is simply to prevent too many people from joining the queue in the first place. You can set the maximum number of requests that the queue processes using the `max_size` parameter of `queue()`. If a request arrives when the queue is already of the maximum size, it will not be allowed to join the queue and instead, the user will receive an error saying that the queue is full and to try again. By default, `max_size=None`, meaning that there is no limit to the number of users that can join the queue.\n\nParadoxically, setting a `max_size` can often improve user experience because it prevents users from being dissuaded by very long queue wait times. Users who are more interested and invested in your demo will keep trying to join the queue, and will be able to get their results faster.\n\n**Recommendation**: For a better user experience, set a `max_size` that is reasonable given your expectations of how long users might be willing to wait for a prediction.\n\nThe `max_batch_size` parameter in events\n\nAnother way to increase the parallelism of your Gradio demo is to write your function so that it can accept **batches** of inputs. Most deep learning models can process batches of samples more efficiently than processing individual samples.\n\nIf you write your function to process a batch of samples, Gradio will automatically batch incoming requests together and pass them into your function as a batch of samples. You need to set `batch` to `True` (by default it is `False`) and set a `max_batch_size` (by default it is `4`) based on the maximum number of samples your function is able to handle. These two parameters can be passed into `gr.Interface()` or to an event in Blocks such as `.click()`.\n\nWhile setting a batch is conceptually similar to having workers process requests in parallel, it is often _faster_ than set", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "e passed into `gr.Interface()` or to an event in Blocks such as `.click()`.\n\nWhile setting a batch is conceptually similar to having workers process requests in parallel, it is often _faster_ than setting the `concurrency_count` for deep learning models. The downside is that you might need to adapt your function a little bit to accept batches of samples instead of individual samples.\n\nHere's an example of a function that does _not_ accept a batch of inputs -- it processes a single input at a time:\n\n```py\nimport time\n\ndef trim_words(word, length):\n    return word[:int(length)]\n\n```\n\nHere's the same function rewritten to take in a batch of samples:\n\n```py\nimport time\n\ndef trim_words(words, lengths):\n    trimmed_words = []\n    for w, l in zip(words, lengths):\n        trimmed_words.append(w[:int(l)])\n    return [trimmed_words]\n\n```\n\nThe second function can be used with `batch=True` and an appropriate `max_batch_size` parameter.\n\n**Recommendation**: If possible, write your function to accept batches of samples, and then set `batch` to `True` and the `max_batch_size` as high as possible based on your machine's memory limits.\n\n", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "If you have done everything above, and your demo is still not fast enough, you can upgrade the hardware that your model is running on. Changing the model from running on CPUs to running on GPUs will usually provide a 10x-50x increase in inference time for deep learning models.\n\nIt is particularly straightforward to upgrade your Hardware on Hugging Face Spaces. Simply click on the \"Settings\" tab in your Space and choose the Space Hardware you'd like.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/spaces-gpu-settings.png)\n\nWhile you might need to adapt portions of your machine learning inference code to run on a GPU (here's a [handy guide](https://cnvrg.io/pytorch-cuda/) if you are using PyTorch), Gradio is completely agnostic to the choice of hardware and will work completely fine if you use it with CPUs, GPUs, TPUs, or any other hardware!\n\nNote: your GPU memory is different than your CPU memory, so if you upgrade your hardware,\nyou might need to adjust the value of the `default_concurrency_limit` parameter described above.\n\n", "heading1": "Upgrading your Hardware (GPUs, TPUs, etc.)", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "Congratulations! You know how to set up a Gradio demo for maximum performance. Good luck on your next viral demo!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "Gradio features [blocks](https://www.gradio.app/docs/blocks) to easily layout applications. To use this feature, you need to stack or nest layout components and create a hierarchy with them. This isn't difficult to implement and maintain for small projects, but after the project gets more complex, this component hierarchy becomes difficult to maintain and reuse.\n\nIn this guide, we are going to explore how we can wrap the layout classes to create more maintainable and easy-to-read applications without sacrificing flexibility.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "We are going to follow the implementation from this Huggingface Space example:\n\n<gradio-app\nspace=\"gradio/wrapping-layouts\">\n</gradio-app>\n\n", "heading1": "Example", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "The wrapping utility has two important classes. The first one is the ```LayoutBase``` class and the other one is the ```Application``` class.\n\nWe are going to look at the ```render``` and ```attach_event``` functions of them for brevity. You can look at the full implementation from [the example code](https://huggingface.co/spaces/WoWoWoWololo/wrapping-layouts/blob/main/app.py).\n\nSo let's start with the ```LayoutBase``` class.\n\nLayoutBase Class\n\n1. Render Function\n\n    Let's look at the ```render``` function in the ```LayoutBase``` class:\n\n```python\nother LayoutBase implementations\n\ndef render(self) -> None:\n    with self.main_layout:\n        for renderable in self.renderables:\n            renderable.render()\n\n    self.main_layout.render()\n```\nThis is a little confusing at first but if you consider the default implementation you can understand it easily.\nLet's look at an example:\n\nIn the default implementation, this is what we're doing:\n\n```python\nwith Row():\n    left_textbox = Textbox(value=\"left_textbox\")\n    right_textbox = Textbox(value=\"right_textbox\")\n```\n\nNow, pay attention to the Textbox variables. These variables' ```render``` parameter is true by default. So as we use the ```with``` syntax and create these variables, they are calling the ```render``` function under the ```with``` syntax.\n\nWe know the render function is called in the constructor with the implementation from the ```gradio.blocks.Block``` class:\n\n```python\nclass Block:\n    constructor parameters are omitted for brevity\n    def __init__(self, ...):\n        other assign functions \n\n        if render:\n            self.render()\n```\n\nSo our implementation looks like this:\n\n```python\nself.main_layout -> Row()\nwith self.main_layout:\n    left_textbox.render()\n    right_textbox.render()\n```\n\nWhat this means is by calling the components' render functions under the ```with``` syntax, we are actually simulating the default implementation.\n\nSo now let's consider two nested ```with```s to see ho", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "at this means is by calling the components' render functions under the ```with``` syntax, we are actually simulating the default implementation.\n\nSo now let's consider two nested ```with```s to see how the outer one works. For this, let's expand our example with the ```Tab``` component:\n\n```python\nwith Tab():\n    with Row():\n        first_textbox = Textbox(value=\"first_textbox\")\n        second_textbox = Textbox(value=\"second_textbox\")\n```\n\nPay attention to the Row and Tab components this time. We have created the Textbox variables above and added them to Row with the ```with``` syntax. Now we need to add the Row component to the Tab component. You can see that the Row component is created with default parameters, so its render parameter is true, that's why the render function is going to be executed under the Tab component's ```with``` syntax.\n\nTo mimic this implementation, we need to call the ```render``` function of the ```main_layout``` variable after the ```with``` syntax of the ```main_layout``` variable.\n\nSo the implementation looks like this:\n\n```python\nwith tab_main_layout:\n    with row_main_layout:\n        first_textbox.render()\n        second_textbox.render()\n\n    row_main_layout.render()\n\ntab_main_layout.render()\n```\n\nThe default implementation and our implementation are the same, but we are using the render function ourselves. So it requires a little work.\n\nNow, let's take a look at the ```attach_event``` function.\n\n2. Attach Event Function\n\n    The function is left as not implemented because it is specific to the class, so each class has to implement its `attach_event` function.\n\n```python\n    other LayoutBase implementations\n\n    def attach_event(self, block_dict: Dict[str, Block]) -> None:\n        raise NotImplementedError\n```\n\nCheck out the ```block_dict``` variable in the ```Application``` class's ```attach_event``` function.\n\nApplication Class\n\n1. Render Function\n\n```python\n    other Application implementations\n\n    def _render(self):\n     ", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "ct``` variable in the ```Application``` class's ```attach_event``` function.\n\nApplication Class\n\n1. Render Function\n\n```python\n    other Application implementations\n\n    def _render(self):\n        with self.app:\n            for child in self.children:\n                child.render()\n\n        self.app.render()\n```\n\nFrom the explanation of the ```LayoutBase``` class's ```render``` function, we can understand the ```child.render``` part.\n\nSo let's look at the bottom part, why are we calling the ```app``` variable's ```render``` function? It's important to call this function because if we look at the implementation in the ```gradio.blocks.Blocks``` class, we can see that it is adding the components and event functions into the root component. To put it another way, it is creating and structuring the gradio application.\n\n2. Attach Event Function\n\n    Let's see how we can attach events to components:\n\n```python\n    other Application implementations\n\n    def _attach_event(self):\n        block_dict: Dict[str, Block] = {}\n\n        for child in self.children:\n            block_dict.update(child.global_children_dict)\n\n        with self.app:\n            for child in self.children:\n                try:\n                    child.attach_event(block_dict=block_dict)\n                except NotImplementedError:\n                    print(f\"{child.name}'s attach_event is not implemented\")\n```\n\nYou can see why the ```global_children_list``` is used in the ```LayoutBase``` class from the example code. With this, all the components in the application are gathered into one dictionary, so the component can access all the components with their names.\n\nThe ```with``` syntax is used here again to attach events to components. If we look at the ```__exit__``` function in the ```gradio.blocks.Blocks``` class, we can see that it is calling the ```attach_load_events``` function which is used for setting event triggers to components. So we have to use the ```with``` syntax to trigger the ```_", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "Blocks``` class, we can see that it is calling the ```attach_load_events``` function which is used for setting event triggers to components. So we have to use the ```with``` syntax to trigger the ```__exit__``` function.\n\nOf course, we can call ```attach_load_events``` without using the ```with``` syntax, but the function needs a ```Context.root_block```, and it is set in the ```__enter__``` function. So we used the ```with``` syntax here rather than calling the function ourselves.\n\n", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "In this guide, we saw\n\n- How we can wrap the layouts\n- How components are rendered\n- How we can structure our application with wrapped layout classes\n\nBecause the classes used in this guide are used for demonstration purposes, they may still not be totally optimized or modular. But that would make the guide much longer!\n\nI hope this guide helps you gain another view of the layout classes and gives you an idea about how you can use them for your needs. See the full implementation of our example [here](https://huggingface.co/spaces/WoWoWoWololo/wrapping-layouts/blob/main/app.py).\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "**[OpenAPI](https://www.openapis.org/)** is a widely adopted standard for describing RESTful APIs in a machine-readable format, typically as a JSON  file. \n\nYou can create a Gradio UI from an OpenAPI Spec **in 1 line of Python**, instantly generating an interactive web interface for any API, making it accessible for demos, testing, or sharing with non-developers, without writing custom frontend code.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/from-openapi-spec", "source_page_title": "Other Tutorials - From Openapi Spec Guide"}, {"text": "Gradio now provides a convenient function, `gr.load_openapi`, that can automatically generate a Gradio app from an OpenAPI v3 specification. This function parses the spec, creates UI components for each endpoint and parameter, and lets you interact with the API directly from your browser.\n\nHere's a minimal example:\n\n```python\nimport gradio as gr\n\ndemo = gr.load_openapi(\n    openapi_spec=\"https://petstore3.swagger.io/api/v3/openapi.json\",\n    base_url=\"https://petstore3.swagger.io/api/v3\",\n    paths=[\"/pet.*\"],\n    methods=[\"get\", \"post\"],\n)\n\ndemo.launch()\n```\n\n**Parameters:**\n- **openapi_spec**: URL, file path, or Python dictionary containing the OpenAPI v3 spec (JSON format only).\n- **base_url**: The base URL for the API endpoints (e.g., `https://api.example.com/v1`).\n- **paths** (optional): List of endpoint path patterns (supports regex) to include. If not set, all paths are included.\n- **methods** (optional): List of HTTP methods (e.g., `[\"get\", \"post\"]`) to include. If not set, all methods are included.\n\nThe generated app will display a sidebar with available endpoints and create interactive forms for each operation, letting you make API calls and view responses in real time.\n\n", "heading1": "How it works", "source_page_url": "https://gradio.app/guides/from-openapi-spec", "source_page_title": "Other Tutorials - From Openapi Spec Guide"}, {"text": "Once your Gradio app is running, you can share the URL with others so they can try out the API through a friendly web interface\u2014no code required. For even more power, you can launch the app as an MCP (Model Control Protocol) server using [Gradio's MCP integration](https://www.gradio.app/guides/building-mcp-server-with-gradio), enabling programmatic access and orchestration of your API via the MCP ecosystem. This makes it easy to build, share, and automate API workflows with minimal effort.\n\n", "heading1": "Next steps", "source_page_url": "https://gradio.app/guides/from-openapi-spec", "source_page_title": "Other Tutorials - From Openapi Spec Guide"}, {"text": "When you demo a machine learning model, you might want to collect data from users who try the model, particularly data points in which the model is not behaving as expected. Capturing these \"hard\" data points is valuable because it allows you to improve your machine learning model and make it more reliable and robust.\n\nGradio simplifies the collection of this data by including a **Flag** button with every `Interface`. This allows a user or tester to easily send data back to the machine where the demo is running. In this Guide, we discuss more about how to use the flagging feature, both with `gradio.Interface` as well as with `gradio.Blocks`.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Flagging with Gradio's `Interface` is especially easy. By default, underneath the output components, there is a button marked **Flag**. When a user testing your model sees input with interesting output, they can click the flag button to send the input and output data back to the machine where the demo is running. The sample is saved to a CSV log file (by default). If the demo involves images, audio, video, or other types of files, these are saved separately in a parallel directory and the paths to these files are saved in the CSV file.\n\nThere are [four parameters](https://gradio.app/docs/interfaceinitialization) in `gradio.Interface` that control how flagging works. We will go over them in greater detail.\n\n- `flagging_mode`: this parameter can be set to either `\"manual\"` (default), `\"auto\"`, or `\"never\"`.\n  - `manual`: users will see a button to flag, and samples are only flagged when the button is clicked.\n  - `auto`: users will not see a button to flag, but every sample will be flagged automatically.\n  - `never`: users will not see a button to flag, and no sample will be flagged.\n- `flagging_options`: this parameter can be either `None` (default) or a list of strings.\n  - If `None`, then the user simply clicks on the **Flag** button and no additional options are shown.\n  - If a list of strings are provided, then the user sees several buttons, corresponding to each of the strings that are provided. For example, if the value of this parameter is `[\"Incorrect\", \"Ambiguous\"]`, then buttons labeled **Flag as Incorrect** and **Flag as Ambiguous** appear. This only applies if `flagging_mode` is `\"manual\"`.\n  - The chosen option is then logged along with the input and output.\n- `flagging_dir`: this parameter takes a string.\n  - It represents what to name the directory where flagged data is stored.\n- `flagging_callback`: this parameter takes an instance of a subclass of the `FlaggingCallback` class\n  - Using this parameter allows you to write custom code that gets run whe", "heading1": "The **Flag** button in `gradio.Interface`", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "flagged data is stored.\n- `flagging_callback`: this parameter takes an instance of a subclass of the `FlaggingCallback` class\n  - Using this parameter allows you to write custom code that gets run when the flag button is clicked\n  - By default, this is set to an instance of `gr.JSONLogger`\n\n", "heading1": "The **Flag** button in `gradio.Interface`", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Within the directory provided by the `flagging_dir` argument, a JSON file will log the flagged data.\n\nHere's an example: The code below creates the calculator interface embedded below it:\n\n```python\nimport gradio as gr\n\n\ndef calculator(num1, operation, num2):\n    if operation == \"add\":\n        return num1 + num2\n    elif operation == \"subtract\":\n        return num1 - num2\n    elif operation == \"multiply\":\n        return num1 * num2\n    elif operation == \"divide\":\n        return num1 / num2\n\n\niface = gr.Interface(\n    calculator,\n    [\"number\", gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]), \"number\"],\n    \"number\",\n    flagging_mode=\"manual\"\n)\n\niface.launch()\n```\n\n<gradio-app space=\"gradio/calculator-flagging-basic\"></gradio-app>\n\nWhen you click the flag button above, the directory where the interface was launched will include a new flagged subfolder, with a csv file inside it. This csv file includes all the data that was flagged.\n\n```directory\n+-- flagged/\n|   +-- logs.csv\n```\n\n_flagged/logs.csv_\n\n```csv\nnum1,operation,num2,Output,timestamp\n5,add,7,12,2022-01-31 11:40:51.093412\n6,subtract,1.5,4.5,2022-01-31 03:25:32.023542\n```\n\nIf the interface involves file data, such as for Image and Audio components, folders will be created to store those flagged data as well. For example an `image` input to `image` output interface will create the following structure.\n\n```directory\n+-- flagged/\n|   +-- logs.csv\n|   +-- image/\n|   |   +-- 0.png\n|   |   +-- 1.png\n|   +-- Output/\n|   |   +-- 0.png\n|   |   +-- 1.png\n```\n\n_flagged/logs.csv_\n\n```csv\nim,Output timestamp\nim/0.png,Output/0.png,2022-02-04 19:49:58.026963\nim/1.png,Output/1.png,2022-02-02 10:40:51.093412\n```\n\nIf you wish for the user to provide a reason for flagging, you can pass a list of strings to the `flagging_options` argument of Interface. Users will have to select one of these choices when flagging, and the option will be saved as an additional column to the CSV.\n\nIf we go back to the calculator example, the fo", "heading1": "What happens to flagged data?", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "` argument of Interface. Users will have to select one of these choices when flagging, and the option will be saved as an additional column to the CSV.\n\nIf we go back to the calculator example, the following code will create the interface embedded below it.\n\n```python\niface = gr.Interface(\n    calculator,\n    [\"number\", gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]), \"number\"],\n    \"number\",\n    flagging_mode=\"manual\",\n    flagging_options=[\"wrong sign\", \"off by one\", \"other\"]\n)\n\niface.launch()\n```\n\n<gradio-app space=\"gradio/calculator-flagging-options\"></gradio-app>\n\nWhen users click the flag button, the csv file will now include a column indicating the selected option.\n\n_flagged/logs.csv_\n\n```csv\nnum1,operation,num2,Output,flag,timestamp\n5,add,7,-12,wrong sign,2022-02-04 11:40:51.093412\n6,subtract,1.5,3.5,off by one,2022-02-04 11:42:32.062512\n```\n\n", "heading1": "What happens to flagged data?", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "What about if you are using `gradio.Blocks`? On one hand, you have even more flexibility\nwith Blocks -- you can write whatever Python code you want to run when a button is clicked,\nand assign that using the built-in events in Blocks.\n\nAt the same time, you might want to use an existing `FlaggingCallback` to avoid writing extra code.\nThis requires two steps:\n\n1. You have to run your callback's `.setup()` somewhere in the code prior to the\n   first time you flag data\n2. When the flagging button is clicked, then you trigger the callback's `.flag()` method,\n   making sure to collect the arguments correctly and disabling the typical preprocessing.\n\nHere is an example with an image sepia filter Blocks demo that lets you flag\ndata using the default `CSVLogger`:\n\n$code_blocks_flag\n$demo_blocks_flag\n\n", "heading1": "Flagging with Blocks", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Important Note: please make sure your users understand when the data they submit is being saved, and what you plan on doing with it. This is especially important when you use `flagging_mode=auto` (when all of the data submitted through the demo is being flagged)\n\nThat's all! Happy building :)\n", "heading1": "Privacy", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Gradio is a Python library that allows you to quickly create customizable web apps for your machine learning models and data processing pipelines. Gradio apps can be deployed on [Hugging Face Spaces](https://hf.space) for free.\n\nIn some cases though, you might want to deploy a Gradio app on your own web server. You might already be using [Nginx](https://www.nginx.com/), a highly performant web server, to serve your website (say `https://www.example.com`), and you want to attach Gradio to a specific subpath on your website (e.g. `https://www.example.com/gradio-demo`).\n\nIn this Guide, we will guide you through the process of running a Gradio app behind Nginx on your own web server to achieve this.\n\n**Prerequisites**\n\n1. A Linux web server with [Nginx installed](https://www.nginx.com/blog/setting-up-nginx/) and [Gradio installed](/quickstart)\n2. A working Gradio app saved as a python file on your web server\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. Start by editing the Nginx configuration file on your web server. By default, this is located at: `/etc/nginx/nginx.conf`\n\nIn the `http` block, add the following line to include server block configurations from a separate file:\n\n```bash\ninclude /etc/nginx/sites-enabled/*;\n```\n\n2. Create a new file in the `/etc/nginx/sites-available` directory (create the directory if it does not already exist), using a filename that represents your app, for example: `sudo nano /etc/nginx/sites-available/my_gradio_app`\n\n3. Paste the following into your file editor:\n\n```bash\nserver {\n    listen 80;\n    server_name example.com www.example.com;  Change this to your domain name\n\n    location /gradio-demo/ {  Change this if you'd like to server your Gradio app on a different path\n        proxy_pass http://127.0.0.1:7860/; Change this if your Gradio app will be running on a different port\n        proxy_buffering off;\n        proxy_redirect off;\n        proxy_http_version 1.1;\n        proxy_set_header Upgrade $http_upgrade;\n        proxy_set_header Connection \"upgrade\";\n        proxy_set_header Host $host;\n        proxy_set_header X-Forwarded-Host $host;\n        proxy_set_header X-Forwarded-Proto $scheme;\n    }\n}\n```\n\n\nTip: Setting the `X-Forwarded-Host` and `X-Forwarded-Proto` headers is important as Gradio uses these, in conjunction with the `root_path` parameter discussed below, to construct the public URL that your app is being served on. Gradio uses the public URL to fetch various static assets. If these headers are not set, your Gradio app may load in a broken state.\n\n*Note:* The `$host` variable does not include the host port. If you are serving your Gradio application on a raw IP address and port, you should use the `$http_host` variable instead, in these lines:\n\n```bash\n        proxy_set_header Host $host;\n        proxy_set_header X-Forwarded-Host $host;\n```\n\n", "heading1": "Editing your Nginx configuration file", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. Before you launch your Gradio app, you'll need to set the `root_path` to be the same as the subpath that you specified in your nginx configuration. This is necessary for Gradio to run on any subpath besides the root of the domain.\n\n    *Note:* Instead of a subpath, you can also provide a complete URL for `root_path` (beginning with `http` or `https`) in which case the `root_path` is treated as an absolute URL instead of a URL suffix (but in this case, you'll need to update the `root_path` if the domain changes).\n\nHere's a simple example of a Gradio app with a custom `root_path` corresponding to the Nginx configuration above.\n\n```python\nimport gradio as gr\nimport time\n\ndef test(x):\ntime.sleep(4)\nreturn x\n\ngr.Interface(test, \"textbox\", \"textbox\").queue().launch(root_path=\"/gradio-demo\")\n```\n\n2. Start a `tmux` session by typing `tmux` and pressing enter (optional)\n\nIt's recommended that you run your Gradio app in a `tmux` session so that you can keep it running in the background easily\n\n3. Then, start your Gradio app. Simply type in `python` followed by the name of your Gradio python file. By default, your app will run on `localhost:7860`, but if it starts on a different port, you will need to update the nginx configuration file above.\n\n", "heading1": "Run your Gradio app on your web server", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. If you are in a tmux session, exit by typing CTRL+B (or CMD+B), followed by the \"D\" key.\n\n2. Finally, restart nginx by running `sudo systemctl restart nginx`.\n\nAnd that's it! If you visit `https://example.com/gradio-demo` on your browser, you should see your Gradio app running there\n\n", "heading1": "Restart Nginx", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "The next generation of AI user interfaces is moving towards audio-native experiences. Users will be able to speak to chatbots and receive spoken responses in return. Several models have been built under this paradigm, including GPT-4o and [mini omni](https://github.com/gpt-omni/mini-omni).\n\nIn this guide, we'll walk you through building your own conversational chat application using mini omni as an example. You can see a demo of the finished app below:\n\n<video src=\"https://github.com/user-attachments/assets/db36f4db-7535-49f1-a2dd-bd36c487ebdf\" controls\nheight=\"600\" width=\"600\" style=\"display: block; margin: auto;\" autoplay=\"true\" loop=\"true\">\n</video>\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "Our application will enable the following user experience:\n\n1. Users click a button to start recording their message\n2. The app detects when the user has finished speaking and stops recording\n3. The user's audio is passed to the omni model, which streams back a response\n4. After omni mini finishes speaking, the user's microphone is reactivated\n5. All previous spoken audio, from both the user and omni, is displayed in a chatbot component\n\nLet's dive into the implementation details.\n\n", "heading1": "Application Overview", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "We'll stream the user's audio from their microphone to the server and determine if the user has stopped speaking on each new chunk of audio.\n\nHere's our `process_audio` function:\n\n```python\nimport numpy as np\nfrom utils import determine_pause\n\ndef process_audio(audio: tuple, state: AppState):\n    if state.stream is None:\n        state.stream = audio[1]\n        state.sampling_rate = audio[0]\n    else:\n        state.stream = np.concatenate((state.stream, audio[1]))\n\n    pause_detected = determine_pause(state.stream, state.sampling_rate, state)\n    state.pause_detected = pause_detected\n\n    if state.pause_detected and state.started_talking:\n        return gr.Audio(recording=False), state\n    return None, state\n```\n\nThis function takes two inputs:\n1. The current audio chunk (a tuple of `(sampling_rate, numpy array of audio)`)\n2. The current application state\n\nWe'll use the following `AppState` dataclass to manage our application state:\n\n```python\nfrom dataclasses import dataclass\n\n@dataclass\nclass AppState:\n    stream: np.ndarray | None = None\n    sampling_rate: int = 0\n    pause_detected: bool = False\n    stopped: bool = False\n    conversation: list = []\n```\n\nThe function concatenates new audio chunks to the existing stream and checks if the user has stopped speaking. If a pause is detected, it returns an update to stop recording. Otherwise, it returns `None` to indicate no changes.\n\nThe implementation of the `determine_pause` function is specific to the omni-mini project and can be found [here](https://huggingface.co/spaces/gradio/omni-mini/blob/eb027808c7bfe5179b46d9352e3fa1813a45f7c3/app.pyL98).\n\n", "heading1": "Processing User Audio", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "After processing the user's audio, we need to generate and stream the chatbot's response. Here's our `response` function:\n\n```python\nimport io\nimport tempfile\nfrom pydub import AudioSegment\n\ndef response(state: AppState):\n    if not state.pause_detected and not state.started_talking:\n        return None, AppState()\n    \n    audio_buffer = io.BytesIO()\n\n    segment = AudioSegment(\n        state.stream.tobytes(),\n        frame_rate=state.sampling_rate,\n        sample_width=state.stream.dtype.itemsize,\n        channels=(1 if len(state.stream.shape) == 1 else state.stream.shape[1]),\n    )\n    segment.export(audio_buffer, format=\"wav\")\n\n    with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n        f.write(audio_buffer.getvalue())\n    \n    state.conversation.append({\"role\": \"user\",\n                                \"content\": {\"path\": f.name,\n                                \"mime_type\": \"audio/wav\"}})\n    \n    output_buffer = b\"\"\n\n    for mp3_bytes in speaking(audio_buffer.getvalue()):\n        output_buffer += mp3_bytes\n        yield mp3_bytes, state\n\n    with tempfile.NamedTemporaryFile(suffix=\".mp3\", delete=False) as f:\n        f.write(output_buffer)\n    \n    state.conversation.append({\"role\": \"assistant\",\n                    \"content\": {\"path\": f.name,\n                                \"mime_type\": \"audio/mp3\"}})\n    yield None, AppState(conversation=state.conversation)\n```\n\nThis function:\n1. Converts the user's audio to a WAV file\n2. Adds the user's message to the conversation history\n3. Generates and streams the chatbot's response using the `speaking` function\n4. Saves the chatbot's response as an MP3 file\n5. Adds the chatbot's response to the conversation history\n\nNote: The implementation of the `speaking` function is specific to the omni-mini project and can be found [here](https://huggingface.co/spaces/gradio/omni-mini/blob/main/app.pyL116).\n\n", "heading1": "Generating the Response", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "Now let's put it all together using Gradio's Blocks API:\n\n```python\nimport gradio as gr\n\ndef start_recording_user(state: AppState):\n    if not state.stopped:\n        return gr.Audio(recording=True)\n\nwith gr.Blocks() as demo:\n    with gr.Row():\n        with gr.Column():\n            input_audio = gr.Audio(\n                label=\"Input Audio\", sources=\"microphone\", type=\"numpy\"\n            )\n        with gr.Column():\n            chatbot = gr.Chatbot(label=\"Conversation\", type=\"messages\")\n            output_audio = gr.Audio(label=\"Output Audio\", streaming=True, autoplay=True)\n    state = gr.State(value=AppState())\n\n    stream = input_audio.stream(\n        process_audio,\n        [input_audio, state],\n        [input_audio, state],\n        stream_every=0.5,\n        time_limit=30,\n    )\n    respond = input_audio.stop_recording(\n        response,\n        [state],\n        [output_audio, state]\n    )\n    respond.then(lambda s: s.conversation, [state], [chatbot])\n\n    restart = output_audio.stop(\n        start_recording_user,\n        [state],\n        [input_audio]\n    )\n    cancel = gr.Button(\"Stop Conversation\", variant=\"stop\")\n    cancel.click(lambda: (AppState(stopped=True), gr.Audio(recording=False)), None,\n                [state, input_audio], cancels=[respond, restart])\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\nThis setup creates a user interface with:\n- An input audio component for recording user messages\n- A chatbot component to display the conversation history\n- An output audio component for the chatbot's responses\n- A button to stop and reset the conversation\n\nThe app streams user audio in 0.5-second chunks, processes it, generates responses, and updates the conversation history accordingly.\n\n", "heading1": "Building the Gradio App", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "This guide demonstrates how to build a conversational chatbot application using Gradio and the mini omni model. You can adapt this framework to create various audio-based chatbot demos. To see the full application in action, visit the Hugging Face Spaces demo: https://huggingface.co/spaces/gradio/omni-mini\n\nFeel free to experiment with different models, audio processing techniques, or user interface designs to create your own unique conversational AI experiences!", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "First, we'll install the following requirements in our system:\n\n```\nopencv-python\ntorch\ntransformers>=4.43.0\nspaces\n```\n\nThen, we'll download the model from the Hugging Face Hub:\n\n```python\nfrom transformers import RTDetrForObjectDetection, RTDetrImageProcessor\n\nimage_processor = RTDetrImageProcessor.from_pretrained(\"PekingU/rtdetr_r50vd\")\nmodel = RTDetrForObjectDetection.from_pretrained(\"PekingU/rtdetr_r50vd\").to(\"cuda\")\n```\n\nWe're moving the model to the GPU. We'll be deploying our model to Hugging Face Spaces and running the inference in the [free ZeroGPU cluster](https://huggingface.co/zero-gpu-explorers). \n\n\n", "heading1": "Setting up the Model", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "Our inference function will accept a video and a desired confidence threshold.\nObject detection models identify many objects and assign a confidence score to each object. The lower the confidence, the higher the chance of a false positive. So we will let our users set the conference threshold.\n\nOur function will iterate over the frames in the video and run the RT-DETR model over each frame.\nWe will then draw the bounding boxes for each detected object in the frame and save the frame to a new output video.\nThe function will yield each output video in chunks of two seconds.\n\nIn order to keep inference times as low as possible on ZeroGPU (there is a time-based quota),\nwe will halve the original frames-per-second in the output video and resize the input frames to be half the original \nsize before running the model.\n\nThe code for the inference function is below - we'll go over it piece by piece.\n\n```python\nimport spaces\nimport cv2\nfrom PIL import Image\nimport torch\nimport time\nimport numpy as np\nimport uuid\n\nfrom draw_boxes import draw_bounding_boxes\n\nSUBSAMPLE = 2\n\n@spaces.GPU\ndef stream_object_detection(video, conf_threshold):\n    cap = cv2.VideoCapture(video)\n\n    This means we will output mp4 videos\n    video_codec = cv2.VideoWriter_fourcc(*\"mp4v\") type: ignore\n    fps = int(cap.get(cv2.CAP_PROP_FPS))\n\n    desired_fps = fps // SUBSAMPLE\n    width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) // 2\n    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) // 2\n\n    iterating, frame = cap.read()\n\n    n_frames = 0\n\n    Use UUID to create a unique video file\n    output_video_name = f\"output_{uuid.uuid4()}.mp4\"\n\n    Output Video\n    output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) type: ignore\n    batch = []\n\n    while iterating:\n        frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5)\n        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n        if n_frames % SUBSAMPLE == 0:\n            batch.append(frame)\n        if len(batc", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "      frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5)\n        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n        if n_frames % SUBSAMPLE == 0:\n            batch.append(frame)\n        if len(batch) == 2 * desired_fps:\n            inputs = image_processor(images=batch, return_tensors=\"pt\").to(\"cuda\")\n\n            with torch.no_grad():\n                outputs = model(**inputs)\n\n            boxes = image_processor.post_process_object_detection(\n                outputs,\n                target_sizes=torch.tensor([(height, width)] * len(batch)),\n                threshold=conf_threshold)\n            \n            for i, (array, box) in enumerate(zip(batch, boxes)):\n                pil_image = draw_bounding_boxes(Image.fromarray(array), box, model, conf_threshold)\n                frame = np.array(pil_image)\n                Convert RGB to BGR\n                frame = frame[:, :, ::-1].copy()\n                output_video.write(frame)\n\n            batch = []\n            output_video.release()\n            yield output_video_name\n            output_video_name = f\"output_{uuid.uuid4()}.mp4\"\n            output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) type: ignore\n\n        iterating, frame = cap.read()\n        n_frames += 1\n```\n\n1. **Reading from the Video**\n\nOne of the industry standards for creating videos in python is OpenCV so we will use it in this app.\n\nThe `cap` variable is how we will read from the input video. Whenever we call `cap.read()`, we are reading the next frame in the video.\n\nIn order to stream video in Gradio, we need to yield a different video file for each \"chunk\" of the output video.\nWe create the next video file to write to with the `output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height))` line. The `video_codec` is how we specify the type of video file. Only \"mp4\" and \"ts\" files are supported for video sreaming at the moment.\n\n\n2. **The Inference Loop**\n\nFor each frame i", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "dth, height))` line. The `video_codec` is how we specify the type of video file. Only \"mp4\" and \"ts\" files are supported for video sreaming at the moment.\n\n\n2. **The Inference Loop**\n\nFor each frame in the video, we will resize it to be half the size. OpenCV reads files in `BGR` format, so will convert to the expected `RGB` format of transfomers. That's what the first two lines of the while loop are doing. \n\nWe take every other frame and add it to a `batch` list so that the output video is half the original FPS. When the batch covers two seconds of video, we will run the model. The two second threshold was chosen to keep the processing time of each batch small enough so that video is smoothly displayed in the server while not requiring too many separate forward passes. In order for video streaming to work properly in Gradio, the batch size should be at least 1 second. \n\nWe run the forward pass of the model and then use the `post_process_object_detection` method of the model to scale the detected bounding boxes to the size of the input frame.\n\nWe make use of a custom function to draw the bounding boxes (source [here](https://huggingface.co/spaces/gradio/rt-detr-object-detection/blob/main/draw_boxes.pyL14)). We then have to convert from `RGB` to `BGR` before writing back to the output video.\n\nOnce we have finished processing the batch, we create a new output video file for the next batch.\n\n", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "The UI code is pretty similar to other kinds of Gradio apps. \nWe'll use a standard two-column layout so that users can see the input and output videos side by side.\n\nIn order for streaming to work, we have to set `streaming=True` in the output video. Setting the video\nto autoplay is not necessary but it's a better experience for users.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as app:\n    gr.HTML(\n        \"\"\"\n    <h1 style='text-align: center'>\n    Video Object Detection with <a href='https://huggingface.co/PekingU/rtdetr_r101vd_coco_o365' target='_blank'>RT-DETR</a>\n    </h1>\n    \"\"\")\n    with gr.Row():\n        with gr.Column():\n            video = gr.Video(label=\"Video Source\")\n            conf_threshold = gr.Slider(\n                label=\"Confidence Threshold\",\n                minimum=0.0,\n                maximum=1.0,\n                step=0.05,\n                value=0.30,\n            )\n        with gr.Column():\n            output_video = gr.Video(label=\"Processed Video\", streaming=True, autoplay=True)\n\n    video.upload(\n        fn=stream_object_detection,\n        inputs=[video, conf_threshold],\n        outputs=[output_video],\n    )\n\n\n```\n\n\n", "heading1": "The Gradio Demo", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "You can check out our demo hosted on Hugging Face Spaces [here](https://huggingface.co/spaces/gradio/rt-detr-object-detection). \n\nIt is also embedded on this page below\n\n$demo_rt-detr-object-detection", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "Just like the classic Magic 8 Ball, a user should ask it a question orally and then wait for a response. Under the hood, we'll use Whisper to transcribe the audio and then use an LLM to generate a magic-8-ball-style answer. Finally, we'll use Parler TTS to read the response aloud.\n\n", "heading1": "The Overview", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "First let's define the UI and put placeholders for all the python logic.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as block:\n    gr.HTML(\n        f\"\"\"\n        <h1 style='text-align: center;'> Magic 8 Ball \ud83c\udfb1 </h1>\n        <h3 style='text-align: center;'> Ask a question and receive wisdom </h3>\n        <p style='text-align: center;'> Powered by <a href=\"https://github.com/huggingface/parler-tts\"> Parler-TTS</a>\n        \"\"\"\n    )\n    with gr.Group():\n        with gr.Row():\n            audio_out = gr.Audio(label=\"Spoken Answer\", streaming=True, autoplay=True)\n            answer = gr.Textbox(label=\"Answer\")\n            state = gr.State()\n        with gr.Row():\n            audio_in = gr.Audio(label=\"Speak your question\", sources=\"microphone\", type=\"filepath\")\n\n    audio_in.stop_recording(generate_response, audio_in, [state, answer, audio_out])\\\n        .then(fn=read_response, inputs=state, outputs=[answer, audio_out])\n\nblock.launch()\n```\n\nWe're placing the output Audio and Textbox components and the input Audio component in separate rows. In order to stream the audio from the server, we'll set `streaming=True` in the output Audio component. We'll also set `autoplay=True` so that the audio plays as soon as it's ready.\nWe'll be using the Audio input component's `stop_recording` event to trigger our application's logic when a user stops recording from their microphone.\n\nWe're separating the logic into two parts. First, `generate_response` will take the recorded audio, transcribe it and generate a response with an LLM. We're going to store the response in a `gr.State` variable that then gets passed to the `read_response` function that generates the audio.\n\nWe're doing this in two parts because only `read_response` will require a GPU. Our app will run on Hugging Faces [ZeroGPU](https://huggingface.co/zero-gpu-explorers) which has time-based quotas. Since generating the response can be done with Hugging Face's Inference API, we shouldn't include that code in our GPU func", "heading1": "The UI", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "GPU](https://huggingface.co/zero-gpu-explorers) which has time-based quotas. Since generating the response can be done with Hugging Face's Inference API, we shouldn't include that code in our GPU function as it will needlessly use our GPU quota.\n\n", "heading1": "The UI", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "As mentioned above, we'll use [Hugging Face's Inference API](https://huggingface.co/docs/huggingface_hub/guides/inference) to transcribe the audio and generate a response from an LLM. After instantiating the client, I use the `automatic_speech_recognition` method (this automatically uses Whisper running on Hugging Face's Inference Servers) to transcribe the audio. Then I pass the question to an LLM (Mistal-7B-Instruct) to generate a response. We are prompting the LLM to act like a magic 8 ball with the system message.\n\nOur `generate_response` function will also send empty updates to the output textbox and audio components (returning `None`). \nThis is because I want the Gradio progress tracker to be displayed over the components but I don't want to display the answer until the audio is ready.\n\n\n```python\nfrom huggingface_hub import InferenceClient\n\nclient = InferenceClient(token=os.getenv(\"HF_TOKEN\"))\n\ndef generate_response(audio):\n    gr.Info(\"Transcribing Audio\", duration=5)\n    question = client.automatic_speech_recognition(audio).text\n\n    messages = [{\"role\": \"system\", \"content\": (\"You are a magic 8 ball.\"\n                                              \"Someone will present to you a situation or question and your job \"\n                                              \"is to answer with a cryptic adage or proverb such as \"\n                                              \"'curiosity killed the cat' or 'The early bird gets the worm'.\"\n                                              \"Keep your answers short and do not include the phrase 'Magic 8 Ball' in your response. If the question does not make sense or is off-topic, say 'Foolish questions get foolish answers.'\"\n                                              \"For example, 'Magic 8 Ball, should I get a dog?', 'A dog is ready for you but are you ready for the dog?'\")},\n                {\"role\": \"user\", \"content\": f\"Magic 8 Ball please answer this question -  {question}\"}]\n    \n    response = client.chat_completion(messages,", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "for you but are you ready for the dog?'\")},\n                {\"role\": \"user\", \"content\": f\"Magic 8 Ball please answer this question -  {question}\"}]\n    \n    response = client.chat_completion(messages, max_tokens=64, seed=random.randint(1, 5000),\n                                      model=\"mistralai/Mistral-7B-Instruct-v0.3\")\n\n    response = response.choices[0].message.content.replace(\"Magic 8 Ball\", \"\").replace(\":\", \"\")\n    return response, None, None\n```\n\n\nNow that we have our text response, we'll read it aloud with Parler TTS. The `read_response` function will be a python generator that yields the next chunk of audio as it's ready.\n\n\nWe'll be using the [Mini v0.1](https://huggingface.co/parler-tts/parler_tts_mini_v0.1) for the feature extraction but the [Jenny fine tuned version](https://huggingface.co/parler-tts/parler-tts-mini-jenny-30H) for the voice. This is so that the voice is consistent across generations.\n\n\nStreaming audio with transformers requires a custom Streamer class. You can see the implementation [here](https://huggingface.co/spaces/gradio/magic-8-ball/blob/main/streamer.py). Additionally, we'll convert the output to bytes so that it can be streamed faster from the backend. \n\n\n```python\nfrom streamer import ParlerTTSStreamer\nfrom transformers import AutoTokenizer, AutoFeatureExtractor, set_seed\nimport numpy as np\nimport spaces\nimport torch\nfrom threading import Thread\n\n\ndevice = \"cuda:0\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\ntorch_dtype = torch.float16 if device != \"cpu\" else torch.float32\n\nrepo_id = \"parler-tts/parler_tts_mini_v0.1\"\n\njenny_repo_id = \"ylacombe/parler-tts-mini-jenny-30H\"\n\nmodel = ParlerTTSForConditionalGeneration.from_pretrained(\n    jenny_repo_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True\n).to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(repo_id)\nfeature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)\n\nsampling_rate = model.audio_encoder.config.sampling_rate\nf", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "sage=True\n).to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(repo_id)\nfeature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)\n\nsampling_rate = model.audio_encoder.config.sampling_rate\nframe_rate = model.audio_encoder.config.frame_rate\n\n@spaces.GPU\ndef read_response(answer):\n\n    play_steps_in_s = 2.0\n    play_steps = int(frame_rate * play_steps_in_s)\n\n    description = \"Jenny speaks at an average pace with a calm delivery in a very confined sounding environment with clear audio quality.\"\n    description_tokens = tokenizer(description, return_tensors=\"pt\").to(device)\n\n    streamer = ParlerTTSStreamer(model, device=device, play_steps=play_steps)\n    prompt = tokenizer(answer, return_tensors=\"pt\").to(device)\n\n    generation_kwargs = dict(\n        input_ids=description_tokens.input_ids,\n        prompt_input_ids=prompt.input_ids,\n        streamer=streamer,\n        do_sample=True,\n        temperature=1.0,\n        min_new_tokens=10,\n    )\n\n    set_seed(42)\n    thread = Thread(target=model.generate, kwargs=generation_kwargs)\n    thread.start()\n\n    for new_audio in streamer:\n        print(f\"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds\")\n        yield answer, numpy_to_mp3(new_audio, sampling_rate=sampling_rate)\n```\n\n", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "You can see our final application [here](https://huggingface.co/spaces/gradio/magic-8-ball)!\n\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "Automatic speech recognition (ASR), the conversion of spoken speech to text, is a very important and thriving area of machine learning. ASR algorithms run on practically every smartphone, and are becoming increasingly embedded in professional workflows, such as digital assistants for nurses and doctors. Because ASR algorithms are designed to be used directly by customers and end users, it is important to validate that they are behaving as expected when confronted with a wide variety of speech patterns (different accents, pitches, and background audio conditions).\n\nUsing `gradio`, you can easily build a demo of your ASR model and share that with a testing team, or test it yourself by speaking through the microphone on your device.\n\nThis tutorial will show how to take a pretrained speech-to-text model and deploy it with a Gradio interface. We will start with a **_full-context_** model, in which the user speaks the entire audio before the prediction runs. Then we will adapt the demo to make it **_streaming_**, meaning that the audio model will convert speech as you speak. \n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). You will also need a pretrained speech recognition model. In this tutorial, we will build demos from 2 ASR libraries:\n\n- Transformers (for this, `pip install torch transformers torchaudio`)\n\nMake sure you have at least one of these installed so that you can follow along the tutorial. You will also need `ffmpeg` [installed on your system](https://www.ffmpeg.org/download.html), if you do not already have it, to process files from the microphone.\n\nHere's how to build a real time speech recognition (ASR) app:\n\n1. [Set up the Transformers ASR Model](1-set-up-the-transformers-asr-model)\n2. [Create a Full-Context ASR Demo with Transformers](2-create-a-full-context-asr-demo-with-transformers)\n3. [Create a Streaming ASR Demo with Transformers](3-create-a-streaming-asr-demo-with-transformers)\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "First, you will need to have an ASR model that you have either trained yourself or you will need to download a pretrained model. In this tutorial, we will start by using a pretrained ASR model from the model, `whisper`.\n\nHere is the code to load `whisper` from Hugging Face `transformers`.\n\n```python\nfrom transformers import pipeline\n\np = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\")\n```\n\nThat's it!\n\n", "heading1": "1. Set up the Transformers ASR Model", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "We will start by creating a _full-context_ ASR demo, in which the user speaks the full audio before using the ASR model to run inference. This is very easy with Gradio -- we simply create a function around the `pipeline` object above.\n\nWe will use `gradio`'s built in `Audio` component, configured to take input from the user's microphone and return a filepath for the recorded audio. The output component will be a plain `Textbox`.\n\n$code_asr\n$demo_asr\n\nThe `transcribe` function takes a single parameter, `audio`, which is a numpy array of the audio the user recorded. The `pipeline` object expects this in float32 format, so we convert it first to float32, and then extract the transcribed text.\n\n", "heading1": "2. Create a Full-Context ASR Demo with Transformers", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "To make this a *streaming* demo, we need to make these changes:\n\n1. Set `streaming=True` in the `Audio` component\n2. Set `live=True` in the `Interface`\n3. Add a `state` to the interface to store the recorded audio of a user\n\nTip: You can also set `time_limit` and `stream_every` parameters in the interface. The `time_limit` caps the amount of time each user's stream can take. The default is 30 seconds so users won't be able to stream audio for more than 30 seconds. The `stream_every` parameter controls how frequently data is sent to your function. By default it is 0.5 seconds.\n\nTake a look below.\n\n$code_stream_asr\n\nNotice that we now have a state variable because we need to track all the audio history. `transcribe` gets called whenever there is a new small chunk of audio, but we also need to keep track of all the audio spoken so far in the state. As the interface runs, the `transcribe` function gets called, with a record of all the previously spoken audio in the `stream` and the new chunk of audio as `new_chunk`. We return the new full audio to be stored back in its current state, and we also return the transcription. Here, we naively append the audio together and call the `transcriber` object on the entire audio. You can imagine more efficient ways of handling this, such as re-processing only the last 5 seconds of audio whenever a new chunk of audio is received. \n\n$demo_stream_asr\n\nNow the ASR model will run inference as you speak! \n", "heading1": "3. Create a Streaming ASR Demo with Transformers", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "Modern voice applications should feel natural and responsive, moving beyond the traditional \"click-to-record\" pattern. By combining Groq's fast inference capabilities with automatic speech detection, we can create a more intuitive interaction model where users can simply start talking whenever they want to engage with the AI.\n\n> Credits: VAD and Gradio code inspired by [WillHeld's Diva-audio-chat](https://huggingface.co/spaces/WillHeld/diva-audio-chat/tree/main).\n\nIn this tutorial, you will learn how to create a multimodal Gradio and Groq app that has automatic speech detection. You can also watch the full video tutorial which includes a demo of the application:\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/azXaioGdm2Q\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "Many voice apps currently work by the user clicking record, speaking, then stopping the recording. While this can be a powerful demo, the most natural mode of interaction with voice requires the app to dynamically detect when the user is speaking, so they can talk back and forth without having to continually click a record button. \n\nCreating a natural interaction with voice and text requires a dynamic and low-latency response. Thus, we need both automatic voice detection and fast inference. With @ricky0123/vad-web powering speech detection and Groq powering the LLM, both of these requirements are met. Groq provides a lightning fast response, and Gradio allows for easy creation of impressively functional apps.\n\nThis tutorial shows you how to build a calorie tracking app where you speak to an AI that automatically detects when you start and stop your response, and provides its own text response back to guide you with questions that allow it to give a calorie estimate of your last meal.\n\n", "heading1": "Background", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "- **Gradio**: Provides the web interface and audio handling capabilities\n- **@ricky0123/vad-web**: Handles voice activity detection\n- **Groq**: Powers fast LLM inference for natural conversations\n- **Whisper**: Transcribes speech to text\n\nSetting Up the Environment\n\nFirst, let\u2019s install and import our essential libraries and set up a client for using the Groq API. Here\u2019s how to do it:\n\n`requirements.txt`\n```\ngradio\ngroq\nnumpy\nsoundfile\nlibrosa\nspaces\nxxhash\ndatasets\n```\n\n`app.py`\n```python\nimport groq\nimport gradio as gr\nimport soundfile as sf\nfrom dataclasses import dataclass, field\nimport os\n\nInitialize Groq client securely\napi_key = os.environ.get(\"GROQ_API_KEY\")\nif not api_key:\n    raise ValueError(\"Please set the GROQ_API_KEY environment variable.\")\nclient = groq.Client(api_key=api_key)\n```\n\nHere, we\u2019re pulling in key libraries to interact with the Groq API, build a sleek UI with Gradio, and handle audio data. We\u2019re accessing the Groq API key securely with a key stored in an environment variable, which is a security best practice for avoiding leaking the API key.\n\n---\n\nState Management for Seamless Conversations\n\nWe need a way to keep track of our conversation history, so the chatbot remembers past interactions, and manage other states like whether recording is currently active. To do this, let\u2019s create an `AppState` class:\n\n```python\n@dataclass\nclass AppState:\n    conversation: list = field(default_factory=list)\n    stopped: bool = False\n    model_outs: Any = None\n```\n\nOur `AppState` class is a handy tool for managing conversation history and tracking whether recording is on or off. Each instance will have its own fresh list of conversations, making sure chat history is isolated to each session. \n\n---\n\nTranscribing Audio with Whisper on Groq\n\nNext, we\u2019ll create a function to transcribe the user\u2019s audio input into text using Whisper, a powerful transcription model hosted on Groq. This transcription will also help us determine whether there\u2019s meani", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "e\u2019ll create a function to transcribe the user\u2019s audio input into text using Whisper, a powerful transcription model hosted on Groq. This transcription will also help us determine whether there\u2019s meaningful speech in the input. Here\u2019s how:\n\n```python\ndef transcribe_audio(client, file_name):\n    if file_name is None:\n        return None\n\n    try:\n        with open(file_name, \"rb\") as audio_file:\n            response = client.audio.transcriptions.with_raw_response.create(\n                model=\"whisper-large-v3-turbo\",\n                file=(\"audio.wav\", audio_file),\n                response_format=\"verbose_json\",\n            )\n            completion = process_whisper_response(response.parse())\n            return completion\n    except Exception as e:\n        print(f\"Error in transcription: {e}\")\n        return f\"Error in transcription: {str(e)}\"\n```\n\nThis function opens the audio file and sends it to Groq\u2019s Whisper model for transcription, requesting detailed JSON output. verbose_json is needed to get information to determine if speech was included in the audio. We also handle any potential errors so our app doesn\u2019t fully crash if there\u2019s an issue with the API request. \n\n```python\ndef process_whisper_response(completion):\n    \"\"\"\n    Process Whisper transcription response and return text or null based on no_speech_prob\n    \n    Args:\n        completion: Whisper transcription response object\n        \n    Returns:\n        str or None: Transcribed text if no_speech_prob <= 0.7, otherwise None\n    \"\"\"\n    if completion.segments and len(completion.segments) > 0:\n        no_speech_prob = completion.segments[0].get('no_speech_prob', 0)\n        print(\"No speech prob:\", no_speech_prob)\n\n        if no_speech_prob > 0.7:\n            return None\n            \n        return completion.text.strip()\n    \n    return None\n```\n\nWe also need to interpret the audio data response. The process_whisper_response function takes the resulting completion from Whisper and checks if the audio was j", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "ext.strip()\n    \n    return None\n```\n\nWe also need to interpret the audio data response. The process_whisper_response function takes the resulting completion from Whisper and checks if the audio was just background noise or had actual speaking that was transcribed. It uses a threshold of 0.7 to interpret the no_speech_prob, and will return None if there was no speech. Otherwise, it will return the text transcript of the conversational response from the human.\n\n\n---\n\nAdding Conversational Intelligence with LLM Integration\n\nOur chatbot needs to provide intelligent, friendly responses that flow naturally. We\u2019ll use a Groq-hosted Llama-3.2 for this:\n\n```python\ndef generate_chat_completion(client, history):\n    messages = []\n    messages.append(\n        {\n            \"role\": \"system\",\n            \"content\": \"In conversation with the user, ask questions to estimate and provide (1) total calories, (2) protein, carbs, and fat in grams, (3) fiber and sugar content. Only ask *one question at a time*. Be conversational and natural.\",\n        }\n    )\n\n    for message in history:\n        messages.append(message)\n\n    try:\n        completion = client.chat.completions.create(\n            model=\"llama-3.2-11b-vision-preview\",\n            messages=messages,\n        )\n        return completion.choices[0].message.content\n    except Exception as e:\n        return f\"Error in generating chat completion: {str(e)}\"\n```\n\nWe\u2019re defining a system prompt to guide the chatbot\u2019s behavior, ensuring it asks one question at a time and keeps things conversational. This setup also includes error handling to ensure the app gracefully manages any issues.\n\n---\n\nVoice Activity Detection for Hands-Free Interaction\n\nTo make our chatbot hands-free, we\u2019ll add Voice Activity Detection (VAD) to automatically detect when someone starts or stops speaking. Here\u2019s how to implement it using ONNX in JavaScript:\n\n```javascript\nasync function main() {\n  const script1 = document.createElement(\"script\");\n  scrip", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "ly detect when someone starts or stops speaking. Here\u2019s how to implement it using ONNX in JavaScript:\n\n```javascript\nasync function main() {\n  const script1 = document.createElement(\"script\");\n  script1.src = \"https://cdn.jsdelivr.net/npm/onnxruntime-web@1.14.0/dist/ort.js\";\n  document.head.appendChild(script1)\n  const script2 = document.createElement(\"script\");\n  script2.onload = async () =>  {\n    console.log(\"vad loaded\");\n    var record = document.querySelector('.record-button');\n    record.textContent = \"Just Start Talking!\"\n    \n    const myvad = await vad.MicVAD.new({\n      onSpeechStart: () => {\n        var record = document.querySelector('.record-button');\n        var player = document.querySelector('streaming-out')\n        if (record != null && (player == null || player.paused)) {\n          record.click();\n        }\n      },\n      onSpeechEnd: (audio) => {\n        var stop = document.querySelector('.stop-button');\n        if (stop != null) {\n          stop.click();\n        }\n      }\n    })\n    myvad.start()\n  }\n  script2.src = \"https://cdn.jsdelivr.net/npm/@ricky0123/vad-web@0.0.7/dist/bundle.min.js\";\n}\n```\n\nThis script loads our VAD model and sets up functions to start and stop recording automatically. When the user starts speaking, it triggers the recording, and when they stop, it ends the recording.\n\n---\n\nBuilding a User Interface with Gradio\n\nNow, let\u2019s create an intuitive and visually appealing user interface with Gradio. This interface will include an audio input for capturing voice, a chat window for displaying responses, and state management to keep things synchronized.\n\n```python\nwith gr.Blocks(theme=theme, js=js) as demo:\n    with gr.Row():\n        input_audio = gr.Audio(\n            label=\"Input Audio\",\n            sources=[\"microphone\"],\n            type=\"numpy\",\n            streaming=False,\n            waveform_options=gr.WaveformOptions(waveform_color=\"B83A4B\"),\n        )\n    with gr.Row():\n        chatbot = gr.Chatbot(label=\"Conversati", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "       type=\"numpy\",\n            streaming=False,\n            waveform_options=gr.WaveformOptions(waveform_color=\"B83A4B\"),\n        )\n    with gr.Row():\n        chatbot = gr.Chatbot(label=\"Conversation\", type=\"messages\")\n    state = gr.State(value=AppState())\n```\n\nIn this code block, we\u2019re using Gradio\u2019s `Blocks` API to create an interface with an audio input, a chat display, and an application state manager. The color customization for the waveform adds a nice visual touch.\n\n---\n\nHandling Recording and Responses\n\nFinally, let\u2019s link the recording and response components to ensure the app reacts smoothly to user inputs and provides responses in real-time.\n\n```python\n    stream = input_audio.start_recording(\n        process_audio,\n        [input_audio, state],\n        [input_audio, state],\n    )\n    respond = input_audio.stop_recording(\n        response, [state, input_audio], [state, chatbot]\n    )\n```\n\nThese lines set up event listeners for starting and stopping the recording, processing the audio input, and generating responses. By linking these events, we create a cohesive experience where users can simply talk, and the chatbot handles the rest.\n\n---\n\n", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "1. When you open the app, the VAD system automatically initializes and starts listening for speech\n2. As soon as you start talking, it triggers the recording automatically\n3. When you stop speaking, the recording ends and:\n   - The audio is transcribed using Whisper\n   - The transcribed text is sent to the LLM\n   - The LLM generates a response about calorie tracking\n   - The response is displayed in the chat interface\n4. This creates a natural back-and-forth conversation where you can simply talk about your meals and get instant feedback on nutritional content\n\nThis app demonstrates how to create a natural voice interface that feels responsive and intuitive. By combining Groq's fast inference with automatic speech detection, we've eliminated the need for manual recording controls while maintaining high-quality interactions. The result is a practical calorie tracking assistant that users can simply talk to as naturally as they would to a human nutritionist.\n\nLink to GitHub repository: [Groq Gradio Basics](https://github.com/bklieger-groq/gradio-groq-basics/tree/main/calorie-tracker)", "heading1": "Summary", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "Start by installing all the dependencies. Add the following lines to a `requirements.txt` file and run `pip install -r requirements.txt`:\n\n```bash\nopencv-python\ntwilio\ngradio>=5.0\ngradio-webrtc\nonnxruntime-gpu\n```\n\nWe'll use the ONNX runtime to speed up YOLOv10 inference. This guide assumes you have access to a GPU. If you don't, change `onnxruntime-gpu` to `onnxruntime`. Without a GPU, the model will run slower, resulting in a laggy demo.\n\nWe'll use OpenCV for image manipulation and the [Gradio WebRTC](https://github.com/freddyaboulton/gradio-webrtc) custom component to use [WebRTC](https://webrtc.org/) under the hood, achieving near-zero latency.\n\n**Note**: If you want to deploy this app on any cloud provider, you'll need to use the free Twilio API for their [TURN servers](https://www.twilio.com/docs/stun-turn). Create a free account on Twilio. If you're not familiar with TURN servers, consult this [guide](https://www.twilio.com/docs/stun-turn/faqfaq-what-is-nat).\n\n", "heading1": "Setting up", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "We'll download the YOLOv10 model from the Hugging Face hub and instantiate a custom inference class to use this model. \n\nThe implementation of the inference class isn't covered in this guide, but you can find the source code [here](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n/blob/main/inference.pyL9) if you're interested. This implementation borrows heavily from this [github repository](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection).\n\nWe're using the `yolov10-n` variant because it has the lowest latency. See the [Performance](https://github.com/THU-MIG/yolov10?tab=readme-ov-fileperformance) section of the README in the YOLOv10 GitHub repository.\n\n```python\nfrom huggingface_hub import hf_hub_download\nfrom inference import YOLOv10\n\nmodel_file = hf_hub_download(\n    repo_id=\"onnx-community/yolov10n\", filename=\"onnx/model.onnx\"\n)\n\nmodel = YOLOv10(model_file)\n\ndef detection(image, conf_threshold=0.3):\n    image = cv2.resize(image, (model.input_width, model.input_height))\n    new_image = model.detect_objects(image, conf_threshold)\n    return new_image\n```\n\nOur inference function, `detection`, accepts a numpy array from the webcam and a desired confidence threshold. Object detection models like YOLO identify many objects and assign a confidence score to each. The lower the confidence, the higher the chance of a false positive. We'll let users adjust the confidence threshold.\n\nThe function returns a numpy array corresponding to the same input image with all detected objects in bounding boxes.\n\n", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "The Gradio demo is straightforward, but we'll implement a few specific features:\n\n1. Use the `WebRTC` custom component to ensure input and output are sent to/from the server with WebRTC. \n2. The [WebRTC](https://github.com/freddyaboulton/gradio-webrtc) component will serve as both an input and output component.\n3. Utilize the `time_limit` parameter of the `stream` event. This parameter sets a processing time for each user's stream. In a multi-user setting, such as on Spaces, we'll stop processing the current user's stream after this period and move on to the next. \n\nWe'll also apply custom CSS to center the webcam and slider on the page.\n\n```python\nimport gradio as gr\nfrom gradio_webrtc import WebRTC\n\ncss = \"\"\".my-group {max-width: 600px !important; max-height: 600px !important;}\n         .my-column {display: flex !important; justify-content: center !important; align-items: center !important;}\"\"\"\n\nwith gr.Blocks(css=css) as demo:\n    gr.HTML(\n        \"\"\"\n        <h1 style='text-align: center'>\n        YOLOv10 Webcam Stream (Powered by WebRTC \u26a1\ufe0f)\n        </h1>\n        \"\"\"\n    )\n    with gr.Column(elem_classes=[\"my-column\"]):\n        with gr.Group(elem_classes=[\"my-group\"]):\n            image = WebRTC(label=\"Stream\", rtc_configuration=rtc_configuration)\n            conf_threshold = gr.Slider(\n                label=\"Confidence Threshold\",\n                minimum=0.0,\n                maximum=1.0,\n                step=0.05,\n                value=0.30,\n            )\n\n        image.stream(\n            fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10\n        )\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\n", "heading1": "The Gradio Demo", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "Our app is hosted on Hugging Face Spaces [here](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n). \n\nYou can use this app as a starting point to build real-time image applications with Gradio. Don't hesitate to open issues in the space or in the [WebRTC component GitHub repo](https://github.com/freddyaboulton/gradio-webrtc) if you have any questions or encounter problems.", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "To create a demo that has both the input and the output components, you simply need to set the values of the `inputs` and `outputs` parameter in `Interface()`. Here's an example demo of a simple image filter:\n\n$code_sepia_filter\n$demo_sepia_filter\n\n", "heading1": "Standard demos", "source_page_url": "https://gradio.app/guides/four-kinds-of-interfaces", "source_page_title": "Building Interfaces - Four Kinds Of Interfaces Guide"}, {"text": "What about demos that only contain outputs? In order to build such a demo, you simply set the value of the `inputs` parameter in `Interface()` to `None`. Here's an example demo of a mock image generation model:\n\n$code_fake_gan_no_input\n$demo_fake_gan_no_input\n\n", "heading1": "Output-only demos", "source_page_url": "https://gradio.app/guides/four-kinds-of-interfaces", "source_page_title": "Building Interfaces - Four Kinds Of Interfaces Guide"}, {"text": "Similarly, to create a demo that only contains inputs, set the value of `outputs` parameter in `Interface()` to be `None`. Here's an example demo that saves any uploaded image to disk:\n\n$code_save_file_no_output\n$demo_save_file_no_output\n\n", "heading1": "Input-only demos", "source_page_url": "https://gradio.app/guides/four-kinds-of-interfaces", "source_page_title": "Building Interfaces - Four Kinds Of Interfaces Guide"}, {"text": "A demo that has a single component as both the input and the output. It can simply be created by setting the values of the `inputs` and `outputs` parameter as the same component. Here's an example demo of a text generation model:\n\n$code_unified_demo_text_generation\n$demo_unified_demo_text_generation\n\nIt may be the case that none of the 4 cases fulfill your exact needs. In this case, you need to use the `gr.Blocks()` approach!", "heading1": "Unified demos", "source_page_url": "https://gradio.app/guides/four-kinds-of-interfaces", "source_page_title": "Building Interfaces - Four Kinds Of Interfaces Guide"}, {"text": "Gradio includes more than 30 pre-built components (as well as many [community-built _custom components_](https://www.gradio.app/custom-components/gallery)) that can be used as inputs or outputs in your demo. These components correspond to common data types in machine learning and data science, e.g. the `gr.Image` component is designed to handle input or output images, the `gr.Label` component displays classification labels and probabilities, the `gr.LinePlot` component displays line plots, and so on. \n\n", "heading1": "Gradio Components", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "We used the default versions of the `gr.Textbox` and `gr.Slider`, but what if you want to change how the UI components look or behave?\n\nLet's say you want to customize the slider to have values from 1 to 10, with a default of 2. And you wanted to customize the output text field \u2014 you want it to be larger and have a label.\n\nIf you use the actual classes for `gr.Textbox` and `gr.Slider` instead of the string shortcuts, you have access to much more customizability through component attributes.\n\n$code_hello_world_2\n$demo_hello_world_2\n\n", "heading1": "Components Attributes", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "Suppose you had a more complex function, with multiple outputs as well. In the example below, we define a function that takes a string, boolean, and number, and returns a string and number. \n\n$code_hello_world_3\n$demo_hello_world_3\n\nJust as each component in the `inputs` list corresponds to one of the parameters of the function, in order, each component in the `outputs` list corresponds to one of the values returned by the function, in order.\n\n", "heading1": "Multiple Input and Output Components", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "Gradio supports many types of components, such as `Image`, `DataFrame`, `Video`, or `Label`. Let's try an image-to-image function to get a feel for these!\n\n$code_sepia_filter\n$demo_sepia_filter\n\nWhen using the `Image` component as input, your function will receive a NumPy array with the shape `(height, width, 3)`, where the last dimension represents the RGB values. We'll return an image as well in the form of a NumPy array. \n\nGradio handles the preprocessing and postprocessing to convert images to NumPy arrays and vice versa. You can also control the preprocessing performed with the `type=` keyword argument. For example, if you wanted your function to take a file path to an image instead of a NumPy array, the input `Image` component could be written as:\n\n```python\ngr.Image(type=\"filepath\")\n```\n\nYou can read more about the built-in Gradio components and how to customize them in the [Gradio docs](https://gradio.app/docs).\n\n", "heading1": "An Image Example", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "You can provide example data that a user can easily load into `Interface`. This can be helpful to demonstrate the types of inputs the model expects, as well as to provide a way to explore your dataset in conjunction with your model. To load example data, you can provide a **nested list** to the `examples=` keyword argument of the Interface constructor. Each sublist within the outer list represents a data sample, and each element within the sublist represents an input for each input component. The format of example data for each component is specified in the [Docs](https://gradio.app/docscomponents).\n\n$code_calculator\n$demo_calculator\n\nYou can load a large dataset into the examples to browse and interact with the dataset through Gradio. The examples will be automatically paginated (you can configure this through the `examples_per_page` argument of `Interface`).\n\nContinue learning about examples in the [More On Examples](https://gradio.app/guides/more-on-examples) guide.\n\n", "heading1": "Example Inputs", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "In the previous example, you may have noticed the `title=` and `description=` keyword arguments in the `Interface` constructor that helps users understand your app.\n\nThere are three arguments in the `Interface` constructor to specify where this content should go:\n\n- `title`: which accepts text and can display it at the very top of interface, and also becomes the page title.\n- `description`: which accepts text, markdown or HTML and places it right under the title.\n- `article`: which also accepts text, markdown or HTML and places it below the interface.\n\n![annotated](https://github.com/gradio-app/gradio/blob/main/guides/assets/annotated.png?raw=true)\n\nAnother useful keyword argument is `label=`, which is present in every `Component`. This modifies the label text at the top of each `Component`. You can also add the `info=` keyword argument to form elements like `Textbox` or `Radio` to provide further information on their usage.\n\n```python\ngr.Number(label='Age', info='In years, must be greater than 0')\n```\n\n", "heading1": "Descriptive Content", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "If your prediction function takes many inputs, you may want to hide some of them within a collapsed accordion to avoid cluttering the UI. The `Interface` class takes an `additional_inputs` argument which is similar to `inputs` but any input components included here are not visible by default. The user must click on the accordion to show these components. The additional inputs are passed into the prediction function, in order, after the standard inputs.\n\nYou can customize the appearance of the accordion by using the optional `additional_inputs_accordion` argument, which accepts a string (in which case, it becomes the label of the accordion), or an instance of the `gr.Accordion()` class (e.g. this lets you control whether the accordion is open or closed by default).\n\nHere's an example:\n\n$code_interface_with_additional_inputs\n$demo_interface_with_additional_inputs\n\n", "heading1": "Additional Inputs within an Accordion", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "Adding examples to an Interface is as easy as providing a list of lists to the `examples`\nkeyword argument.\nEach sublist is a data sample, where each element corresponds to an input of the prediction function.\nThe inputs must be ordered in the same order as the prediction function expects them.\n\nIf your interface only has one input component, then you can provide your examples as a regular list instead of a list of lists.\n\nLoading Examples from a Directory\n\nYou can also specify a path to a directory containing your examples. If your Interface takes only a single file-type input, e.g. an image classifier, you can simply pass a directory filepath to the `examples=` argument, and the `Interface` will load the images in the directory as examples.\nIn the case of multiple inputs, this directory must\ncontain a log.csv file with the example values.\nIn the context of the calculator demo, we can set `examples='/demo/calculator/examples'` and in that directory we include the following `log.csv` file:\n\n```csv\nnum,operation,num2\n5,\"add\",3\n4,\"divide\",2\n5,\"multiply\",3\n```\n\nThis can be helpful when browsing flagged data. Simply point to the flagged directory and the `Interface` will load the examples from the flagged data.\n\nProviding Partial Examples\n\nSometimes your app has many input components, but you would only like to provide examples for a subset of them. In order to exclude some inputs from the examples, pass `None` for all data samples corresponding to those particular components.\n\n", "heading1": "Providing Examples", "source_page_url": "https://gradio.app/guides/more-on-examples", "source_page_title": "Building Interfaces - More On Examples Guide"}, {"text": "You may wish to provide some cached examples of your model for users to quickly try out, in case your model takes a while to run normally.\nIf `cache_examples=True`, your Gradio app will run all of the examples and save the outputs when you call the `launch()` method. This data will be saved in a directory called `gradio_cached_examples` in your working directory by default. You can also set this directory with the `GRADIO_EXAMPLES_CACHE` environment variable, which can be either an absolute path or a relative path to your working directory.\n\nWhenever a user clicks on an example, the output will automatically be populated in the app now, using data from this cached directory instead of actually running the function. This is useful so users can quickly try out your model without adding any load!\n\nAlternatively, you can set `cache_examples=\"lazy\"`. This means that each particular example will only get cached after it is first used (by any user) in the Gradio app. This is helpful if your prediction function is long-running and you do not want to wait a long time for your Gradio app to start.\n\nKeep in mind once the cache is generated, it will not be updated automatically in future launches. If the examples or function logic change, delete the cache folder to clear the cache and rebuild it with another `launch()`.\n", "heading1": "Caching examples", "source_page_url": "https://gradio.app/guides/more-on-examples", "source_page_title": "Building Interfaces - More On Examples Guide"}, {"text": "If the state is something that should be accessible to all function calls and all users, you can create a variable outside the function call and access it inside the function. For example, you may load a large model outside the function and use it inside the function so that every function call does not need to reload the model.\n\n$code_score_tracker\n\nIn the code above, the `scores` array is shared between all users. If multiple users are accessing this demo, their scores will all be added to the same list, and the returned top 3 scores will be collected from this shared reference.\n\n", "heading1": "Global State", "source_page_url": "https://gradio.app/guides/interface-state", "source_page_title": "Building Interfaces - Interface State Guide"}, {"text": "Another type of data persistence Gradio supports is session state, where data persists across multiple submits within a page session. However, data is _not_ shared between different users of your model. To store data in a session state, you need to do three things:\n\n1. Pass in an extra parameter into your function, which represents the state of the interface.\n2. At the end of the function, return the updated value of the state as an extra return value.\n3. Add the `'state'` input and `'state'` output components when creating your `Interface`\n\nHere's a simple app to illustrate session state - this app simply stores users previous submissions and displays them back to the user:\n\n\n$code_interface_state\n$demo_interface_state\n\n\nNotice how the state persists across submits within each page, but if you load this demo in another tab (or refresh the page), the demos will not share chat history. Here, we could not store the submission history in a global variable, otherwise the submission history would then get jumbled between different users.\n\nThe initial value of the `State` is `None` by default. If you pass a parameter to the `value` argument of `gr.State()`, it is used as the default value of the state instead. \n\nNote: the `Interface` class only supports a single session state variable (though it can be a list with multiple elements). For more complex use cases, you can use Blocks, [which supports multiple `State` variables](/guides/state-in-blocks/). Alternatively, if you are building a chatbot that maintains user state, consider using the `ChatInterface` abstraction, [which manages state automatically](/guides/creating-a-chatbot-fast).\n", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/interface-state", "source_page_title": "Building Interfaces - Interface State Guide"}, {"text": "You can make interfaces automatically refresh by setting `live=True` in the interface. Now the interface will recalculate as soon as the user input changes.\n\n$code_calculator_live\n$demo_calculator_live\n\nNote there is no submit button, because the interface resubmits automatically on change.\n\n", "heading1": "Live Interfaces", "source_page_url": "https://gradio.app/guides/reactive-interfaces", "source_page_title": "Building Interfaces - Reactive Interfaces Guide"}, {"text": "Some components have a \"streaming\" mode, such as `Audio` component in microphone mode, or the `Image` component in webcam mode. Streaming means data is sent continuously to the backend and the `Interface` function is continuously being rerun.\n\nThe difference between `gr.Audio(source='microphone')` and `gr.Audio(source='microphone', streaming=True)`, when both are used in `gr.Interface(live=True)`, is that the first `Component` will automatically submit data and run the `Interface` function when the user stops recording, whereas the second `Component` will continuously send data and run the `Interface` function _during_ recording.\n\nHere is example code of streaming images from the webcam.\n\n$code_stream_frames\n\nStreaming can also be done in an output component. A `gr.Audio(streaming=True)` output component can take a stream of audio data yielded piece-wise by a generator function and combines them into a single audio file. For a detailed example, see our guide on performing [automatic speech recognition](/guides/real-time-speech-recognition) with Gradio.\n", "heading1": "Streaming Components", "source_page_url": "https://gradio.app/guides/reactive-interfaces", "source_page_title": "Building Interfaces - Reactive Interfaces Guide"}, {"text": "**Prerequisite**: Gradio requires [Python 3.10 or higher](https://www.python.org/downloads/).\n\n\nWe recommend installing Gradio using `pip`, which is included by default in Python. Run this in your terminal or command prompt:\n\n```bash\npip install --upgrade gradio\n```\n\n\nTip: It is best to install Gradio in a virtual environment. Detailed installation instructions for all common operating systems <a href=\"https://www.gradio.app/main/guides/installing-gradio-in-a-virtual-environment\">are provided here</a>. \n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "You can run Gradio in your favorite code editor, Jupyter notebook, Google Colab, or anywhere else you write Python. Let's write your first Gradio app:\n\n\n$code_hello_world_4\n\n\nTip: We shorten the imported name from <code>gradio</code> to <code>gr</code>. This is a widely adopted convention for better readability of code. \n\nNow, run your code. If you've written the Python code in a file named `app.py`, then you would run `python app.py` from the terminal.\n\nThe demo below will open in a browser on [http://localhost:7860](http://localhost:7860) if running from a file. If you are running within a notebook, the demo will appear embedded within the notebook.\n\n$demo_hello_world_4\n\nType your name in the textbox on the left, drag the slider, and then press the Submit button. You should see a friendly greeting on the right.\n\nTip: When developing locally, you can run your Gradio app in <strong>hot reload mode</strong>, which automatically reloads the Gradio app whenever you make changes to the file. To do this, simply type in <code>gradio</code> before the name of the file instead of <code>python</code>. In the example above, you would type: `gradio app.py` in your terminal. You can also enable <strong>vibe mode</strong> by using the <code>--vibe</code> flag, e.g. <code>gradio --vibe app.py</code>, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. Learn more in the <a href=\"https://www.gradio.app/guides/developing-faster-with-reload-mode\">Hot Reloading Guide</a>.\n\n\n**Understanding the `Interface` Class**\n\nYou'll notice that in order to make your first demo, you created an instance of the `gr.Interface` class. The `Interface` class is designed to create demos for machine learning models which accept one or more inputs, and return one or more outputs. \n\nThe `Interface` class has three core arguments:\n\n- `fn`: the function to wrap a user interface (UI) around\n- `inputs`: the Gradio component(s) to use for the input. The num", "heading1": "Building Your First Demo", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "turn one or more outputs. \n\nThe `Interface` class has three core arguments:\n\n- `fn`: the function to wrap a user interface (UI) around\n- `inputs`: the Gradio component(s) to use for the input. The number of components should match the number of arguments in your function.\n- `outputs`: the Gradio component(s) to use for the output. The number of components should match the number of return values from your function.\n\nThe `fn` argument is very flexible -- you can pass *any* Python function that you want to wrap with a UI. In the example above, we saw a relatively simple function, but the function could be anything from a music generator to a tax calculator to the prediction function of a pretrained machine learning model.\n\nThe `inputs` and `outputs` arguments take one or more Gradio components. As we'll see, Gradio includes more than [30 built-in components](https://www.gradio.app/docs/gradio/introduction) (such as the `gr.Textbox()`, `gr.Image()`, and `gr.HTML()` components) that are designed for machine learning applications. \n\nTip: For the `inputs` and `outputs` arguments, you can pass in the name of these components as a string (`\"textbox\"`) or an instance of the class (`gr.Textbox()`).\n\nIf your function accepts more than one argument, as is the case above, pass a list of input components to `inputs`, with each input component corresponding to one of the arguments of the function, in order. The same holds true if your function returns more than one value: simply pass in a list of components to `outputs`. This flexibility makes the `Interface` class a very powerful way to create demos.\n\nWe'll dive deeper into the `gr.Interface` on our series on [building Interfaces](https://www.gradio.app/main/guides/the-interface-class).\n\n", "heading1": "Building Your First Demo", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "What good is a beautiful demo if you can't share it? Gradio lets you easily share a machine learning demo without having to worry about the hassle of hosting on a web server. Simply set `share=True` in `launch()`, and a publicly accessible URL will be created for your demo. Let's revisit our example demo,  but change the last line as follows:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n    return \"Hello \" + name + \"!\"\n\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n    \ndemo.launch(share=True)  Share your demo with just 1 extra parameter \ud83d\ude80\n```\n\nWhen you run this code, a public URL will be generated for your demo in a matter of seconds, something like:\n\n\ud83d\udc49 &nbsp; `https://a23dsf231adb.gradio.live`\n\nNow, anyone around the world can try your Gradio demo from their browser, while the machine learning model and all computation continues to run locally on your computer.\n\nTo learn more about sharing your demo, read our dedicated guide on [sharing your Gradio application](https://www.gradio.app/guides/sharing-your-app).\n\n\n", "heading1": "Sharing Your Demo", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "So far, we've been discussing the `Interface` class, which is a high-level class that lets to build demos quickly with Gradio. But what else does Gradio include?\n\nCustom Demos with `gr.Blocks`\n\nGradio offers a low-level approach for designing web apps with more customizable layouts and data flows with the `gr.Blocks` class. Blocks supports things like controlling where components appear on the page, handling multiple data flows and more complex interactions (e.g. outputs can serve as inputs to other functions), and updating properties/visibility of components based on user interaction \u2014 still all in Python. \n\nYou can build very custom and complex applications using `gr.Blocks()`. For example, the popular image generation [Automatic1111 Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) is built using Gradio Blocks. We dive deeper into the `gr.Blocks` on our series on [building with Blocks](https://www.gradio.app/guides/blocks-and-event-listeners).\n\nChatbots with `gr.ChatInterface`\n\nGradio includes another high-level class, `gr.ChatInterface`, which is specifically designed to create Chatbot UIs. Similar to `Interface`, you supply a function and Gradio creates a fully working Chatbot UI. If you're interested in creating a chatbot, you can jump straight to [our dedicated guide on `gr.ChatInterface`](https://www.gradio.app/guides/creating-a-chatbot-fast).\n\nThe Gradio Python & JavaScript Ecosystem\n\nThat's the gist of the core `gradio` Python library, but Gradio is actually so much more! It's an entire ecosystem of Python and JavaScript libraries that let you build machine learning applications, or query them programmatically, in Python or JavaScript. Here are other related parts of the Gradio ecosystem:\n\n* [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) (`gradio_client`): query any Gradio app programmatically in Python.\n* [Gradio JavaScript Client](https://www.gradio.app/guides/getting-started-with-t", "heading1": "An Overview of Gradio", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "app/guides/getting-started-with-the-python-client) (`gradio_client`): query any Gradio app programmatically in Python.\n* [Gradio JavaScript Client](https://www.gradio.app/guides/getting-started-with-the-js-client) (`@gradio/client`): query any Gradio app programmatically in JavaScript.\n* [Gradio-Lite](https://www.gradio.app/guides/gradio-lite) (`@gradio/lite`): write Gradio apps in Python that run entirely in the browser (no server needed!), thanks to Pyodide. \n* [Hugging Face Spaces](https://huggingface.co/spaces): the most popular place to host Gradio applications \u2014 for free!\n\n", "heading1": "An Overview of Gradio", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "Keep learning about Gradio sequentially using the Gradio Guides, which include explanations as well as example code and embedded interactive demos. Next up: [let's dive deeper into the Interface class](https://www.gradio.app/guides/the-interface-class).\n\nOr, if you already know the basics and are looking for something specific, you can search the more [technical API documentation](https://www.gradio.app/docs/).\n\n\n", "heading1": "What's Next?", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "You can also build Gradio applications without writing any code. Simply type `gradio sketch` into your terminal to open up an editor that lets you define and modify Gradio components, adjust their layouts, add events, all through a web editor. Or [use this hosted version of Gradio Sketch, running on Hugging Face Spaces](https://huggingface.co/spaces/aliabid94/Sketch).", "heading1": "Gradio Sketch", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}]
\ No newline at end of file
+[{"text": "The gr.UndoData class is a subclass of gr.Event data that specifically\ncarries information about the `.undo()` event. When gr.UndoData is added as a\ntype hint to an argument of an event listener method, a gr.UndoData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/undodata", "source_page_title": "Gradio - Undodata Docs"}, {"text": "import gradio as gr\n    \n    def undo(retry_data: gr.UndoData, history: list[gr.MessageDict]):\n        history_up_to_retry = history[:retry_data.index]\n        return history_up_to_retry\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot()\n        chatbot.undo(undo, chatbot, chatbot)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/undodata", "source_page_title": "Gradio - Undodata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the user message that should be undone.\n\n\n    \n    \n    value: Any\n\nThe value of the user message that should be undone.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/undodata", "source_page_title": "Gradio - Undodata Docs"}, {"text": "Creates a slider that ranges from `minimum` to `maximum` with a step size\nof `step`.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "**As input component** : Passes slider value as a `float` into the\nfunction.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: float\n    )\n    \t...\n\n  \n\n**As output component** : Expects an `int` or `float` returned from\nfunction and sets slider value to it as long as it is within range (otherwise,\nsets to minimum value).\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    minimum: float\n\ndefault `= 0`\n\nminimum value for slider. When used as an input, if a user provides a smaller\nvalue, a gr.Error exception is raised by the backend.\n\n\n    \n    \n    maximum: float\n\ndefault `= 100`\n\nmaximum value for slider. When used as an input, if a user provides a larger\nvalue, a gr.Error exception is raised by the backend.\n\n\n    \n    \n    value: float | Callable | None\n\ndefault `= None`\n\ndefault value for slider. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\nIgnored if randomized=True.\n\n\n    \n    \n    step: float | None\n\ndefault `= None`\n\nincrement between slider values.\n\n\n    \n    \n    precision: int | None\n\ndefault `= None`\n\nPrecision to round input/output to. If set to 0, will round to nearest integer\nand convert type to int. If None, no rounding happens.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "sed as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, slider will be adjustable; if False, adjusting will be disabled. If\nnot provided, this is inferred based on whether the component is used as an\ninput or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same ke", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": " intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    randomize: bool\n\ndefault `= False`\n\nIf True, the value of the slider when the app loads is taken uniformly at\nrandom from the range given by the minimum and maximum.\n\n\n    \n    \n    show_reset_button: bool\n\ndefault `= True`\n\nif False, will hide button to reset slider to default value.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Slider` | \"slider\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "sentence_builderslider_releaseinterface_random_sliderblocks_random_slider\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.Checkb", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "abel=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def identity(x, state): state += 1 return x,\nstate, state with gr.Blocks() as demo: slider = gr.Slider(0, 100, step=0.1)\nstate = gr.State(value=0) with gr.Row(): number = gr.Number(label=\"On\nrelease\") number2 = gr.Number(label=\"Number of events fired\")\nslider.release(identity, inputs=[slider, state], outputs=[number, state,\nnumber2], api_name=\"predict\") if __name__ == \"__main__\": print(\"here\")\ndemo.launch() print(demo.server_port)\n\n    \n    \n    import gradio as gr\n    \n    def identity(x, state):\n        state += 1\n        return x, state, state\n    \n    with gr.Blocks() as demo:\n        slider = gr.Slider(0, 100, step=0.1)\n        state = gr.State(value=0)\n        with gr.Row():\n            number = gr.Number(label=\"On release\")\n            number2 = gr.Number(label=\"Number o", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "        slider = gr.Slider(0, 100, step=0.1)\n        state = gr.State(value=0)\n        with gr.Row():\n            number = gr.Number(label=\"On release\")\n            number2 = gr.Number(label=\"Number of events fired\")\n        slider.release(identity, inputs=[slider, state], outputs=[number, state, number2], api_name=\"predict\")\n    \n    if __name__ == \"__main__\":\n        print(\"here\")\n        demo.launch()\n        print(demo.server_port)\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def func(slider_1, slider_2, *args): return\nslider_1 + slider_2 * 5 demo = gr.Interface( func, [ gr.Slider(minimum=1.5,\nmaximum=250000.89, randomize=True, label=\"Random Big Range\"),\ngr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only\nmultiple of 0.05 allowed\"), gr.Slider(minimum=0, maximum=1, randomize=True,\nstep=0.25, label=\"Random only multiples of 0.25 allowed\"),\ngr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random\nbetween -100 and 100 step 3\"), gr.Slider(minimum=-100, maximum=100,\nrandomize=True, label=\"Random between -100 and 100\"), gr.Slider(value=0.25,\nminimum=5, maximum=30, step=-1), ], \"number\", ) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def func(slider_1, slider_2, *args):\n        return slider_1 + slider_2 * 5\n    \n    demo = gr.Interface(\n        func,\n        [\n            gr.Slider(minimum=1.5, maximum=250000.89, randomize=True, label=\"Random Big Range\"),\n            gr.Slider(minimum=-1, maximum=1, randomize=True, step=0.05, label=\"Random only multiple of 0.05 allowed\"),\n            gr.Slider(minimum=0, maximum=1, randomize=True, step=0.25, label=\"Random only multiples of 0.25 allowed\"),\n            gr.Slider(minimum=-100, maximum=100, randomize=True, step=3, label=\"Random between -100 and 100 step 3\"),\n            gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n            gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n        ],\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "d 100 step 3\"),\n            gr.Slider(minimum=-100, maximum=100, randomize=True, label=\"Random between -100 and 100\"),\n            gr.Slider(value=0.25, minimum=5, maximum=30, step=-1),\n        ],\n        \"number\",\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def func(slider_1, slider_2): return slider_1\n* 5 + slider_2 with gr.Blocks() as demo: slider = gr.Slider(minimum=-10.2,\nmaximum=15, label=\"Random Slider (Static)\", randomize=True) slider_1 =\ngr.Slider(minimum=100, maximum=200, label=\"Random Slider (Input 1)\",\nrandomize=True) slider_2 = gr.Slider(minimum=10, maximum=23.2, label=\"Random\nSlider (Input 2)\", randomize=True) slider_3 = gr.Slider(value=3, label=\"Non\nrandom slider\") btn = gr.Button(\"Run\") btn.click(func, inputs=[slider_1,\nslider_2], outputs=gr.Number()) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def func(slider_1, slider_2):\n        return slider_1 * 5 + slider_2\n    \n    with gr.Blocks() as demo:\n        slider = gr.Slider(minimum=-10.2, maximum=15, label=\"Random Slider (Static)\", randomize=True)\n        slider_1 = gr.Slider(minimum=100, maximum=200, label=\"Random Slider (Input 1)\", randomize=True)\n        slider_2 = gr.Slider(minimum=10, maximum=23.2, label=\"Random Slider (Input 2)\", randomize=True)\n        slider_3 = gr.Slider(value=3, label=\"Non random slider\")\n        btn = gr.Button(\"Run\")\n        btn.click(func, inputs=[slider_1, slider_2], outputs=gr.Number())\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Slider component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Slider.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Slider changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Slider.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Slider.  \n`Slider.release(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user releases the mouse on this Slider.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "one\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": " been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n  ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": " unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/slider", "source_page_title": "Gradio - Slider Docs"}, {"text": "Creates a numeric field for user to enter numbers as input or display\nnumeric output.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "**As input component** : Passes field value as a `float` or `int` into the\nfunction, depending on `precision`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: float | int | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects an `int` or `float` returned from the\nfunction and sets field value to it.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> float | int | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: float | Callable | None\n\ndefault `= None`\n\ndefault value. If None, the component will be empty and show the `placeholder`\nif is set. If no `placeholder` is set, the component will show 0. If a\nfunction is provided, the function will be called each time the app loads to\nset the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    placeholder: str | I18nData | None\n\ndefault `= None`\n\nplaceholder hint to provide behind number input.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applie", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be editable; if False, editing will be disabled. If not\nprovided, this is inferred based on whether the component is used as an input\nor output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "rameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    precision: int | None\n\ndefault `= None`\n\nPrecision to round input/output to. If set to 0, will round to nearest integer\nand convert type to int. If None, no rounding happens.\n\n\n    \n    \n    minimum: float | None\n\ndefault `= None`\n\nMinimum value. Only applied when component is used as an input. If a user\nprovides a smaller value, a gr.Error exception is raised by the backend.\n\n\n    \n    \n    maximum: float | None\n\ndefault `= None`\n\nMaximum value. Only applied when component is used as an input. If a user\nprovides a larger value, a gr.Error exception is raised by the backend.\n\n\n    \n    \n    step: float\n\ndefault `= 1`\n\nThe interval between allowed numbers in the component. Can be used along with\noptional parameters `minimum` and `maximum` to create a range of legal values\nstarting from `minimum` and incrementing according to this parameter.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Number` | \"number\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "tax_calculatorblocks_simple_squares\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def tax_calculator(income, marital_status,\nassets): tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\ntotal_deductible = sum(assets[\"Cost\"]) taxable_income = income -\ntotal_deductible total_tax = 0 for bracket, rate in tax_brackets: if\ntaxable_income > bracket: total_tax += (taxable_income - bracket) * rate / 100\nif marital_status == \"Married\": total_tax *= 0.75 elif marital_status ==\n\"Divorced\": total_tax *= 0.8 return round(total_tax) demo = gr.Interface(\ntax_calculator, [ \"number\", gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\ngr.Dataframe( headers=[\"Item\", \"Cost\"], datatype=[\"str\", \"number\"],\nlabel=\"Assets Purchased this Year\", ), ], \"number\", examples=[ [10000,\n\"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]], [80000,\n\"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]], ], ) demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def tax_calculator(income, marital_status, assets):\n        tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\n        total_deductible = sum(assets[\"Cost\"])\n        taxable_income = income - total_deductible\n    \n        total_tax = 0\n        for bracket, rate in tax_brackets:\n            if taxable_income > bracket:\n                total_tax += (taxable_income - bracket) * rate / 100\n    \n        if marital_status == \"Married\":\n            total_tax *= 0.75\n        elif marital_status == \"Divorced\":\n            total_tax *= 0.8\n    \n        return round(total_tax)\n    \n    demo = gr.Interface(\n        tax_calculator,\n        [\n            \"number\",\n            gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\n            gr.Dataframe(\n                headers=[\"Item\", \"Cost\"],\n                datatype=[\"str\", \"number\"],\n                label=\"Assets Purchased this Year\",\n            ),\n        ],\n        \"number\",\n        examples=[\n            [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n         ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "          label=\"Assets Purchased this Year\",\n            ),\n        ],\n        \"number\",\n        examples=[\n            [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n            [80000, \"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]],\n        ],\n    )\n    \n    demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr demo = gr.Blocks(css=\"\"\"btn {color: red} .abc\n{font-family: \"Comic Sans MS\", \"Comic Sans\", cursive !important}\"\"\") with\ndemo: default_json = {\"a\": \"a\"} num = gr.State(value=0) squared =\ngr.Number(value=0) btn = gr.Button(\"Next Square\", elem_id=\"btn\",\nelem_classes=[\"abc\", \"def\"]) stats = gr.State(value=default_json) table =\ngr.JSON() def increase(var, stats_history): var += 1 stats_history[str(var)] =\nvar**2 return var, var**2, stats_history, stats_history btn.click(increase,\n[num, stats], [num, squared, stats, table]) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    demo = gr.Blocks(css=\"\"\"btn {color: red} .abc {font-family: \"Comic Sans MS\", \"Comic Sans\", cursive !important}\"\"\")\n    \n    with demo:\n        default_json = {\"a\": \"a\"}\n    \n        num = gr.State(value=0)\n        squared = gr.Number(value=0)\n        btn = gr.Button(\"Next Square\", elem_id=\"btn\", elem_classes=[\"abc\", \"def\"])\n    \n        stats = gr.State(value=default_json)\n        table = gr.JSON()\n    \n        def increase(var, stats_history):\n            var += 1\n            stats_history[str(var)] = var**2\n            return var, var**2, stats_history, stats_history\n    \n        btn.click(increase, [num, stats], [num, squared, stats, table])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Number component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Number.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Number changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Number.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Number.  \n`Number.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the Number is focused.  \n`Number.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Number is focused.  \n`Number.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Number is unfocused/blurred.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefau", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": " inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enab", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "n on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/number", "source_page_title": "Gradio - Number Docs"}, {"text": "Group is a layout element within Blocks which groups together children so\nthat they do not have any padding or margin between them.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "with gr.Group():\n        gr.Textbox(label=\"First\")\n        gr.Textbox(label=\"Last\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, group will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/group", "source_page_title": "Gradio - Group Docs"}, {"text": "This class is a wrapper over the Dataset component and can be used to\ncreate Examples for Blocks / Interfaces. Populates the Dataset component with\nexamples and assigns event listener so that clicking on an example populates\nthe input/output components. Optionally handles example caching for fast\ninference.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    examples: list[Any] | list[list[Any]] | str\n\nexample inputs that can be clicked to populate specific components. Should be\nnested list, in which the outer list consists of samples and each inner list\nconsists of an input corresponding to each input component. A string path to a\ndirectory of examples can also be provided but it should be within the\ndirectory with the python file running the gradio app. If there are multiple\ninput components and a directory is provided, a log.csv file must be present\nin the directory to link corresponding inputs.\n\n\n    \n    \n    inputs: Component | list[Component]\n\nthe component or list of components corresponding to the examples\n\n\n    \n    \n    outputs: Component | list[Component] | None\n\ndefault `= None`\n\noptionally, provide the component or list of components corresponding to the\noutput of the examples. Required if `cache_examples` is not False.\n\n\n    \n    \n    fn: Callable | None\n\ndefault `= None`\n\noptionally, provide the function to run to generate the outputs corresponding\nto the examples. Required if `cache_examples` is not False. Also required if\n`run_on_click` is True.\n\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES\nenvironment variable, which should be either \"true\" or \"false\". In HuggingFace\nSpaces, this parameter is True (as long as `fn` and `outputs` are also\nprovided). The default option otherwise is False. Note that examples are\ncached separately from Gradio's queue() so certain features, such as\ngr.Progress(), gr.Info(), gr.Warning(), etc. will not be displayed in Gradio's\nUI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "d in Gradio's\nUI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare cached at app launch. If None, will use the GRADIO_CACHE_MODE environment\nvariable if defined, or default to \"eager\".\n\n\n    \n    \n    examples_per_page: int\n\ndefault `= 10`\n\nhow many examples to show per page.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= \"Examples\"`\n\nthe label to use for the examples component (by default, \"Examples\")\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM.\n\n\n    \n    \n    run_on_click: bool\n\ndefault `= False`\n\nif cache_examples is False, clicking on an example does not run the function\nwhen an example is clicked. Set this to True to run the function when an\nexample is clicked. Has no effect if cache_examples is True.\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nif True, preprocesses the example input before running the prediction function\nand caching the output. Only applies if `cache_examples` is not False.\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nif True, postprocesses the example output after running the prediction\nfunction and before caching. Only applies if `cache_examples` is not False.\n\n\n    \n    \n    show_api: bool\n\ndefault `= False`\n\nWhether to show the event associated with clicking on the examples in the\n\"view API\" page of the Gradio app, or in the \".view_api()\" method of the\nGradio clients.\n\n\n    \n    \n    api_name: str | Literal[False]\n\ndefault `= \"load_example\"`\n\nDefines how the event associated with clicking on the examples appears in the\nAPI docs. Can be a string or False. If set to a string, the endpoint will be\nexposed in the API docs with the given name. If False, the endpoint will not\nbe exposed in the API docs and downstream apps (including those that `gr.load`\nthis app) will not be able to use the example ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "posed in the API docs with the given name. If False, the endpoint will not\nbe exposed in the API docs and downstream apps (including those that `gr.load`\nthis app) will not be able to use the example function.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the event associated with clicking on the examples in the API\ndocs. Can be a string, None, or False. If set to a string, the endpoint will\nbe exposed in the API docs with the given description. If None, the function's\ndocstring will be used as the API endpoint description. If False, then no\ndescription will be displayed in the API docs.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. Used only if\ncache_examples is not False.\n\n\n    \n    \n    example_labels: list[str] | None\n\ndefault `= None`\n\nA list of labels for each example. If provided, the length of this list should\nbe the same as the number of examples, and these labels will be used in the UI\ninstead of rendering the example values.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, the examples component will be hidden in the UI.\n\n\n    \n    \n    preload: int | Literal[False]\n\ndefault `= False`\n\nIf an integer is provided (and examples are being cached), the example at that\nindex in the examples list will be preloaded when the Gradio app is loaded. If\nFalse, no example will be preloaded.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    dataset: gradio.Dataset\n\nThe `gr.Dataset` component corresponding to this Examples object.\n\n\n    \n    \n    load_input_event: gradio.events.Dependency\n\nThe Gradio event that populates the input values when the examples are\nclicked. You can attach a `.then()` or a `.success()` to this event to trigger\nsubsequent events to fire after this event.\n\n\n    \n    \n    cache_event: gradio.events.Dependency | None\n\nThe Gradio event that populates the cached output values when the examples are\nclicked. You can attach a `.then()` or a `.success()` to this event to trigger\nsubsequent events to fire after this event. This event is `None` if\n`cache_examples` if False, and is the same as `load_input_event` if\n`cache_examples` is `'lazy'`.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "**Updating Examples**\n\nIn this demo, we show how to update the examples by updating the samples of\nthe underlying dataset. Note that this only works if `cache_examples=False` as\nupdating the underlying dataset does not update the cache.\n\n    \n    \n    import gradio as gr\n    \n    def update_examples(country):\n        if country == \"USA\":\n            return gr.Dataset(samples=[[\"Chicago\"], [\"Little Rock\"], [\"San Francisco\"]])\n        else:\n            return gr.Dataset(samples=[[\"Islamabad\"], [\"Karachi\"], [\"Lahore\"]])\n    \n    with gr.Blocks() as demo:\n        dropdown = gr.Dropdown(label=\"Country\", choices=[\"USA\", \"Pakistan\"], value=\"USA\")\n        textbox = gr.Textbox()\n        examples = gr.Examples([[\"Chicago\"], [\"Little Rock\"], [\"San Francisco\"]], textbox)\n        dropdown.change(update_examples, dropdown, examples.dataset)\n        \n    demo.launch()\n\n", "heading1": "Examples", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "calculator_blocks\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def calculator(num1, operation, num2): if\noperation == \"add\": return num1 + num2 elif operation == \"subtract\": return\nnum1 - num2 elif operation == \"multiply\": return num1 * num2 elif operation ==\n\"divide\": return num1 / num2 with gr.Blocks() as demo: with gr.Row(): with\ngr.Column(): num_1 = gr.Number(value=4) operation = gr.Radio([\"add\",\n\"subtract\", \"multiply\", \"divide\"]) num_2 = gr.Number(value=0) submit_btn =\ngr.Button(value=\"Calculate\") with gr.Column(): result = gr.Number()\nsubmit_btn.click( calculator, inputs=[num_1, operation, num_2],\noutputs=[result], api_name=False ) examples = gr.Examples( examples=[ [5,\n\"add\", 3], [4, \"divide\", 2], [-4, \"multiply\", 2.5], [0, \"subtract\", 1.2], ],\ninputs=[num_1, operation, num_2], ) if __name__ == \"__main__\":\ndemo.launch(show_api=False)\n\n    \n    \n    import gradio as gr\n    \n    def calculator(num1, operation, num2):\n        if operation == \"add\":\n            return num1 + num2\n        elif operation == \"subtract\":\n            return num1 - num2\n        elif operation == \"multiply\":\n            return num1 * num2\n        elif operation == \"divide\":\n            return num1 / num2\n    \n    with gr.Blocks() as demo:\n        with gr.Row():\n            with gr.Column():\n                num_1 = gr.Number(value=4)\n                operation = gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"])\n                num_2 = gr.Number(value=0)\n                submit_btn = gr.Button(value=\"Calculate\")\n            with gr.Column():\n                result = gr.Number()\n    \n        submit_btn.click(\n            calculator, inputs=[num_1, operation, num_2], outputs=[result], api_name=False\n        )\n        examples = gr.Examples(\n            examples=[\n                [5, \"add\", 3],\n                [4, \"divide\", 2],\n                [-4, \"multiply\", 2.5],\n                [0, \"subtract\", 1.2],\n            ],\n            inputs=[num_1, operation, num_2],\n        )\n    \n    if __name__ == \"__mai", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "    [4, \"divide\", 2],\n                [-4, \"multiply\", 2.5],\n                [0, \"subtract\", 1.2],\n            ],\n            inputs=[num_1, operation, num_2],\n        )\n    \n    if __name__ == \"__main__\":\n        demo.launch(show_api=False)\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/examples", "source_page_title": "Gradio - Examples Docs"}, {"text": "Button that clears the value of a component or a list of components when\nclicked. It is instantiated with the list of components to clear.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "**As input component** : (Rarely used) the `str` corresponding to the\nbutton label when the button is clicked\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : string corresponding to the button label\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    components: None | list[Component] | Component\n\ndefault `= None`\n\n\n    \n    \n    value: str\n\ndefault `= \"Clear\"`\n\ndefault text for the button to display. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop']\n\ndefault `= \"secondary\"`\n\nsets the background and text color of the button. Use 'primary' for main call-\nto-action buttons, 'secondary' for a more subdued style, 'stop' for a stop\nbutton, 'huggingface' for a black background with white text, consistent with\nHugging Face's button styles.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | Path | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    link: str | None\n\ndefault `= None`\n\nURL to open when the button is clicked. If None, no link will be used.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nif False, the Button will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefa", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "one\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    api_name: str | None | Literal['False']\n\ndefault `= None`\n\n\n    \n    \n    show_api: bool\n\ndefault `= False`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "width parameter will be respected first.\n\n\n    \n    \n    api_name: str | None | Literal['False']\n\ndefault `= None`\n\n\n    \n    \n    show_api: bool\n\ndefault `= False`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ClearButton` | \"clearbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ClearButton component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ClearButton.add(fn, \u00b7\u00b7\u00b7)` | Adds a component or list of components to the list of components that will be cleared when the button is clicked.  \n`ClearButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Button is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    components: None | Component | list[Component]\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/clearbutton", "source_page_title": "Gradio - Clearbutton Docs"}, {"text": "The gr.LikeData class is a subclass of gr.EventData that specifically\ncarries information about the `.like()` event. When gr.LikeData is added as a\ntype hint to an argument of an event listener method, a gr.LikeData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "import gradio as gr\n    \n    def test(value, like_data: gr.LikeData):\n        return {\n            \"chatbot_value\": value,\n            \"liked_message\": like_data.value,\n            \"liked_index\": like_data.index,\n            \"liked_or_disliked_as_bool\": like_data.liked\n        }\n    \n    with gr.Blocks() as demo:\n        c = gr.Chatbot([(\"abc\", \"def\")])\n        t = gr.JSON()\n        c.like(test, c, t)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the liked/disliked item. Is a tuple if the component is two\ndimensional.\n\n\n    \n    \n    value: Any\n\nThe value of the liked/disliked item.\n\n\n    \n    \n    liked: bool\n\nTrue if the item was liked, False if disliked, or string value if any other\nfeedback.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "chatbot_core_components_simple\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import random Chatbot demo with multimodal\ninput (text, markdown, LaTeX, code blocks, image, audio, video, & model3d).\nPlus shows support for streaming text. color_map = { \"harmful\": \"crimson\",\n\"neutral\": \"gray\", \"beneficial\": \"green\", } def html_src(harm_level): return\nf\"\"\" <div style=\"display: flex; gap: 5px;\"> <div style=\"background-color:\n{color_map[harm_level]}; padding: 2px; border-radius: 5px;\"> {harm_level}\n</div> </div> \"\"\" def print_like_dislike(x: gr.LikeData): print(x.index,\nx.value, x.liked) def add_message(history, message): for x in\nmessage[\"files\"]: history.append(((x,), None)) if message[\"text\"] is not None:\nhistory.append((message[\"text\"], None)) return history,\ngr.MultimodalTextbox(value=None, interactive=False) def bot(history,\nresponse_type): if response_type == \"gallery\": history[-1][1] = gr.Gallery( [\n\"https://raw.githubusercontent.com/gradio-\napp/gradio/main/test/test_files/bus.png\",\n\"https://raw.githubusercontent.com/gradio-\napp/gradio/main/test/test_files/bus.png\", ] ) elif response_type == \"image\":\nhistory[-1][1] = gr.Image( \"https://raw.githubusercontent.com/gradio-\napp/gradio/main/test/test_files/bus.png\" ) elif response_type == \"video\":\nhistory[-1][1] = gr.Video( \"https://github.com/gradio-\napp/gradio/raw/main/demo/video_component/files/world.mp4\" ) elif response_type\n== \"audio\": history[-1][1] = gr.Audio( \"https://github.com/gradio-\napp/gradio/raw/main/test/test_files/audio_sample.wav\" ) elif response_type ==\n\"html\": history[-1][1] = gr.HTML( html_src(random.choice([\"harmful\",\n\"neutral\", \"beneficial\"])) ) elif response_type == \"model3d\": history[-1][1] =\ngr.Model3D( \"https://github.com/gradio-\napp/gradio/raw/main/test/test_files/Fox.gltf\" ) else: history[-1][1] = \"Cool!\"\nreturn history with gr.Blocks(fill_height=True) as demo: chatbot = gr.Chatbot(\nelem_id=\"chatbot\", bubble_full_width=False, scale=1, ) response_type =\ngr.Radio( [ \"image\", \"text\", \"gallery\", \"video\", \"aud", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "urn history with gr.Blocks(fill_height=True) as demo: chatbot = gr.Chatbot(\nelem_id=\"chatbot\", bubble_full_width=False, scale=1, ) response_type =\ngr.Radio( [ \"image\", \"text\", \"gallery\", \"video\", \"audio\", \"html\", \"model3d\",\n], value=\"text\", label=\"Response Type\", ) chat_input = gr.MultimodalTextbox(\ninteractive=True, placeholder=\"Enter message or upload file...\",\nshow_label=False, ) chat_msg = chat_input.submit( add_message, [chatbot,\nchat_input], [chatbot, chat_input] ) bot_msg = chat_msg.then( bot, [chatbot,\nresponse_type], chatbot, api_name=\"bot_response\" ) bot_msg.then(lambda:\ngr.MultimodalTextbox(interactive=True), None, [chat_input])\nchatbot.like(print_like_dislike, None, None) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    import random\n    \n    Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, video, & model3d). Plus shows support for streaming text.\n    \n    color_map = {\n        \"harmful\": \"crimson\",\n        \"neutral\": \"gray\",\n        \"beneficial\": \"green\",\n    }\n    \n    def html_src(harm_level):\n        return f\"\"\"\n    \n    \n    \n      \n    \n    \n      {harm_level}\n      \n    \n    \n    \n    \n    \n    \"\"\"\n    \n    def print_like_dislike(x: gr.LikeData):\n        print(x.index, x.value, x.liked)\n    \n    def add_message(history, message):\n        for x in message[\"files\"]:\n            history.append(((x,), None))\n        if message[\"text\"] is not None:\n            history.append((message[\"text\"], None))\n        return history, gr.MultimodalTextbox(value=None, interactive=False)\n    \n    def bot(history, response_type):\n        if response_type == \"gallery\":\n            history[-1][1] = gr.Gallery(\n                [\n                    \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\",\n                    \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\",\n                ]\n            )\n        elif response_type == \"image\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "/test/test_files/bus.png\",\n                    \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\",\n                ]\n            )\n        elif response_type == \"image\":\n            history[-1][1] = gr.Image(\n                \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\"\n            )\n        elif response_type == \"video\":\n            history[-1][1] = gr.Video(\n                \"https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4\"\n            )\n        elif response_type == \"audio\":\n            history[-1][1] = gr.Audio(\n                \"https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav\"\n            )\n        elif response_type == \"html\":\n            history[-1][1] = gr.HTML(\n                html_src(random.choice([\"harmful\", \"neutral\", \"beneficial\"]))\n            )\n        elif response_type == \"model3d\":\n            history[-1][1] = gr.Model3D(\n               \"https://github.com/gradio-app/gradio/raw/main/test/test_files/Fox.gltf\"\n            )\n        else:\n            history[-1][1] = \"Cool!\"\n        return history\n    \n    with gr.Blocks(fill_height=True) as demo:\n        chatbot = gr.Chatbot(\n            elem_id=\"chatbot\",\n            bubble_full_width=False,\n            scale=1,\n        )\n        response_type = gr.Radio(\n            [\n                \"image\",\n                \"text\",\n                \"gallery\",\n                \"video\",\n                \"audio\",\n                \"html\",\n                \"model3d\",\n            ],\n            value=\"text\",\n            label=\"Response Type\",\n        )\n    \n        chat_input = gr.MultimodalTextbox(\n            interactive=True,\n            placeholder=\"Enter message or upload file...\",\n            show_label=False,\n        )\n    \n        chat_msg = chat_input.submit(\n            add_message, [chatbot, chat_input], [chatbot, chat_input]\n        )\n        bot_msg = chat_msg.then(\n            bo", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "  show_label=False,\n        )\n    \n        chat_msg = chat_input.submit(\n            add_message, [chatbot, chat_input], [chatbot, chat_input]\n        )\n        bot_msg = chat_msg.then(\n            bot, [chatbot, response_type], chatbot, api_name=\"bot_response\"\n        )\n        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n    \n        chatbot.like(print_like_dislike, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/likedata", "source_page_title": "Gradio - Likedata Docs"}, {"text": "Creates a set of checkboxes. Can be used as an input to pass a set of\nvalues to a function or as an output to display values, a subset of which are\nselected.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "**As input component** : Passes the list of checked checkboxes as a `list[str | int | float]` or their indices as a `list[int]` into the function, depending on `type`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: list[str | int | float] | list[int | None]\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `list[str | int | float]` of values or a single `str | int | float` value, the checkboxes with these values are checked.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> list[str | int | float] | str | int | float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    choices: list[str | int | float | tuple[str, str | int | float]] | None\n\ndefault `= None`\n\nA list of string or numeric options to select from. An option can also be a\ntuple of the form (name, value), where name is the displayed name of the\ncheckbox button and value is the value to be passed to the function, or\nreturned by the function.\n\n\n    \n    \n    value: list[str | float | int] | str | float | int | Callable | None\n\ndefault `= None`\n\nDefault selected list of options. If a single choice is selected, it can be\npassed in as a string or numeric type. If a function is provided, the function\nwill be called each time the app loads to set the initial value of this\ncomponent.\n\n\n    \n    \n    type: Literal['value', 'index']\n\ndefault `= \"value\"`\n\nType of value to be returned by component. \"value\" returns the list of strings\nof the choices selected, \"index\" returns the list of indices of the choices\nselected.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n   ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "nt] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nIf True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nRelative width compared to adjacent Components in a Row. For example, if\nComponent A has scale=2, and Component B has scale=1, A will be twice as wide\nas B. Should be an integer.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nMinimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, choices in this checkbox group will be checkable; if False, checking\nwill be disabled. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-render", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "ssign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.CheckboxGroup` | \"checkboxgroup\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "sentence_builder\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "  gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe CheckboxGroup component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`CheckboxGroup.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the CheckboxGroup changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`CheckboxGroup.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the CheckboxGroup.  \n`CheckboxGroup.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the CheckboxGroup. Uses event data gradio.SelectData to carry `value` referring to the label of the CheckboxGroup, and `selected` to refer to state of the CheckboxGroup. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | Bl", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "ne\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will p", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "onent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions w", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "ed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.rende", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkboxgroup", "source_page_title": "Gradio - Checkboxgroup Docs"}, {"text": "Sets up an API or MCP endpoint for a generic function without needing\ndefine events listeners or components. Derives its typing from type hints in\nthe provided function's signature rather than the components.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/api", "source_page_title": "Gradio - Api Docs"}, {"text": "import gradio as gr\n    with gr.Blocks() as demo:\n        with gr.Row():\n            input = gr.Textbox()\n            button = gr.Button(\"Submit\")\n        output = gr.Textbox()\n        def fn(a: int, b: int, c: list[str]) -> tuple[int, str]:\n            return a + b, c[a:b]\n        gr.api(fn, api_name=\"add_and_slice\")\n    _, url, _ = demo.launch()\n    \n    from gradio_client import Client\n    client = Client(url)\n    result = client.predict(\n            a=3,\n            b=5,\n            c=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n            api_name=\"/add_and_slice\"\n    )\n    print(result)\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/api", "source_page_title": "Gradio - Api Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    fn: Callable | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. The function should be fully typed, and the type\nhints will be used to derive the typing information for the API/MCP endpoint.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\nDefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If False, the endpoint will not be exposed in the api docs. If set to\nNone, will use the functions name as the endpoint route. If set to a string,\nthe endpoint will be exposed in the api docs with the given name.\n\n\n    \n    \n    api_description: str | None\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nI", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/api", "source_page_title": "Gradio - Api Docs"}, {"text": "\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\nThe time limit for the function to run. Parameter only used for the\n`.stream()` event.\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for the `.stream()` event.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/api", "source_page_title": "Gradio - Api Docs"}, {"text": "Sets up an event listener that triggers a function when the specified\nevent(s) occur. This is especially useful when the same function should be\ntriggered by multiple events. Only a single API endpoint is generated for all\nevents in the triggers list.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "import gradio as gr\n    \n    with gr.Blocks() as demo:\n        with gr.Row():\n            input = gr.Textbox()\n            button = gr.Button(\"Submit\")\n        output = gr.Textbox()\n        gr.on(\n            triggers=[button.click, input.submit],\n            fn=lambda x: x,\n            inputs=[input],\n            outputs=[output]\n        )\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    triggers: list[EventListenerCallable] | EventListenerCallable | None\n\ndefault `= None`\n\nList of triggers to listen to, e.g. [btn.click, number.change]. If None, will\nrun on app load and changes to any inputs.\n\n\n    \n    \n    fn: Callable[..., Any] | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\nDefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If False, the endpoint will not be exposed in the api docs. If set to\nNone, will use the functions name as the endpoint route. If set to a string,\nthe endpoint will be exposed in the api docs with the given name.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\n\n    \n    \n    scroll_to_output: bool\n\nde", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "to a string,\nthe endpoint will be exposed in the api docs with the given name.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all,\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run pos", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "onent data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs', return should be a list of values for output\ncomponents.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowes", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\nThe time limit for the function to run. Parameter only used for the\n`.stream()` event.\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for the `.stream()` event.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/on", "source_page_title": "Gradio - On Docs"}, {"text": "Load a chat interface from an OpenAI API chat compatible endpoint.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/load_chat", "source_page_title": "Gradio - Load_Chat Docs"}, {"text": "import gradio as gr\n    demo = gr.load_chat(\"http://localhost:11434/v1\", model=\"deepseek-r1\")\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/load_chat", "source_page_title": "Gradio - Load_Chat Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    base_url: str\n\nThe base URL of the endpoint, e.g. \"http://localhost:11434/v1/\"\n\n\n    \n    \n    model: str\n\nThe name of the model you are loading, e.g. \"llama3.2\"\n\n\n    \n    \n    token: str | None\n\ndefault `= None`\n\nThe API token or a placeholder string if you are using a local model, e.g.\n\"ollama\"\n\n\n    \n    \n    file_types: Literal['text_encoded', 'image'] | list[Literal['text_encoded', 'image']] | None\n\ndefault `= \"text_encoded\"`\n\nThe file types allowed to be uploaded by the user. \"text_encoded\" allows\nuploading any text-encoded file (which is simply appended to the prompt), and\n\"image\" adds image upload support. Set to None to disable file uploads.\n\n\n    \n    \n    system_message: str | None\n\ndefault `= None`\n\nThe system message to use for the conversation, if any.\n\n\n    \n    \n    streaming: bool\n\ndefault `= True`\n\nWhether the response should be streamed.\n\n\n    \n    \n    kwargs: <class 'inspect._empty'>\n\nAdditional keyword arguments to pass into ChatInterface for customization.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/load_chat", "source_page_title": "Gradio - Load_Chat Docs"}, {"text": "Interface is Gradio's main high-level class, and allows you to create a\nweb-based GUI / demo around a machine learning model (or any Python function)\nin a few lines of code. You must specify three parameters: (1) the function to\ncreate a GUI for (2) the desired input components and (3) the desired output\ncomponents. Additional parameters can be used to control the appearance and\nbehavior of the demo.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "import gradio as gr\n    \n    def image_classifier(inp):\n        return {'cat': 0.3, 'dog': 0.7}\n    \n    demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\")\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    fn: Callable\n\nthe function to wrap an interface around. Often a machine learning model's\nprediction function. Each parameter of the function corresponds to one input\ncomponent, and the function should return a single value or a tuple of values,\nwith each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of input components should match the number of parameters in fn. If\nset to None, then only the output components will be displayed.\n\n\n    \n    \n    outputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of output components should match the number of values returned by\nfn. If set to None, then only the input components will be displayed.\n\n\n    \n    \n    examples: list[Any] | list[list[Any]] | str | None\n\ndefault `= None`\n\nsample inputs for the function; if provided, appear below the UI components\nand can be clicked to populate the interface. Should be nested list, in which\nthe outer list consists of samples and each inner list consists of an input\ncorresponding to each input component. A string path to a directory of\nexamples can also be provided, but it should be within the directory with the\npython file running the gradio app. If there are multiple input components and\na directory is provided, a log.csv file must be present in the directory to\nlink corresponding inputs.\n\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES\nenvironment variable, which should be either \"true\" or \"false\". In HuggingFace\nSpaces, this parameter defaults to True (as long as `fn` and `outputs` are\nalso provided). Note that examples are cached separately from Gradio's queue()\nso certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will\nnot be displayed in Gradio's UI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare cached at app launch. If None, will use the GRADIO_CACHE_MODE environment\nvariable if defined, or default to \"eager\". In HuggingFace Spaces, this\nparameter defaults to \"eager\" except for ZeroGPU Spaces, in which case it\ndefaults to \"lazy\".\n\n\n    \n    \n    examples_per_page: int\n\ndefault `= 10`\n\nif examples are provided, how many to display per page.\n\n\n    \n    \n    example_labels: list[str] | None\n\ndefault `= None`\n\na list of labels for each example. If provided, the length of this list should\nbe the same as the number of examples, and these labels will be used in the UI\ninstead of rendering the example values.\n\n\n    \n    \n    preload_example: int | Literal[False]\n\ndefault `= False`\n\nIf an integer is provided (and examples are being cached), the example at that\nindex in the examples list will be preloaded when the Gradio app is first\nloaded. If False, no example will be preloaded.\n\n\n    \n    \n    live: bool\n\ndefault `= False`\n\nwhether the interface should automatically rerun if any of the inputs change.\n\n\n    \n    \n    title: str | I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n    \n    \n    description: str | None\n\ndefault `= None`\n\na description for the interface; if provided, appears above the input and\noutput components and beneath the title in regular font. Accepts Markdown and\nHTML content.\n\n\n    \n    \n    article: str | None\n\ndefault `= None`\n\nan expanded article explaining the interface; if provided, appears below the\ninput and output components in regular font. Accepts Markdown and HTML\ncontent. If it is an HTTP(S) link to a downloadable remote file, the content\nof this file is displayed.\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\na Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    flagging_mode: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\none of \"never\", \"auto\", or \"manual\". If \"never\" or \"auto\", users will not see\na button to flag an input and output. If \"manual\", users will see a button to\nflag. If \"auto\", every input the user submits will be automatically flagged,\nalong with the generated output. If \"manual\", both the input and outputs are\nflagged when the user clicks flag button. This parameter can be set with\nenvironmental variable GRADIO_FLAGGING_MODE; otherwise defaults to \"manual\".\n\n\n    \n    \n    flagging_options: list[str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nif provided, allows user to select from the list of options when flagging.\nOnly applies if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "es if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored in the flagging CSV; or\nit can be a list of strings [\"X\", \"Y\"], in which case the values will be the\nlist of strings and the labels will [\"Flag as X\", \"Flag as Y\"], etc.\n\n\n    \n    \n    flagging_dir: str\n\ndefault `= \".gradio/flagged\"`\n\npath to the the directory where flagged data is stored. If the directory does\nnot exist, it will be created.\n\n\n    \n    \n    flagging_callback: FlaggingCallback | None\n\ndefault `= None`\n\neither None or an instance of a subclass of FlaggingCallback which will be\ncalled when a sample is flagged. If set to None, an instance of\ngradio.flagging.CSVLogger will be created and logs will be saved to a local\nCSV file in flagging_dir. Default to None.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nif True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nthe maximum number of inputs to batch together if this is called from the\nqueue (only relevant if batch=True)\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show the prediction endpoint in the \"view API\" page of the Gradio\napp, or in the \".view_api()\" method of the Gradio clients. Unlike setting\napi_name to False, setting show_api to False will still allow downstream apps\nas well as the Clients to use this event.\n\n\n    \n    \n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " in the \".view_api()\" method of the Gradio clients. Unlike setting\napi_name to False, setting show_api to False will still allow downstream apps\nas well as the Clients to use this event.\n\n\n    \n    \n    api_name: str | Literal[False] | None\n\ndefault `= \"predict\"`\n\ndefines how the prediction endpoint appears in the API docs. Can be a string,\nNone, or False. If set to a string, the endpoint will be exposed in the API\ndocs with the given name. If None, the name of the prediction function will be\nused as the API endpoint. If False, the endpoint will not be exposed in the\nAPI docs and downstream apps (including those that `gr.load` this app) will\nnot be able to use this prediction endpoint.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    allow_duplication: bool\n\ndefault `= False`\n\nif True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nif set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`.queue()`, which itself is 1 by default).\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n    \n    \n    css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo web", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a code string. The custom js should be in the form of a single js\nfunction. This function will automatically be executed when the page loads.\nFor more flexibility, use the head parameter to insert js inside <script>\ntags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html code to insert into the head of the demo webpage. This can be used\nto add custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n\n    \n    \n    head_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` parameter is also set, the html from `head` will\nbe included first.\n\n\n    \n    \n    additional_inputs: str | Component | list[str | Component] | None\n\ndefault `= None`\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThese components will be rendered in an accordion below the main input\ncomponents. By default, no additional input components will be displayed.\n\n\n    \n    \n    additional_inputs_accordion: str | Accordion | None\n\ndefault `= None`\n\nif a string is provided, this is the label of the `gr.Accordion` to use to\ncontain additional inputs. A `gr.Accordion` object can be provided as well to\nconfigure other properties of the container holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    submit_btn: ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ntainer holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    submit_btn: str | Button\n\ndefault `= \"Submit\"`\n\nthe button to use for submitting inputs. Defaults to a `gr.Button(\"Submit\",\nvariant=\"primary\")`. This parameter does not apply if the Interface is output-\nonly, in which case the submit button always displays \"Generate\". Can be set\nto a string (which becomes the button label) or a `gr.Button` object (which\nallows for more customization).\n\n\n    \n    \n    stop_btn: str | Button\n\ndefault `= \"Stop\"`\n\nthe button to use for stopping the interface. Defaults to a `gr.Button(\"Stop\",\nvariant=\"stop\", visible=False)`. Can be set to a string (which becomes the\nbutton label) or a `gr.Button` object (which allows for more customization).\n\n\n    \n    \n    clear_btn: str | Button | None\n\ndefault `= \"Clear\"`\n\nthe button to use for clearing the inputs. Defaults to a `gr.Button(\"Clear\",\nvariant=\"secondary\")`. Can be set to a string (which becomes the button label)\nor a `gr.Button` object (which allows for more customization). Can be set to\nNone, which hides the button.\n\n\n    \n    \n    delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\na tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "gress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    fill_width: bool\n\ndefault `= False`\n\nwhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width.\n\n\n    \n    \n    allow_flagging: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= 30`\n\nThe time limit for the stream to run. Default is 30 seconds. Parameter only\nused for streaming images or audio if the interface is live and the input\ncomponents are set to \"streaming=True\".\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for streaming images or audio if\nthe interface is live and the input components are set to \"streaming=True\".\n\n\n    \n    \n    deep_link: str | DeepLinkButton | bool | None\n\ndefault `= None`\n\na string or `gr.DeepLinkButton` object that creates a unique URL you can use\nto share your app and all components **as they currently are** with others.\nAutomatically enabled on Hugging Face Spaces unless explicitly set to False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "hello_worldhello_world_2hello_world_3\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name): return \"Hello \" + name + \"!\"\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\") if __name__\n== \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    \n    def greet(name):\n        return \"Hello \" + name + \"!\"\n    \n    \n    demo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, intensity): return \"Hello, \" +\nname + \"!\" * intensity demo = gr.Interface( fn=greet, inputs=[\"text\",\ngr.Slider(value=2, minimum=1, maximum=10, step=1)],\noutputs=[gr.Textbox(label=\"greeting\", lines=3)], ) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, intensity):\n        return \"Hello, \" + name + \"!\" * intensity\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", gr.Slider(value=2, minimum=1, maximum=10, step=1)],\n        outputs=[gr.Textbox(label=\"greeting\", lines=3)],\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, is_morning, temperature):\nsalutation = \"Good morning\" if is_morning else \"Good evening\" greeting =\nf\"{salutation} {name}. It is {temperature} degrees today\" celsius =\n(temperature - 32) * 5 / 9 return greeting, round(celsius, 2) demo =\ngr.Interface( fn=greet, inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\noutputs=[\"text\", \"number\"], ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, is_morning, temperature):\n        salutation = \"Good morning\" if is_morning else \"Good evening\"\n        greeting = f\"{salutation} {name}. It is {temperature} degrees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 10", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "grees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n        outputs=[\"text\", \"number\"],\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.launch(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nLaunches a simple web server that serves the demo. Can also be used to create\na public link used by anyone to access the demo from their browser by setting\nshare=True.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    def reverse(text):\n        return text[::-1]\n    demo = gr.Interface(reverse, \"text\", \"text\")\n    demo.launch(share=True, auth=(\"username\", \"password\"))\n\nParameters \u25bc\n\n\n    \n    \n    inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the de", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "pp inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the default\nbrowser.\n\n\n    \n    \n    share: bool | None\n\ndefault `= None`\n\nwhether to create a publicly shareable link for the gradio app. Creates an SSH\ntunnel to make your UI accessible from anywhere. If not provided, it is set to\nFalse by default every time, except when running in Google Colab. When\nlocalhost is not accessible (e.g. Google Colab), setting share=False is not\nsupported. Can be set by environment variable GRADIO_SHARE=True.\n\n\n    \n    \n    debug: bool\n\ndefault `= False`\n\nif True, blocks the main thread from running. If running in Google Colab, this\nis needed to print the errors in the cell output.\n\n\n    \n    \n    max_threads: int\n\ndefault `= 40`\n\nthe maximum number of total threads that the Gradio app can generate in\nparallel. The default is inherited from the starlette library (currently 40).\n\n\n    \n    \n    auth: Callable[[str, str], bool] | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access app. Can also provide function that takes username and\npassword and returns True if valid login.\n\n\n    \n    \n    auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page.\n\n\n    \n    \n    prevent_thread_lock: bool\n\ndefault `= False`\n\nBy default, the gradio app blocks the main thread while the server is running.\nIf set to True, the gradio app will not block and the gradio server will\nterminate as soon as the script finishes.\n\n\n    \n    \n    show_error: bool\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= No", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= None`\n\nto make app accessible on local network, set this to \"0.0.0.0\". Can be set by\nenvironment variable GRADIO_SERVER_NAME. If None, will use \"127.0.0.1\".\n\n\n    \n    \n    server_port: int | None\n\ndefault `= None`\n\nwill start gradio app on this port (if available). Can be set by environment\nvariable GRADIO_SERVER_PORT. If None, will search for an available port\nstarting at 7860.\n\n\n    \n    \n    height: int\n\ndefault `= 500`\n\nThe height in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    width: int | str\n\ndefault `= \"100%\"`\n\nThe width in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    favicon_path: str | Path | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for the web page.\n\n\n    \n    \n    ssl_keyfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the private key file to\ncreate a local server running on https.\n\n\n    \n    \n    ssl_certfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the signed certificate for\nhttps. Needs to be provided if ssl_keyfile is provided.\n\n\n    \n    \n    ssl_keyfile_password: str | None\n\ndefault `= None`\n\nIf a password is provided, will use this with the ssl certificate for https.\n\n\n    \n    \n    ssl_verify: bool\n\ndefault `= True`\n\nIf False, skips certificate validation which allows self-signed certificates\nto be used.\n\n\n    \n    \n    quiet: bool\n\ndefault `= False`\n\nIf True, suppresses most print statements.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent di", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": ": bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is allowed to\nserve. Must be absolute paths. Warning: if you provide directories, any files\nin these directories or their subdirectories are accessible to all users of\nyour app. Can be set by comma separated environment variable\nGRADIO_ALLOWED_PATHS. These files are generally assumed to be secure and will\nbe displayed in the browser when possible.\n\n\n    \n    \n    blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is not allowed to\nserve (i.e. users of your app are not allowed to access). Must be absolute\npaths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default. Can be set by comma separated\nenvironment variable GRADIO_BLOCKED_PATHS.\n\n\n    \n    \n    root_path: str | None\n\ndefault `= None`\n\nThe root path (or \"mount point\") of the application, if it's not served from\nthe root (\"/\") of the domain. Often used when the application is behind a\nreverse proxy that forwards requests to the application. For example, if the\napplication is served at \"https://example.com/myapp\", the `root_path` should\nbe set to \"/myapp\". A full URL beginning with http:// or https:// can be\nprovided, which will be used as the root path in its entirety. Can be set by\nenvironment variable GRADIO_ROOT_PATH. Defaults to \"\".\n\n\n    \n    \n    app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capac", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capacity to reduce memory usage when using gradio.State or returning\nupdated components from functions. Defaults to 10000.\n\n\n    \n    \n    share_server_address: str | None\n\ndefault `= None`\n\nUse this to specify a custom FRP server and port for sharing Gradio apps (only\napplies if share=True). If not provided, will use the default FRP server at\nhttps://gradio.live. See https://github.com/huggingface/frp for more\ninformation.\n\n\n    \n    \n    share_server_protocol: Literal['http', 'https'] | None\n\ndefault `= None`\n\nUse this to specify the protocol to use for the share links. Defaults to\n\"https\", unless a custom share_server_address is provided, in which case it\ndefaults to \"http\". If you are using a custom share_server_address and want to\nuse https, you must set this to \"https\".\n\n\n    \n    \n    share_server_tls_certificate: str | None\n\ndefault `= None`\n\nThe path to a TLS certificate file to use when connecting to a custom share\nserver. This parameter is not used with the default FRP server at\nhttps://gradio.live. Otherwise, you must provide a valid TLS certificate file\n(e.g. a \"cert.pem\") relative to the current working directory, or the\nconnection will not use TLS encryption, which is insecure.\n\n\n    \n    \n    auth_dependency: Callable[[fastapi.Request], str | None] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the app (they will see a 401 Unauthorized response). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any ", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ed with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    enable_monitoring: bool | None\n\ndefault `= None`\n\nEnables traffic monitoring of the app through the /monitoring endpoint. By\ndefault is None, which enables this endpoint. If explicitly True, will also\nprint the monitoring URL to the console. If False, will disable monitoring\naltogether.\n\n\n    \n    \n    strict_cors: bool\n\ndefault `= True`\n\nIf True, prevents external domains from making requests to a Gradio server\nrunning on localhost. If False, allows requests to localhost that originate\nfrom localhost but also, crucially, from \"null\". This parameter should\nnormally be True to prevent CSRF attacks but may need to be False when\nembedding a *locally-running Gradio app* using web components.\n\n\n    \n    \n    node_server_name: str | None\n\ndefault `= None`\n\n\n    \n    \n    node_port: int | None\n\ndefault `= None`\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 20+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n    \n    \n    pwa: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an installable PWA (Progressive Web\nApp). If set to None (default behavior), then the PWA feature will be enabled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_M", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "   mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_MCP_SERVER environment variable will be used to determine if the MCP\nserver should be enabled (which is \"True\" on Hugging Face Spaces).\n\n\n    \n    \n    i18n: I18n | None\n\ndefault `= None`\n\nAn I18n instance containing custom translations, which are used to translate\nstrings in our components (e.g. the labels of components or Markdown strings).\nThis feature can only be used to translate static text in the frontend, not\nvalues in the backend.\n\n", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.load(block, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%2", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Interface initially loads in the browser.\n\nParameters \u25bc\n\n\n    \n    \n    block: Block | None\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple correspondin", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime ", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "w the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click met", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "f other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api w", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ew_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.from_pipeline(pipeline, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nClass method that constructs an Interface from a Hugging Face\ntransformers.Pipeline or diffusers.DiffusionPipeline object. The input and\noutput components are automatically determined from the pipeline.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Co", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    from transformers import pipeline\n    pipe = pipeline(\"image-classification\")\n    gr.Interface.from_pipeline(pipe).launch()\n\nParameters \u25bc\n\n\n    \n    \n    pipeline: Pipeline | DiffusionPipeline\n\nthe pipeline object to use.\n\n", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ne object to use.\n\n", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.integrate(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20I", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nA catch-all method for integrating with other libraries. This method should be\nrun after launch()\n\nParameters \u25bc\n\n\n    \n    \n    comet_ml: <class 'inspect._empty'>\n\ndefault `= None`\n\nIf a comet_ml Experiment object is provided, will integrate with the\nexperiment and appear on Comet dashboard\n\n\n    \n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "\n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n\nIf the mlflow module is provided, will integrate with the experiment and\nappear on ML Flow dashboard\n\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.queue(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nBy enabling the queue you can control when users know their position in the\nqueue, and set a limit on maximum number of events allowed.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "ro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())\n    demo.queue(max_size=20)\n    demo.launch()\n\nParameters \u25bc\n\n\n    \n    \n    status_update_rate: float | Literal['auto']\n\ndefault `= \"auto\"`\n\nIf \"auto\", Queue will send status estimations to all clients whenever a job is\nfinished. Otherwise Queue will send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowin", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": " send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowing requests made\ndirectly to those endpoints to skip the queue.\n\n\n    \n    \n    max_size: int | None\n\ndefault `= None`\n\nThe maximum number of events the queue will store at any given moment. If the\nqueue is full, new events will not be added and a user will receive a message\nsaying that the queue is full. If None, the queue size will be unlimited.\n\n\n    \n    \n    default_concurrency_limit: int | None | Literal['not_set']\n\ndefault `= \"not_set\"`\n\nThe default value of `concurrency_limit` to use for event listeners that don't\nspecify a value. Can be set by environment variable\nGRADIO_DEFAULT_CONCURRENCY_LIMIT. Defaults to 1 if not set otherwise.\n\n", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface", "source_page_title": "Gradio - Interface Docs"}, {"text": "Creates a Dialogue component for displaying or collecting multi-speaker\nconversations. This component can be used as input to allow users to enter\ndialogue involving multiple speakers, or as output to display diarized speech,\nsuch as the result of a transcription or speaker identification model. Each\nmessage can be associated with a specific speaker, making it suitable for use\ncases like conversations, interviews, or meetings.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "**As input component** : Returns the dialogue as a string.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: tuple[str, list[tuple[str, str]]] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a string or a list of dictionaries of\ndialogue lines, where each dictionary contains 'speaker' and 'text' keys, or a\nstring.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> tuple[np.ndarray | PIL.Image.Image | str, list[tuple[np.ndarray | tuple[int, int, int, int], str]]] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: list[dict[str, str]] | Callable | None\n\ndefault `= None`\n\nValue of the dialogue. It is a list of dictionaries, each containing a\n'speaker' key and a 'text' key. If a function is provided, the function will\nbe called each time the app loads to set the initial value of this component.\n\n\n    \n    \n    speakers: list[str] | None\n\ndefault `= None`\n\nThe different speakers allowed in the dialogue. If `None` or an empty list, no\nspeakers will be displayed. Instead, the component will be a standard textarea\nthat optionally supports `tags` autocompletion.\n\n\n    \n    \n    formatter: Callable | None\n\ndefault `= None`\n\nA function that formats the dialogue line dictionary, e.g. {\"speaker\":\n\"Speaker 1\", \"text\": \"Hello, how are you?\"} into a string, e.g. \"Speaker 1:\nHello, how are you?\". This function is run on user input and the resulting\nstring is passed into the prediction function.\n\n\n    \n    \n    unformatter: Callable | None\n\ndefault `= None`\n\nA function that parses a formatted dialogue string back into a dialogue line\ndictionary. Should take a single string line and return a dictionary with\n'speaker' and 'text' keys. If not provided, the default unformatter will\nattempt to parse the default formatter pattern.\n\n\n    \n    \n    tags: list[str] | None\n\ndefault `= None`\n\nThe different tags allowed in the dialogue. Tags are displayed in an\nautocomplete menu below the input textbox when the user starts typing `:`. Use\nthe exact tag name expected by the AI model or inference function.\n\n\n    \n    \n    separator: str\n\ndefault `= \" \"`\n\nThe separator between the different dialogue lines used to join the formatted\ndialogue lines into a single string. For example, a newline character or empty\nstring.\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nA dictionary mapping speaker names to colors. The colors may be specified as\nhex codes or by their names. For example: {\"Speaker 1\": \"red\", \"Speaker 2\":\n\"FFEE22\"}. If not provided, default ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "`= None`\n\nA dictionary mapping speaker names to colors. The colors may be specified as\nhex codes or by their names. For example: {\"Speaker 1\": \"red\", \"Speaker 2\":\n\"FFEE22\"}. If not provided, default colors will be assigned to speakers. This\nis only used if `interactive` is False.\n\n\n    \n    \n    label: str | None\n\ndefault `= \"Dialogue\"`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | None\n\ndefault `= \"Type colon (:) in the dialogue line to see the available tags\"`\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\nplaceholder hint to provide behind textarea.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display the label. If False, the copy button is hidden as well\nas well as the label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible:", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "e, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= False`\n\nIf True, will focus on the textbox when the page loads. Use this carefully, as\nit can cause usability issues for sighted and non-sighted users.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | None\n\ndefault `= None`\n\nif assigned, will be used to assume identity across a re-render. Components\nthat have the same key across a re-render will have their value preserved.\n\n\n    \n    \n    max_lines: int | None\n\ndefault `= None`\n\nmaximum number of lines allowed in the dialogue.\n\n\n    \n    \n    show_submit_button: bool\n\ndefault `= True`\n\nIf True, includes a submit button to submit the dialogue.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= True`\n\nIf True, includes a copy button to copy the text in the textbox. Only applies\nif show_label is True.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "ncludes a copy button to copy the text in the textbox. Only applies\nif show_label is True.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Dialogue` | \"dialogue\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "dia_dialogue_demo\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import httpx tags = [ \"(laughs)\", \"(clears throat)\", \"(sighs)\", \"(gasps)\", \"(coughs)\", \"(singing)\", \"(sings)\", \"(mumbles)\", \"(beep)\", \"(groans)\", \"(sniffs)\", \"(claps)\", \"(screams)\", \"(inhales)\", \"(exhales)\", \"(applause)\", \"(burps)\", \"(humming)\", \"(sneezes)\", \"(chuckle)\", \"(whistles)\", ] speakers = [\"Speaker 1\", \"Speaker 2\"] client = httpx.AsyncClient(timeout=180) API_URL = \"https://router.huggingface.co/fal-ai/fal-ai/dia-tts\" async def query(dialogue: str, token: gr.OAuthToken | None): if token is None: raise gr.Error( \"No token provided. Use Sign in with Hugging Face to get a token.\" ) headers = { \"Authorization\": f\"Bearer {token.token}\", } response = await client.post(API_URL, headers=headers, json={\"text\": dialogue}) url = response.json()[\"audio\"][\"url\"] print(\"URL: \", url) return url def formatter(speaker, text): speaker = speaker.split(\" \")[1] return f\"[S{speaker}] {text}\" with gr.Blocks() as demo: with gr.Sidebar(): login_button = gr.LoginButton() gr.HTML( \"\"\" <h1 style='text-align: center; display: flex; align-items: center; justify-content: center;'> <img src=\"https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/dancing_huggy.gif\" alt=\"Dancing Huggy\" style=\"height: 100px; margin-right: 10px\"> Dia Dialogue Generation Model </h1> <h2 style='text-align: center; display: flex; align-items: center; justify-content: center;'>Model by &nbsp;<a href=\"https://huggingface.co/nari-labs/Dia-1.6B\"> Nari Labs</a>. Powered by HF and &nbsp; <a href=\"https://fal.ai/\">Fal AI</a>&nbsp; API.</h2> <h4>Dia is a dialogue generation model that can generate realistic dialogue between two speakers. Use the dialogue component to create a conversation and then hit the submit button in the bottom right corner to see it come to life .</h4> \"\"\" ) with gr.Row(): with gr.Column(): dialogue = gr.Dialogue( speakers=speakers, tags=tags, formatter=formatter ) with gr.Column(): with gr.Row(): audio = gr.Audio(label=\"Audio\") with gr", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": " life .</h4> \"\"\" ) with gr.Row(): with gr.Column(): dialogue = gr.Dialogue( speakers=speakers, tags=tags, formatter=formatter ) with gr.Column(): with gr.Row(): audio = gr.Audio(label=\"Audio\") with gr.Row(): gr.DeepLinkButton(value=\"Share Audio via Link\") with gr.Row(): gr.Examples( examples=[ [ [ { \"speaker\": \"Speaker 1\", \"text\": \"Why did the chicken cross the road?\", }, {\"speaker\": \"Speaker 2\", \"text\": \"I don't know!\"}, { \"speaker\": \"Speaker 1\", \"text\": \"to get to the other side! (laughs)\", }, ] ], [ [ { \"speaker\": \"Speaker 1\", \"text\": \"I am a little tired today (sighs).\", }, {\"speaker\": \"Speaker 2\", \"text\": \"Hang in there!\"}, ] ], ], inputs=[dialogue], cache_examples=False, ) dialogue.submit(query, [dialogue], audio) if __name__ == \"__main__\": demo.launch() \n    \n    \n    import gradio as gr\n    import httpx\n    \n    \n    tags = [\n        \"(laughs)\",\n        \"(clears throat)\",\n        \"(sighs)\",\n        \"(gasps)\",\n        \"(coughs)\",\n        \"(singing)\",\n        \"(sings)\",\n        \"(mumbles)\",\n        \"(beep)\",\n        \"(groans)\",\n        \"(sniffs)\",\n        \"(claps)\",\n        \"(screams)\",\n        \"(inhales)\",\n        \"(exhales)\",\n        \"(applause)\",\n        \"(burps)\",\n        \"(humming)\",\n        \"(sneezes)\",\n        \"(chuckle)\",\n        \"(whistles)\",\n    ]\n    speakers = [\"Speaker 1\", \"Speaker 2\"]\n    \n    client = httpx.AsyncClient(timeout=180)\n    API_URL = \"https://router.huggingface.co/fal-ai/fal-ai/dia-tts\"\n    \n    \n    async def query(dialogue: str, token: gr.OAuthToken | None):\n        if token is None:\n            raise gr.Error(\n                \"No token provided. Use Sign in with Hugging Face to get a token.\"\n            )\n        headers = {\n            \"Authorization\": f\"Bearer {token.token}\",\n        }\n        response = await client.post(API_URL, headers=headers, json={\"text\": dialogue})\n        url = response.json()[\"audio\"][\"url\"]\n        print(\"URL: \", url)\n        return url\n    \n    \n    def formatter(speaker, text):\n        speaker = spea", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "eaders=headers, json={\"text\": dialogue})\n        url = response.json()[\"audio\"][\"url\"]\n        print(\"URL: \", url)\n        return url\n    \n    \n    def formatter(speaker, text):\n        speaker = speaker.split(\" \")[1]\n        return f\"[S{speaker}] {text}\"\n    \n    \n    with gr.Blocks() as demo:\n        with gr.Sidebar():\n            login_button = gr.LoginButton()\n        gr.HTML(\n            \"\"\"\n            \n    \n    \n            ![Dancing Huggy](https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/dancing_huggy.gif) Dia Dialogue Generation Model\n            \n    \n    \n            \n    \n    Model by \u00a0[ Nari Labs](https://huggingface.co/nari-labs/Dia-1.6B). Powered by HF and \u00a0 [Fal AI](https://fal.ai/)\u00a0  API.\n    \n    \n            \n    \n    Dia is a dialogue generation model that can generate realistic dialogue between two speakers. Use the dialogue component to create a conversation and then hit the submit button in the bottom right corner to see it come to life .\n    \n    \n            \"\"\"\n        )\n        with gr.Row():\n            with gr.Column():\n                dialogue = gr.Dialogue(\n                    speakers=speakers, tags=tags, formatter=formatter\n                )\n            with gr.Column():\n                with gr.Row():\n                    audio = gr.Audio(label=\"Audio\")\n                with gr.Row():\n                    gr.DeepLinkButton(value=\"Share Audio via Link\")\n        with gr.Row():\n            gr.Examples(\n                examples=[\n                    [\n                        [\n                            {\n                                \"speaker\": \"Speaker 1\",\n                                \"text\": \"Why did the chicken cross the road?\",\n                            },\n                            {\"speaker\": \"Speaker 2\", \"text\": \"I don't know!\"},\n                            {\n                                \"speaker\": \"Speaker 1\",\n                                \"text\": \"to get to the other side! (laughs)\",\n     ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": " 2\", \"text\": \"I don't know!\"},\n                            {\n                                \"speaker\": \"Speaker 1\",\n                                \"text\": \"to get to the other side! (laughs)\",\n                            },\n                        ]\n                    ],\n                    [\n                        [\n                            {\n                                \"speaker\": \"Speaker 1\",\n                                \"text\": \"I am a little tired today (sighs).\",\n                            },\n                            {\"speaker\": \"Speaker 2\", \"text\": \"Hang in there!\"},\n                        ]\n                    ],\n                ],\n                inputs=[dialogue],\n                cache_examples=False,\n            )\n    \n        dialogue.submit(query, [dialogue], audio)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Dialogue component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Dialogue.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Dialogue changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Dialogue.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Dialogue.  \n`Dialogue.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the Dialogue is focused.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    b", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "n the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending even", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "ending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "value identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dialogue", "source_page_title": "Gradio - Dialogue Docs"}, {"text": "The gr.DeletedFileData class is a subclass of gr.EventData that\nspecifically carries information about the `.delete()` event. When\ngr.DeletedFileData is added as a type hint to an argument of an event listener\nmethod, a gr.DeletedFileData object will automatically be passed as the value\nof that argument. The attributes of this object contains information about the\nevent that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "import gradio as gr\n    \n    def test(delete_data: gr.DeletedFileData):\n        return delete_data.file.path\n    \n    with gr.Blocks() as demo:\n        files = gr.File(file_count=\"multiple\")\n        deleted_file = gr.File()\n        files.delete(test, None, deleted_file)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    file: FileData\n\nThe file that was deleted, as a FileData object. The str path to the file can\nbe retrieved with the .path attribute.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "file_component_events\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def delete_file(n: int, file:\ngr.DeletedFileData): return [file.file.path, n + 1] with gr.Blocks() as demo:\nwith gr.Row(): with gr.Column(): file_component = gr.File(label=\"Upload Single\nFile\", file_count=\"single\") with gr.Column(): output_file_1 = gr.File(\nlabel=\"Upload Single File Output\", file_count=\"single\" ) num_load_btn_1 =\ngr.Number(label=\"Load Upload Single File\", value=0) file_component.upload(\nlambda s, n: (s, n + 1), [file_component, num_load_btn_1], [output_file_1,\nnum_load_btn_1], ) with gr.Row(): with gr.Column(): file_component_multiple =\ngr.File( label=\"Upload Multiple Files\", file_count=\"multiple\" ) with\ngr.Column(): output_file_2 = gr.File( label=\"Upload Multiple Files Output\",\nfile_count=\"multiple\" ) num_load_btn_2 = gr.Number(label=\"Load Upload\nMultiple Files\", value=0) file_component_multiple.upload( lambda s, n: (s, n +\n1), [file_component_multiple, num_load_btn_2], [output_file_2,\nnum_load_btn_2], ) with gr.Row(): with gr.Column(): file_component_specific =\ngr.File( label=\"Upload Multiple Files Image/Video\", file_count=\"multiple\",\nfile_types=[\"image\", \"video\"], ) with gr.Column(): output_file_3 = gr.File(\nlabel=\"Upload Multiple Files Output Image/Video\", file_count=\"multiple\" )\nnum_load_btn_3 = gr.Number( label=\"Load Upload Multiple Files Image/Video\",\nvalue=0 ) file_component_specific.upload( lambda s, n: (s, n + 1),\n[file_component_specific, num_load_btn_3], [output_file_3, num_load_btn_3], )\nwith gr.Row(): with gr.Column(): file_component_pdf = gr.File(label=\"Upload\nPDF File\", file_types=[\".pdf\"]) with gr.Column(): output_file_4 =\ngr.File(label=\"Upload PDF File Output\") num_load_btn_4 = gr.Number(label=\"\nLoad Upload PDF File\", value=0) file_component_pdf.upload( lambda s, n: (s, n\n+ 1), [file_component_pdf, num_load_btn_4], [output_file_4, num_load_btn_4], )\nwith gr.Row(): with gr.Column(): file_component_invalid = gr.File(\nlabel=\"Upload File with Invalid file_types\", file_types=", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "1), [file_component_pdf, num_load_btn_4], [output_file_4, num_load_btn_4], )\nwith gr.Row(): with gr.Column(): file_component_invalid = gr.File(\nlabel=\"Upload File with Invalid file_types\", file_types=[\"invalid file_type\"],\n) with gr.Column(): output_file_5 = gr.File(label=\"Upload File with Invalid\nfile_types Output\") num_load_btn_5 = gr.Number( label=\"Load Upload File with\nInvalid file_types\", value=0 ) file_component_invalid.upload( lambda s, n: (s,\nn + 1), [file_component_invalid, num_load_btn_5], [output_file_5,\nnum_load_btn_5], ) with gr.Row(): with gr.Column(): del_file_input =\ngr.File(label=\"Delete File\", file_count=\"multiple\") with gr.Column():\ndel_file_data = gr.Textbox(label=\"Delete file data\") num_load_btn_6 =\ngr.Number(label=\"Deleted File\", value=0) del_file_input.delete( delete_file,\n[num_load_btn_6], [del_file_data, num_load_btn_6], ) f =\ngr.File(label=\"Upload many File\", file_count=\"multiple\") \nf.delete(delete_file) f.delete(delete_file, inputs=None, outputs=None) if\n__name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def delete_file(n: int, file: gr.DeletedFileData):\n        return [file.file.path, n + 1]\n    \n    with gr.Blocks() as demo:\n    \n        with gr.Row():\n            with gr.Column():\n                file_component = gr.File(label=\"Upload Single File\", file_count=\"single\")\n            with gr.Column():\n                output_file_1 = gr.File(\n                    label=\"Upload Single File Output\", file_count=\"single\"\n                )\n                num_load_btn_1 = gr.Number(label=\"Load Upload Single File\", value=0)\n                file_component.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component, num_load_btn_1],\n                    [output_file_1, num_load_btn_1],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_multiple = gr.File(\n                    label=\"Upload Multiple Files\", file_count=\"multiple\"\n ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "1],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_multiple = gr.File(\n                    label=\"Upload Multiple Files\", file_count=\"multiple\"\n                )\n            with gr.Column():\n                output_file_2 = gr.File(\n                    label=\"Upload Multiple Files Output\", file_count=\"multiple\"\n                )\n                num_load_btn_2 = gr.Number(label=\"Load Upload Multiple Files\", value=0)\n                file_component_multiple.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_multiple, num_load_btn_2],\n                    [output_file_2, num_load_btn_2],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_specific = gr.File(\n                    label=\"Upload Multiple Files Image/Video\",\n                    file_count=\"multiple\",\n                    file_types=[\"image\", \"video\"],\n                )\n            with gr.Column():\n                output_file_3 = gr.File(\n                    label=\"Upload Multiple Files Output Image/Video\", file_count=\"multiple\"\n                )\n                num_load_btn_3 = gr.Number(\n                    label=\"Load Upload Multiple Files Image/Video\", value=0\n                )\n                file_component_specific.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_specific, num_load_btn_3],\n                    [output_file_3, num_load_btn_3],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_pdf = gr.File(label=\"Upload PDF File\", file_types=[\".pdf\"])\n            with gr.Column():\n                output_file_4 = gr.File(label=\"Upload PDF File Output\")\n                num_load_btn_4 = gr.Number(label=\"Load Upload PDF File\", value=0)\n                file_component_pdf.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_pdf, num_load_bt", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "_btn_4 = gr.Number(label=\"Load Upload PDF File\", value=0)\n                file_component_pdf.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_pdf, num_load_btn_4],\n                    [output_file_4, num_load_btn_4],\n                )\n        with gr.Row():\n            with gr.Column():\n                file_component_invalid = gr.File(\n                    label=\"Upload File with Invalid file_types\",\n                    file_types=[\"invalid file_type\"],\n                )\n            with gr.Column():\n                output_file_5 = gr.File(label=\"Upload File with Invalid file_types Output\")\n                num_load_btn_5 = gr.Number(\n                    label=\"Load Upload File with Invalid file_types\", value=0\n                )\n                file_component_invalid.upload(\n                    lambda s, n: (s, n + 1),\n                    [file_component_invalid, num_load_btn_5],\n                    [output_file_5, num_load_btn_5],\n                )\n        with gr.Row():\n            with gr.Column():\n                del_file_input = gr.File(label=\"Delete File\", file_count=\"multiple\")\n            with gr.Column():\n                del_file_data = gr.Textbox(label=\"Delete file data\")\n                num_load_btn_6 = gr.Number(label=\"Deleted File\", value=0)\n                del_file_input.delete(\n                    delete_file,\n                    [num_load_btn_6],\n                    [del_file_data, num_load_btn_6],\n                )\n        f = gr.File(label=\"Upload many File\", file_count=\"multiple\")\n        f.delete(delete_file)\n        f.delete(delete_file, inputs=None, outputs=None)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/deletedfiledata", "source_page_title": "Gradio - Deletedfiledata Docs"}, {"text": "Creates a textarea for users to enter string input or display string output\nand also allows for the uploading of multimedia files.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "**As input component** : Passes text value and list of file(s) as a `dict`\ninto the function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: MultimodalValue | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `dict` with \"text\" and \"files\", both\noptional. The files array is a list of file paths or URLs.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> MultimodalValue | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | dict[str, str | list] | Callable | None\n\ndefault `= None`\n\nDefault value to show in MultimodalTextbox. A string value, or a dictionary of\nthe form {\"text\": \"sample text\", \"files\": [{path: \"files/file.jpg\", orig_name:\n\"file.jpg\", url: \"http://image_url.jpg\", size: 100}]}. If a function is\nprovided, the function will be called each time the app loads to set the\ninitial value of this component.\n\n\n    \n    \n    sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None\n\ndefault `= None`\n\nA list of sources permitted. \"upload\" creates a button where users can click\nto upload or drop files, \"microphone\" creates a microphone input. If None,\ndefaults to [\"upload\"].\n\n\n    \n    \n    file_types: list[str] | None\n\ndefault `= None`\n\nList of file extensions or types of files to be uploaded (e.g. ['image',\n'.json', '.mp4']). \"file\" allows any file to be uploaded, \"image\" allows only\nimage files to be uploaded, \"audio\" allows only audio files to be uploaded,\n\"video\" allows only video files to be uploaded, \"text\" allows only text files\nto be uploaded.\n\n\n    \n    \n    file_count: Literal['single', 'multiple', 'directory']\n\ndefault `= \"single\"`\n\nif single, allows user to upload one file. If \"multiple\", user uploads\nmultiple files. If \"directory\", user uploads all files in selected directory.\nReturn type will be list for each file in case of \"multiple\" or \"directory\".\n\n\n    \n    \n    lines: int\n\ndefault `= 1`\n\nminimum number of line rows to provide in textarea.\n\n\n    \n    \n    max_lines: int\n\ndefault `= 20`\n\nmaximum number of line rows to provide in textarea.\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\nplaceholder hint to provide behind textarea.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "e`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "cted first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= False`\n\nIf True, will focus on the textbox when the page loads. Use this carefully, as\nit can cause usability issues for sighted and non-sighted users.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "er()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    text_align: Literal['left', 'right'] | None\n\ndefault `= None`\n\nHow to align the text in the textbox, can be: \"left\", \"right\", or None\n(default). If None, the alignment is left if `rtl` is False, or right if `rtl`\nis True. Can only be changed if `type` is \"text\".\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True and `type` is \"text\", sets the direction of the text to right-to-left\n(cursor appears on the left of the text). Default is False, which renders\ncursor on the right.\n\n\n    \n    \n    submit_btn: str | bool | None\n\ndefault `= True`\n\nIf False, will not show a submit button. If a string, will use that string as\nthe submit button text.\n\n\n    \n    \n    stop_btn: str | bool | None\n\ndefault `= False`\n\nIf True, will show a stop button (useful for streaming demos). If a string,\nwill use that string as the stop button text.\n\n\n    \n    \n    max_plain_text_length: int\n\ndefault `= 1000`\n\nMaximum length of plain text in the textbox. If the text exceeds this length,\nthe text will be pasted as a file. Default is 1000.\n\n\n    \n    \n    html_attributes: InputHTMLAttributes | None\n\ndefault `= None`\n\nAn instance of gr.InputHTMLAttributes, which can be used to set HTML\nattributes for the input/textarea elements. Example:\nInputHTMLAttributes(autocorrect=\"off\", spellcheck=False) to disable\nautocorrect and spellcheck.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.MultimodalTextbox` | \"multimodaltextbox\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "chatbot_multimodal\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import time Chatbot demo with multimodal\ninput (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows\nsupport for streaming text. def print_like_dislike(x: gr.LikeData):\nprint(x.index, x.value, x.liked) def add_message(history, message): for x in\nmessage[\"files\"]: history.append({\"role\": \"user\", \"content\": {\"path\": x}}) if\nmessage[\"text\"] is not None: history.append({\"role\": \"user\", \"content\":\nmessage[\"text\"]}) return history, gr.MultimodalTextbox(value=None,\ninteractive=False) def bot(history: list): response = \"**That's cool!**\"\nhistory.append({\"role\": \"assistant\", \"content\": \"\"}) for character in\nresponse: history[-1][\"content\"] += character time.sleep(0.05) yield history\nwith gr.Blocks() as demo: chatbot = gr.Chatbot(elem_id=\"chatbot\",\nbubble_full_width=False, type=\"messages\") chat_input = gr.MultimodalTextbox(\ninteractive=True, file_count=\"multiple\", placeholder=\"Enter message or upload\nfile...\", show_label=False, sources=[\"microphone\", \"upload\"], ) chat_msg =\nchat_input.submit( add_message, [chatbot, chat_input], [chatbot, chat_input] )\nbot_msg = chat_msg.then(bot, chatbot, chatbot, api_name=\"bot_response\")\nbot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None,\n[chat_input]) chatbot.like(print_like_dislike, None, None,\nlike_user_message=True) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import time\n    \n    Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, & video). Plus shows support for streaming text.\n    \n    \n    def print_like_dislike(x: gr.LikeData):\n        print(x.index, x.value, x.liked)\n    \n    \n    def add_message(history, message):\n        for x in message[\"files\"]:\n            history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n        if message[\"text\"] is not None:\n            history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n        return history, gr.MultimodalTextbox(valu", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "le\": \"user\", \"content\": {\"path\": x}})\n        if message[\"text\"] is not None:\n            history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n        return history, gr.MultimodalTextbox(value=None, interactive=False)\n    \n    \n    def bot(history: list):\n        response = \"**That's cool!**\"\n        history.append({\"role\": \"assistant\", \"content\": \"\"})\n        for character in response:\n            history[-1][\"content\"] += character\n            time.sleep(0.05)\n            yield history\n    \n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(elem_id=\"chatbot\", bubble_full_width=False, type=\"messages\")\n    \n        chat_input = gr.MultimodalTextbox(\n            interactive=True,\n            file_count=\"multiple\",\n            placeholder=\"Enter message or upload file...\",\n            show_label=False,\n            sources=[\"microphone\", \"upload\"],\n        )\n    \n        chat_msg = chat_input.submit(\n            add_message, [chatbot, chat_input], [chatbot, chat_input]\n        )\n        bot_msg = chat_msg.then(bot, chatbot, chatbot, api_name=\"bot_response\")\n        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n    \n        chatbot.like(print_like_dislike, None, None, like_user_message=True)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe MultimodalTextbox component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`MultimodalTextbox.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the MultimodalTextbox changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`MultimodalTextbox.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the MultimodalTextbox.  \n`MultimodalTextbox.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the MultimodalTextbox. Uses event data gradio.SelectData to carry `value` referring to the label of the MultimodalTextbox, and `selected` to refer to state of the MultimodalTextbox. See EventData documentation on how to use this event data  \n`MultimodalTextbox.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the MultimodalTextbox is focused.  \n`MultimodalTextbox.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the MultimodalTextbox is focused.  \n`MultimodalTextbox.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the MultimodalTextbox is unfocused/blurred.  \n`MultimodalTextbox.stop(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the MultimodalTextbox.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's pred", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "t Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "roll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of oth", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_ap", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "rency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/multimodaltextbox", "source_page_title": "Gradio - Multimodaltextbox Docs"}, {"text": "Column is a layout element within Blocks that renders all children\nvertically. The widths of columns can be set through the `scale` and\n`min_width` parameters. If a certain scale results in a column narrower than\nmin_width, the min_width parameter will win.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/column", "source_page_title": "Gradio - Column Docs"}, {"text": "with gr.Blocks() as demo:\n        with gr.Row():\n            with gr.Column(scale=1):\n                text1 = gr.Textbox()\n                text2 = gr.Textbox()\n            with gr.Column(scale=4):\n                btn1 = gr.Button(\"Button 1\")\n                btn2 = gr.Button(\"Button 2\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/column", "source_page_title": "Gradio - Column Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    scale: int\n\ndefault `= 1`\n\nrelative width compared to adjacent Columns. For example, if Column A has\nscale=2, and Column B has scale=1, A will be twice as wide as B.\n\n\n    \n    \n    min_width: int\n\ndefault `= 320`\n\nminimum pixel width of Column, will wrap if not sufficient screen space to\nsatisfy this value. If a certain scale value results in a column narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    variant: Literal['default', 'panel', 'compact']\n\ndefault `= \"default\"`\n\ncolumn type, 'default' (no background), 'panel' (gray background color and\nrounded corners), or 'compact' (rounded corners and no internal gap).\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, column will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    show_progress: bool\n\ndefault `= False`\n\nIf True, shows progress animation when being updated.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) paramet", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/column", "source_page_title": "Gradio - Column Docs"}, {"text": "] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/column", "source_page_title": "Gradio - Column Docs"}, {"text": "Used to render arbitrary Markdown output. Can also render latex enclosed by\ndollar signs. As this component does not accept user input, it is rarely used\nas an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "**As input component** : Passes the `str` of Markdown corresponding to the\ndisplayed value.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a valid `str` that can be rendered as\nMarkdown.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | I18nData | Callable | None\n\ndefault `= None`\n\nValue to show in Markdown component. If a function is provided, the function\nwill be called each time the app loads to set the initial value of this\ncomponent.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThis parameter has no effect\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nThis parameter has no effect.\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True, sets the direction of the rendered text to right-to-left. Default is\nFalse, which renders text left-to-right.\n\n\n    \n    \n    latex_delimiters: list[dict[str, str | bool]] | None\n\ndefault `= None`\n\nA list of dicts of the form {\"left\": open delimiter (str), \"right\": close\ndelimiter (str), \"display\": whether to display in newline (bool)} that will be\nused to render LaTeX expressions. If not provided, `latex_delimiters` is set\nto `[{ \"left\": \"$$\", \"right\": \"$$\", \"display\": True }]`, so only expressions\nenclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass\nin an empty list to disable LaTeX rendering. For more information, see the\n[KaTeX documentation](https://katex.org/docs/autorender.html).\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "t will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    sanitize_html: bool\n\ndefault `= True`\n\nIf False, will disable HTML sanitization when converted from markdown. This is\nnot recommended, as it can lead to security vulnerabilities.\n\n\n    \n    \n    line_breaks: bool\n\ndefault `= False`\n\nIf True, will enable Github-flavored Markdown line breaks in chatbot messages.\nIf False (default), single new lines will be ignored.\n\n\n    \n    \n    header_links: bool\n\ndefault `= False`\n\nIf True, will automatically create anchors for headings, displaying a link\nicon on hover.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. If mark", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "a link\nicon on hover.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. If markdown content exceeds the height, the\ncomponent will scroll.\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= None`\n\nThe maximum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If markdown content exceeds the\nheight, the component will scroll. If markdown content is shorter than the\nheight, the component will shrink to fit the content. Will not have any effect\nif `height` is set and is smaller than `max_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If markdown content exceeds the\nheight, the component will expand to fit the content. Will not have any effect\nif `height` is set and is larger than `min_height`.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, includes a copy button to copy the text in the Markdown component.\nDefault is False.\n\n\n    \n    \n    container: bool\n\ndefault `= False`\n\nIf True, the Markdown component will be displayed in a container. Default is\nFalse.\n\n\n    \n    \n    padding: bool\n\ndefault `= False`\n\nIf True, the Markdown component will have a certain padding (set by the\n`--block-padding` CSS variable) in all directions. Default is False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Markdown` | \"markdown\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "blocks_helloblocks_kinematics\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def welcome(name): return f\"Welcome to Gradio,\n{name}!\" with gr.Blocks() as demo: gr.Markdown( \"\"\" Hello World! Start\ntyping below to see the output. \"\"\") inp = gr.Textbox(placeholder=\"What is\nyour name?\") out = gr.Textbox() inp.change(welcome, inp, out) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def welcome(name):\n        return f\"Welcome to Gradio, {name}!\"\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\n        \"\"\"\n        Hello World!\n        Start typing below to see the output.\n        \"\"\")\n        inp = gr.Textbox(placeholder=\"What is your name?\")\n        out = gr.Textbox()\n        inp.change(welcome, inp, out)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd import numpy as np import gradio as gr def\nplot(v, a): g = 9.81 theta = a / 180 * 3.14 tmax = ((2 * v) * np.sin(theta)) /\ng timemat = tmax * np.linspace(0, 1, 40) x = (v * timemat) * np.cos(theta) y =\n((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2)) df =\npd.DataFrame({\"x\": x, \"y\": y}) return df demo = gr.Blocks() with demo:\ngr.Markdown( r\"Let's do some kinematics! Choose the speed and angle to see the\ntrajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n) with gr.Row(): speed = gr.Slider(1, 30, 25, label=\"Speed\") angle =\ngr.Slider(0, 90, 45, label=\"Angle\") output = gr.LinePlot( x=\"x\", y=\"y\",\noverlay_point=True, tooltip=[\"x\", \"y\"], x_lim=[0, 100], y_lim=[0, 60],\nwidth=350, height=300, ) btn = gr.Button(value=\"Run\") btn.click(plot, [speed,\nangle], output) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import pandas as pd\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot(v, a):\n        g = 9.81\n        theta = a / 180 * 3.14\n        tmax = ((2 * v) * np.sin(theta)) / g\n        timemat = tmax * np.linspace(0, 1, 40)\n    \n        x = (v * timemat) * np.cos(theta)\n        y = ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "\n        g = 9.81\n        theta = a / 180 * 3.14\n        tmax = ((2 * v) * np.sin(theta)) / g\n        timemat = tmax * np.linspace(0, 1, 40)\n    \n        x = (v * timemat) * np.cos(theta)\n        y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))\n        df = pd.DataFrame({\"x\": x, \"y\": y})\n        return df\n    \n    demo = gr.Blocks()\n    \n    with demo:\n        gr.Markdown(\n            r\"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n        )\n    \n        with gr.Row():\n            speed = gr.Slider(1, 30, 25, label=\"Speed\")\n            angle = gr.Slider(0, 90, 45, label=\"Angle\")\n        output = gr.LinePlot(\n            x=\"x\",\n            y=\"y\",\n            overlay_point=True,\n            tooltip=[\"x\", \"y\"],\n            x_lim=[0, 100],\n            y_lim=[0, 60],\n            width=350,\n            height=300,\n        )\n        btn = gr.Button(value=\"Run\")\n        btn.click(plot, [speed, angle], output)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Markdown component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Markdown.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Markdown changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Markdown.copy(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user copies content from the Markdown. Uses event data gradio.CopyData to carry information about the copied content. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: s", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "xt] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": " request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npe", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/markdown", "source_page_title": "Gradio - Markdown Docs"}, {"text": "The gr.EditData class is a subclass of gr.Event data that specifically\ncarries information about the `.edit()` event. When gr.EditData is added as a\ntype hint to an argument of an event listener method, a gr.EditData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/editdata", "source_page_title": "Gradio - Editdata Docs"}, {"text": "import gradio as gr\n    \n    def edit(edit_data: gr.EditData, history: list[gr.MessageDict]):\n        history_up_to_edit = history[:edit_data.index]\n        history_up_to_edit[-1] = edit_data.value\n        return history_up_to_edit\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot()\n        chatbot.undo(edit, chatbot, chatbot)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/editdata", "source_page_title": "Gradio - Editdata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the message that was edited.\n\n\n    \n    \n    previous_value: Any\n\nThe previous content of the message that was edited.\n\n\n    \n    \n    value: Any\n\nThe new content of the message that was edited.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/editdata", "source_page_title": "Gradio - Editdata Docs"}, {"text": "A base class for defining methods that all input/output components should\nhave.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "**As input component** : Passes a value of arbitrary type through.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: Any\n    )\n    \t...\n\n  \n\n**As output component** : Expects a value of arbitrary type, as long as it\ncan be deepcopied.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> Any\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: Any\n\ndefault `= None`\n\nthe initial value (of arbitrary type) of the state. The provided argument is\ndeepcopied. If a callable is provided, the function will be called whenever\nthe app loads to set the initial value of the state.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nshould always be True, is included for consistency with other components.\n\n\n    \n    \n    time_to_live: int | float | None\n\ndefault `= None`\n\nthe number of seconds the state should be stored for after it is created or\nupdated. If None, the state will be stored indefinitely. Gradio automatically\ndeletes state variables after a user closes the browser tab or refreshes the\npage, so this is useful for clearing state for potentially long running\nsessions.\n\n\n    \n    \n    delete_callback: Callable[[Any], None] | None\n\ndefault `= None`\n\na function that is called when the state is deleted. The function should take\nthe state value as an argument.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe State component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`State.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the State changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "nes how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`).", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nf", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": " Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/state", "source_page_title": "Gradio - State Docs"}, {"text": "Button that triggers a Spaces Duplication, when the demo is on Hugging Face\nSpaces. Does nothing locally.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "**As input component** : (Rarely used) the `str` corresponding to the\nbutton label when the button is clicked\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : string corresponding to the button label\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str\n\ndefault `= \"Duplicate Space\"`\n\ndefault text for the button to display. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop', 'huggingface']\n\ndefault `= \"huggingface\"`\n\nsets the background and text color of the button. Use 'primary' for main call-\nto-action buttons, 'secondary' for a more subdued style, 'stop' for a stop\nbutton, 'huggingface' for a black background with white text, consistent with\nHugging Face's button styles.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"sm\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | Path | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    link: str | None\n\ndefault `= None`\n\nURL to open when the button is clicked. If None, no link will be used.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nif False, the Button will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are ass", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "gned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    scale: int | None\n\ndefault `= 0`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.DuplicateButton` | \"duplicatebutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe DuplicateButton component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`DuplicateButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Button is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_d", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": " as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to m", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/duplicatebutton", "source_page_title": "Gradio - Duplicatebutton Docs"}, {"text": "The render decorator allows Gradio Blocks apps to have dynamic layouts, so\nthat the components and event listeners in your app can change depending on\ncustom logic. Attaching a @gr.render decorator to a function will cause the\nfunction to be re-run whenever the inputs are changed (or specified triggers\nare activated). The function contains the components and event listeners that\nwill update based on the inputs.  \nThe basic usage of @gr.render is as follows:  \n1\\. Create a function and attach the @gr.render decorator to it.  \n2\\. Add the input components to the `inputs=` argument of @gr.render, and\ncreate a corresponding argument in your function for each component.  \n3\\. Add all components inside the function that you want to update based on\nthe inputs. Any event listeners that use these components should also be\ninside this function.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/render", "source_page_title": "Gradio - Render Docs"}, {"text": "import gradio as gr\n    \n    with gr.Blocks() as demo:\n        input_text = gr.Textbox()\n    \n        @gr.render(inputs=input_text)\n        def show_split(text):\n            if len(text) == 0:\n                gr.Markdown(\"No Input Provided\")\n            else:\n                for letter in text:\n                    with gr.Row():\n                        text = gr.Textbox(letter)\n                        btn = gr.Button(\"Clear\")\n                        btn.click(lambda: gr.Textbox(value=\"\"), None, text)\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/render", "source_page_title": "Gradio - Render Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    inputs: list[Component] | Component | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    triggers: list[EventListenerCallable] | EventListenerCallable | None\n\ndefault `= None`\n\nList of triggers to listen to, e.g. [btn.click, number.change]. If None, will\nlisten to changes to any inputs.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= \"always_last\"`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= None`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/render", "source_page_title": "Gradio - Render Docs"}, {"text": "This function allows you to pass custom info messages to the user. You can\ndo so simply by writing `gr.Info('message here')` in your function, and when\nthat line is executed the custom message will appear in a modal on the demo.\nThe modal is gray by default and has the heading: \"Info.\" Queue must be\nenabled for this behavior; otherwise, the message will be printed to the\nconsole.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "import gradio as gr\n    def hello_world():\n        gr.Info('This is some info.')\n        return \"hello world\"\n    with gr.Blocks() as demo:\n        md = gr.Markdown()\n        demo.load(hello_world, inputs=None, outputs=[md])\n    demo.queue().launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    message: str\n\ndefault `= \"Info issued.\"`\n\nThe info message to be displayed to the user. Can be HTML, which will be\nrendered in the modal.\n\n\n    \n    \n    duration: float | None\n\ndefault `= 10`\n\nThe duration in seconds that the info message should be displayed for. If None\nor 0, the message will be displayed indefinitely until the user closes it.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the error message should be displayed in the UI.\n\n\n    \n    \n    title: str\n\ndefault `= \"Info\"`\n\nThe title to be displayed to the user at the top of the modal.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "blocks_chained_events\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def failure(): raise gr.Error(\"This should\nfail!\") def exception(): raise ValueError(\"Something went wrong\") def\nsuccess(): return True def warning_fn(): gr.Warning(\"This is a warning!\") def\ninfo_fn(): gr.Info(\"This is some info\") with gr.Blocks() as demo:\ngr.Markdown(\"Used in E2E tests of success event trigger. The then event\ncovered in chatbot E2E tests.\" \" Also testing that the status modals show\nup.\") with gr.Row(): result = gr.Textbox(label=\"Result\") result_2 =\ngr.Textbox(label=\"Consecutive Event\") with gr.Row(): success_btn =\ngr.Button(value=\"Trigger Success\") success_btn_2 = gr.Button(value=\"Trigger\nConsecutive Success\") failure_btn = gr.Button(value=\"Trigger Failure\")\nfailure_exception = gr.Button(value=\"Trigger Failure With ValueError\") with\ngr.Row(): trigger_warning = gr.Button(value=\"Trigger Warning\") trigger_info =\ngr.Button(value=\"Trigger Info\") success_btn_2.click(success, None,\nNone).success(lambda: \"First Event Trigered\", None, result).success(lambda:\n\"Consecutive Event Triggered\", None, result_2) success_btn.click(success,\nNone, None).success(lambda: \"Success event triggered\", inputs=None,\noutputs=result) failure_btn.click(failure, None, None).success(lambda: \"Should\nnot be triggered\", inputs=None, outputs=result)\nfailure_exception.click(exception, None, None)\ntrigger_warning.click(warning_fn, None, None) trigger_info.click(info_fn,\nNone, None) if __name__ == \"__main__\": demo.launch(show_error=True)\n\n    \n    \n    import gradio as gr\n    \n    def failure():\n        raise gr.Error(\"This should fail!\")\n    \n    def exception():\n        raise ValueError(\"Something went wrong\")\n    \n    def success():\n        return True\n    \n    def warning_fn():\n        gr.Warning(\"This is a warning!\")\n    \n    def info_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n        ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "fo_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n                    \" Also testing that the status modals show up.\")\n        with gr.Row():\n            result = gr.Textbox(label=\"Result\")\n            result_2 = gr.Textbox(label=\"Consecutive Event\")\n        with gr.Row():\n            success_btn = gr.Button(value=\"Trigger Success\")\n            success_btn_2 = gr.Button(value=\"Trigger Consecutive Success\")\n            failure_btn = gr.Button(value=\"Trigger Failure\")\n            failure_exception = gr.Button(value=\"Trigger Failure With ValueError\")\n        with gr.Row():\n            trigger_warning = gr.Button(value=\"Trigger Warning\")\n            trigger_info = gr.Button(value=\"Trigger Info\")\n    \n            success_btn_2.click(success, None, None).success(lambda: \"First Event Trigered\", None, result).success(lambda: \"Consecutive Event Triggered\", None, result_2)\n            success_btn.click(success, None, None).success(lambda: \"Success event triggered\", inputs=None, outputs=result)\n            failure_btn.click(failure, None, None).success(lambda: \"Should not be triggered\", inputs=None, outputs=result)\n            failure_exception.click(exception, None, None)\n            trigger_warning.click(warning_fn, None, None)\n            trigger_info.click(info_fn, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch(show_error=True)\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/info", "source_page_title": "Gradio - Info Docs"}, {"text": "Mount a gradio.Blocks to an existing FastAPI application.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": "from fastapi import FastAPI\n    import gradio as gr\n    app = FastAPI()\n    @app.get(\"/\")\n    def read_main():\n        return {\"message\": \"This is your main app\"}\n    io = gr.Interface(lambda x: \"Hello, \" + x + \"!\", \"textbox\", \"textbox\")\n    app = gr.mount_gradio_app(app, io, path=\"/gradio\")\n\nThen run `uvicorn run:app` from the terminal and navigate to\nhttp://localhost:8000/gradio.\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    app: fastapi.FastAPI\n\nThe parent FastAPI application.\n\n\n    \n    \n    blocks: gradio.Blocks\n\nThe blocks object we want to mount to the parent app.\n\n\n    \n    \n    path: str\n\nThe path at which the gradio application will be mounted, e.g. \"/gradio\".\n\n\n    \n    \n    server_name: str\n\ndefault `= \"0.0.0.0\"`\n\nThe server name on which the Gradio app will be run.\n\n\n    \n    \n    server_port: int\n\ndefault `= 7860`\n\nThe port on which the Gradio app will be run.\n\n\n    \n    \n    show_api: bool | None\n\ndefault `= None`\n\nIf False, hides the \"Use via API\" button on the Gradio interface.\n\n\n    \n    \n    app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    auth: Callable | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access the gradio app. Can also provide function that takes\nusername and password and returns True if valid login.\n\n\n    \n    \n    auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page for this gradio app.\n\n\n    \n    \n    auth_dependency: Callable[[fastapi.Request], str | None] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the gradio app (they will see a 401 Unauthorized\nresponse). To be used with external authentication systems like OAuth. Cannot\nbe used with `auth`.\n\n\n    \n    \n    root_path: str | None\n\ndefault `= None`\n\nThe subpath corresponding to the public deployment of this FastAPI\napplication. For example, if the application is served at\n\"https://example.com/myapp\", the `root_path` should be set to \"/myapp\". A full\nURL beginning with http:// or https:// can be provided, wh", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": "tAPI\napplication. For example, if the application is served at\n\"https://example.com/myapp\", the `root_path` should be set to \"/myapp\". A full\nURL beginning with http:// or https:// can be provided, which will be used in\nits entirety. Normally, this does not need to provided (even if you are using\na custom `path`). However, if you are serving the FastAPI app behind a proxy,\nthe proxy may not provide the full path to the Gradio app in the request\nheaders. In which case, you can provide the root path here.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that this gradio app is\nallowed to serve. Must be absolute paths. Warning: if you provide directories,\nany files in these directories or their subdirectories are accessible to all\nusers of your app.\n\n\n    \n    \n    blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that this gradio app is not\nallowed to serve (i.e. users of your app are not allowed to access). Must be\nabsolute paths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default.\n\n\n    \n    \n    favicon_path: str | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for this gradio app's page.\n\n\n    \n    \n    show_error: bool\n\ndefault `= True`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. Otherwise, errors will only be visible in\nthe terminal session running the Gradio app.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwh", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": " is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 20+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n    \n    \n    node_server_name: str | None\n\ndefault `= None`\n\nThe name of the Node server to use for SSR. If None, will use\nGRADIO_NODE_SERVER_NAME environment variable or search for a node binary in\nthe system.\n\n\n    \n    \n    node_port: int | None\n\ndefault `= None`\n\nThe port on which the Node server should run. If None, will use\nGRADIO_NODE_SERVER_PORT environment variable or find a free port.\n\n\n    \n    \n    enable_monitoring: bool | None\n\ndefault `= None`\n\n\n    \n    \n    pwa: bool | None\n\ndefault `= None`\n\n\n    \n    \n    i18n: I18n | None\n\ndefault `= None`\n\nIf provided, the i18n instance to use for this gradio app.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the MCP server will be launched on the gradio app. If None, will use\nGRADIO_MCP_SERVER environment variable or default to False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/mount_gradio_app", "source_page_title": "Gradio - Mount_Gradio_App Docs"}, {"text": "Creates a component allows users to upload or view 3D Model files (.obj,\n.glb, .stl, .gltf, .splat, or .ply).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "**As input component** : Passes the uploaded file as a `str` filepath to\nthe function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects function to return a `str` or\n`pathlib.Path` filepath of type (.obj, .glb, .stl, or .gltf)\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Callable | None\n\ndefault `= None`\n\npath to (.obj, .glb, .stl, .gltf, .splat, or .ply) file to show in model3D\nviewer. If a function is provided, the function will be called each time the\napp loads to set the initial value of this component.\n\n\n    \n    \n    display_mode: Literal['solid', 'point_cloud', 'wireframe'] | None\n\ndefault `= None`\n\nthe display mode of the 3D model in the scene. Can be \"solid\" (which renders\nthe model as a solid object), \"point_cloud\", or \"wireframe\". For .splat, or\n.ply files, this parameter is ignored, as those files can only be rendered as\nsolid objects.\n\n\n    \n    \n    clear_color: tuple[float, float, float, float] | None\n\ndefault `= None`\n\nbackground color of scene, should be a tuple of 4 floats between 0 and 1\nrepresenting RGBA values.\n\n\n    \n    \n    camera_position: tuple[int | float | None, int | float | None, int | float | None]\n\ndefault `= (None, None, None)`\n\ninitial camera position of scene, provided as a tuple of `(alpha, beta,\nradius)`. Each value is optional. If provided, `alpha` and `beta` should be in\ndegrees reflecting the angular position along the longitudinal and latitudinal\naxes, respectively. Radius corresponds to the distance from the center of the\nobject to the camera.\n\n\n    \n    \n    zoom_speed: float\n\ndefault `= 1`\n\nthe speed of zooming in and out of the scene when the cursor wheel is rotated\nor when screen is pinched on a mobile device. Should be a positive float,\nincrease this value to make zooming faster, decrease to make it slower.\nAffects the wheelPrecision property of the camera.\n\n\n    \n    \n    pan_speed: float\n\ndefault `= 1`\n\nthe speed of panning the scene when the cursor is dragged or when the screen\nis dragged on a mobile device. Should be a positive float, increase this value\nto make panning faster, decrease to make it slower. Affects the panSensibility\nproperty of the camera.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the model3D c", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "his value\nto make panning faster, decrease to make it slower. Affects the panSensibility\nproperty of the camera.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the model3D component, specified in pixels if a number is\npassed, or in CSS units if a string is passed.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool |", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "reen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload a file; if False, can only be used to\ndisplay files. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Model3D` | \"model3d\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Model3D component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Model3D.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Model3D changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Model3D.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Model3D.  \n`Model3D.edit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user edits the Model3D (e.g. image) using the built-in editor.  \n`Model3D.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Model3D using the clear button for the component.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.compo", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": " inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "ress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pe", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "one\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "| tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/model3d", "source_page_title": "Gradio - Model3D Docs"}, {"text": "Displays a classification label, along with confidence scores of top\ncategories, if provided. As this component does not accept user input, it is\nrarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "**As input component** : Depending on the value, passes the label as a `str | int | float`, or the labels and confidences as a `dict[str, float]`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: dict[str, float] | str | int | float | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `dict[str, float]` of classes and confidences, or `str` with just the class or an `int | float` for regression outputs, or a `str` path to a .json file containing a json dictionary in one of the preceding formats.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> dict[str, float] | str | int | float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: dict[str, float] | str | float | Callable | None\n\ndefault `= None`\n\nDefault value to show in the component. If a str or number is provided, simply\ndisplays the string or number. If a {Dict[str, float]} of classes and\nconfidences is provided, displays the top class on top and the\n`num_top_classes` below, along with their confidence bars. If a function is\nprovided, the function will be called each time the app loads to set the\ninitial value of this component.\n\n\n    \n    \n    num_top_classes: int | None\n\ndefault `= None`\n\nnumber of most confident classes to show.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_he", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nThe background color of the label (either a valid css color name or\nhexadecimal string).\n\n\n    \n    \n    show", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "e values\nprovided during constructor.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nThe background color of the label (either a valid css color name or\nhexadecimal string).\n\n\n    \n    \n    show_heading: bool\n\ndefault `= True`\n\nIf False, the heading will not be displayed if a dictionary of labels and\nconfidences is provided. The heading will still be visible if the value is a\nstring or number.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Label` | \"label\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Label component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Label.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Label changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Label.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Label. Uses event data gradio.SelectData to carry `value` referring to the label of the Label, and `selected` to refer to state of the Label. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an emp", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "ontext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queu", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "lt `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would al", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": " not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "ener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/label", "source_page_title": "Gradio - Label Docs"}, {"text": "Creates a set of (string or numeric type) radio buttons of which only one\ncan be selected.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "**As input component** : Passes the value of the selected radio button as a `str | int | float`, or its index as an `int` into the function, depending on `type`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | int | float | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str | int | float` corresponding to the value of the radio button to be selected\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | int | float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    choices: list[str | int | float | tuple[str, str | int | float]] | None\n\ndefault `= None`\n\nA list of string or numeric options to select from. An option can also be a\ntuple of the form (name, value), where name is the displayed name of the radio\nbutton and value is the value to be passed to the function, or returned by the\nfunction.\n\n\n    \n    \n    value: str | int | float | Callable | None\n\ndefault `= None`\n\nThe option selected by default. If None, no option is selected by default. If\na function is provided, the function will be called each time the app loads to\nset the initial value of this component.\n\n\n    \n    \n    type: Literal['value', 'index']\n\ndefault `= \"value\"`\n\nType of value to be returned by component. \"value\" returns the string of the\nchoice selected, \"index\" returns the index of the choice selected.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nRelative width compared to adjacent Components in a Row. For example, if\nComponent A has scale=2, and Component B has scale=1, A will be twice as wide\nas B. Should be an integer.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nMinimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, choices in this radio group will be selectable; if False, selection\nwill be disabled. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_k", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True, the radio buttons will be displayed in right-to-left order. Default\nis False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Radio` | \"radio\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "sentence_builderblocks_essay\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": ",\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr countries_cities_dict = { \"USA\": [\"New York\",\n\"Los Angeles\", \"Chicago\"], \"Canada\": [\"Toronto\", \"Montreal\", \"Vancouver\"],\n\"Pakistan\": [\"Karachi\", \"Lahore\", \"Islamabad\"], } def change_textbox(choice):\nif choice == \"short\": return gr.Textbox(lines=2, visible=True),\ngr.Button(interactive=True) elif choice == \"long\": return gr.Textbox(lines=8,\nvisible=True, value=\"Lorem ipsum dolor sit amet\"), gr.Button(interactive=True)\nelse: return gr.Textbox(visible=False), gr.Button(interactive=False) with\ngr.Blocks() as demo: radio = gr.Radio( [\"short\", \"long\", \"none\"], label=\"What\nkind of essay would you like to write?\" ) text = gr.Textbox(lines=2,\ninteractive=True, show_copy_button=True) with gr.Row(): num =\ngr.Number(minimum=0, maximum=100, label=\"input\") out =\ngr.Number(label=\"output\") minimum_slider = gr.Slider(0, 100, 0, label=\"min\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "ines=2,\ninteractive=True, show_copy_button=True) with gr.Row(): num =\ngr.Number(minimum=0, maximum=100, label=\"input\") out =\ngr.Number(label=\"output\") minimum_slider = gr.Slider(0, 100, 0, label=\"min\")\nmaximum_slider = gr.Slider(0, 100, 100, label=\"max\") submit_btn =\ngr.Button(\"Submit\", variant=\"primary\") with gr.Row(): country =\ngr.Dropdown(list(countries_cities_dict.keys()), label=\"Country\") cities =\ngr.Dropdown([], label=\"Cities\") @country.change(inputs=country,\noutputs=cities) def update_cities(country): cities =\nlist(countries_cities_dict[country]) return gr.Dropdown(choices=cities,\nvalue=cities[0], interactive=True) def reset_bounds(minimum, maximum): return\ngr.Number(minimum=minimum, maximum=maximum) radio.change(fn=change_textbox,\ninputs=radio, outputs=[text, submit_btn]) gr.on( [minimum_slider.change,\nmaximum_slider.change], reset_bounds, [minimum_slider, maximum_slider],\noutputs=num, ) num.submit(lambda x: x, num, out) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    countries_cities_dict = {\n        \"USA\": [\"New York\", \"Los Angeles\", \"Chicago\"],\n        \"Canada\": [\"Toronto\", \"Montreal\", \"Vancouver\"],\n        \"Pakistan\": [\"Karachi\", \"Lahore\", \"Islamabad\"],\n    }\n    \n    def change_textbox(choice):\n        if choice == \"short\":\n            return gr.Textbox(lines=2, visible=True), gr.Button(interactive=True)\n        elif choice == \"long\":\n            return gr.Textbox(lines=8, visible=True, value=\"Lorem ipsum dolor sit amet\"), gr.Button(interactive=True)\n        else:\n            return gr.Textbox(visible=False), gr.Button(interactive=False)\n    \n    with gr.Blocks() as demo:\n        radio = gr.Radio(\n            [\"short\", \"long\", \"none\"], label=\"What kind of essay would you like to write?\"\n        )\n        text = gr.Textbox(lines=2, interactive=True, show_copy_button=True)\n    \n        with gr.Row():\n            num = gr.Number(minimum=0, maximum=100, label=\"input\")\n            out = gr.Number(label=\"output\")\n        m", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "x(lines=2, interactive=True, show_copy_button=True)\n    \n        with gr.Row():\n            num = gr.Number(minimum=0, maximum=100, label=\"input\")\n            out = gr.Number(label=\"output\")\n        minimum_slider = gr.Slider(0, 100, 0, label=\"min\")\n        maximum_slider = gr.Slider(0, 100, 100, label=\"max\")\n        submit_btn = gr.Button(\"Submit\", variant=\"primary\")\n    \n        with gr.Row():\n            country = gr.Dropdown(list(countries_cities_dict.keys()), label=\"Country\")\n            cities = gr.Dropdown([], label=\"Cities\")\n        @country.change(inputs=country, outputs=cities)\n        def update_cities(country):\n            cities = list(countries_cities_dict[country])\n            return gr.Dropdown(choices=cities, value=cities[0], interactive=True)\n    \n        def reset_bounds(minimum, maximum):\n            return gr.Number(minimum=minimum, maximum=maximum)\n    \n        radio.change(fn=change_textbox, inputs=radio, outputs=[text, submit_btn])\n        gr.on(\n            [minimum_slider.change, maximum_slider.change],\n            reset_bounds,\n            [minimum_slider, maximum_slider],\n            outputs=num,\n        )\n        num.submit(lambda x: x, num, out)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Radio component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Radio.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Radio. Uses event data gradio.SelectData to carry `value` referring to the label of the Radio, and `selected` to refer to state of the Radio. See EventData documentation on how to use this event data  \n`Radio.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Radio changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Radio.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Radio.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nLi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": " function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "will show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions ar", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-rende", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "   key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/radio", "source_page_title": "Gradio - Radio Docs"}, {"text": "The gr.SelectData class is a subclass of gr.EventData that specifically\ncarries information about the `.select()` event. When gr.SelectData is added\nas a type hint to an argument of an event listener method, a gr.SelectData\nobject will automatically be passed as the value of that argument. The\nattributes of this object contains information about the event that triggered\nthe listener.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "import gradio as gr\n    \n    with gr.Blocks() as demo:\n        table = gr.Dataframe([[1, 2, 3], [4, 5, 6]])\n        gallery = gr.Gallery([(\"cat.jpg\", \"Cat\"), (\"dog.jpg\", \"Dog\")])\n        textbox = gr.Textbox(\"Hello World!\")\n        statement = gr.Textbox()\n    \n        def on_select(evt: gr.SelectData):\n            return f\"You selected {evt.value} at {evt.index} from {evt.target}\"\n    \n        table.select(on_select, None, statement)\n        gallery.select(on_select, None, statement)\n        textbox.select(on_select, None, statement)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the selected item. Is a tuple if the component is two dimensional\nor selection is a range.\n\n\n    \n    \n    value: Any\n\nThe value of the selected item.\n\n\n    \n    \n    row_value: list[float | str]\n\nThe value of the entire row that the selected item belongs to, as a 1-D list.\nOnly implemented for the `Dataframe` component, returns None for other\ncomponents.\n\n\n    \n    \n    col_value: list[float | str]\n\nThe value of the entire column that the selected item belongs to, as a 1-D\nlist. Only implemented for the `Dataframe` component, returns None for other\ncomponents.\n\n\n    \n    \n    selected: bool\n\nTrue if the item was selected, False if deselected.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "gallery_selectionstictactoe\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np with gr.Blocks() as demo:\nimgs = gr.State() gallery = gr.Gallery(allow_preview=False) def\ndeselect_images(): return gr.Gallery(selected_index=None) def\ngenerate_images(): images = [] for _ in range(9): image = np.ones((100, 100,\n3), dtype=np.uint8) * np.random.randint( 0, 255, 3 ) image is a solid single\ncolor images.append(image) return images, images demo.load(generate_images,\nNone, [gallery, imgs]) with gr.Row(): selected = gr.Number(show_label=False)\ndarken_btn = gr.Button(\"Darken selected\") deselect_button =\ngr.Button(\"Deselect\") deselect_button.click(deselect_images, None, gallery)\ndef get_select_index(evt: gr.SelectData): return evt.index\ngallery.select(get_select_index, None, selected) def darken_img(imgs, index):\nindex = int(index) imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\nreturn imgs, imgs darken_btn.click(darken_img, [imgs, selected], [imgs,\ngallery]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    \n    with gr.Blocks() as demo:\n        imgs = gr.State()\n        gallery = gr.Gallery(allow_preview=False)\n    \n        def deselect_images():\n            return gr.Gallery(selected_index=None)\n    \n        def generate_images():\n            images = []\n            for _ in range(9):\n                image = np.ones((100, 100, 3), dtype=np.uint8) * np.random.randint(\n                    0, 255, 3\n                )  image is a solid single color\n                images.append(image)\n            return images, images\n    \n        demo.load(generate_images, None, [gallery, imgs])\n    \n        with gr.Row():\n            selected = gr.Number(show_label=False)\n            darken_btn = gr.Button(\"Darken selected\")\n        deselect_button = gr.Button(\"Deselect\")\n    \n        deselect_button.click(deselect_images, None, gallery)\n    \n        def get_select_index(evt: gr.SelectData):\n            return evt.index\n    \n  ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "  deselect_button = gr.Button(\"Deselect\")\n    \n        deselect_button.click(deselect_images, None, gallery)\n    \n        def get_select_index(evt: gr.SelectData):\n            return evt.index\n    \n        gallery.select(get_select_index, None, selected)\n    \n        def darken_img(imgs, index):\n            index = int(index)\n            imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\n            return imgs, imgs\n    \n        darken_btn.click(darken_img, [imgs, selected], [imgs, gallery])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr with gr.Blocks() as demo: turn =\ngr.Textbox(\"X\", interactive=False, label=\"Turn\") board =\ngr.Dataframe(value=[[\"\", \"\", \"\"]] * 3, interactive=False, type=\"array\") def\nplace(board: list[list[int]], turn, evt: gr.SelectData): if evt.value: return\nboard, turn board[evt.index[0]][evt.index[1]] = turn turn = \"O\" if turn == \"X\"\nelse \"X\" return board, turn board.select(place, [board, turn], [board, turn],\nshow_progress=\"hidden\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    with gr.Blocks() as demo:\n        turn = gr.Textbox(\"X\", interactive=False, label=\"Turn\")\n        board = gr.Dataframe(value=[[\"\", \"\", \"\"]] * 3, interactive=False, type=\"array\")\n    \n        def place(board: list[list[int]], turn, evt: gr.SelectData):  \n            if evt.value:\n                return board, turn\n            board[evt.index[0]][evt.index[1]] = turn\n            turn = \"O\" if turn == \"X\" else \"X\"\n            return board, turn\n    \n        board.select(place, [board, turn], [board, turn], show_progress=\"hidden\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/selectdata", "source_page_title": "Gradio - Selectdata Docs"}, {"text": "Creates a component to displays a base image and colored annotations on top\nof that image. Annotations can take the from of rectangles (e.g. object\ndetection) or masks (e.g. image segmentation). As this component does not\naccept user input, it is rarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "**As input component** : Passes its value as a `tuple` consisting of a\n`str` filepath to a base image and `list` of annotations. Each annotation\nitself is `tuple` of a mask (as a `str` filepath to image) and a `str` label.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: tuple[str, list[tuple[str, str]]] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a a tuple of a base image and list of\nannotations: a `tuple[Image, list[Annotation]]`. The `Image` itself can be\n`str` filepath, `numpy.ndarray`, or `PIL.Image`. Each `Annotation` is a\n`tuple[Mask, str]`. The `Mask` can be either a `tuple` of 4 `int`'s\nrepresenting the bounding box coordinates (x1, y1, x2, y2), or 0-1 confidence\nmask in the form of a `numpy.ndarray` of the same shape as the image, while\nthe second element of the `Annotation` tuple is a `str` label.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> tuple[np.ndarray | PIL.Image.Image | str, list[tuple[np.ndarray | tuple[int, int, int, int], str]]] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: tuple[np.ndarray | PIL.Image.Image | str, list[tuple[np.ndarray | tuple[int, int, int, int], str]]] | None\n\ndefault `= None`\n\nTuple of base image and list of (annotation, label) pairs.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFormat used to save images before it is returned to the front end, such as\n'jpeg' or 'png'. This parameter only takes effect when the base image is\nreturned from the prediction function as a numpy array or a PIL Image. The\nformat should be supported by the PIL library.\n\n\n    \n    \n    show_legend: bool\n\ndefault `= True`\n\nIf True, will show a legend of the annotations.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfile or numpy array, but will affect the displayed image.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfile or numpy array, but will affect the displayed image.\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nA dictionary mapping labels to colors. The colors must be specified as hex\ncodes.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "ct otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nRelative width compared to adjacent Components in a Row. For example, if\nComponent A has scale=2, and Component B has scale=1, A will be twice as wide\nas B. Should be an integer.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nMinimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a button to allow the image to be viewed in fullscreen\nmode.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.AnnotatedImage` | \"annotatedimage\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "image_segmentation\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np import random with\ngr.Blocks() as demo: section_labels = [ \"apple\", \"banana\", \"carrot\", \"donut\",\n\"eggplant\", \"fish\", \"grapes\", \"hamburger\", \"ice cream\", \"juice\", ] with\ngr.Row(): num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\nnum_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\") with\ngr.Row(): img_input = gr.Image() img_output = gr.AnnotatedImage(\ncolor_map={\"banana\": \"a89a00\", \"carrot\": \"ffae00\"} ) section_btn =\ngr.Button(\"Identify Sections\") selected_section = gr.Textbox(label=\"Selected\nSection\") def section(img, num_boxes, num_segments): sections = [] for a in\nrange(num_boxes): x = random.randint(0, img.shape[1]) y = random.randint(0,\nimg.shape[0]) w = random.randint(0, img.shape[1] - x) h = random.randint(0,\nimg.shape[0] - y) sections.append(((x, y, x + w, y + h), section_labels[a]))\nfor b in range(num_segments): x = random.randint(0, img.shape[1]) y =\nrandom.randint(0, img.shape[0]) r = random.randint(0, min(x, y, img.shape[1] -\nx, img.shape[0] - y)) mask = np.zeros(img.shape[:2]) for i in\nrange(img.shape[0]): for j in range(img.shape[1]): dist_square = (i - y) ** 2\n+ (j - x) ** 2 if dist_square < r**2: mask[i, j] = round((r**2 - dist_square)\n/ r**2 * 4) / 4 sections.append((mask, section_labels[b + num_boxes])) return\n(img, sections) section_btn.click(section, [img_input, num_boxes,\nnum_segments], img_output) def select_section(evt: gr.SelectData): return\nsection_labels[evt.index] img_output.select(select_section, None,\nselected_section) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    import random\n    \n    with gr.Blocks() as demo:\n        section_labels = [\n            \"apple\",\n            \"banana\",\n            \"carrot\",\n            \"donut\",\n            \"eggplant\",\n            \"fish\",\n            \"grapes\",\n            \"hamburger\",\n            \"ice cream\",\n            \"juice\",\n        ]\n    \n        w", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "        \"carrot\",\n            \"donut\",\n            \"eggplant\",\n            \"fish\",\n            \"grapes\",\n            \"hamburger\",\n            \"ice cream\",\n            \"juice\",\n        ]\n    \n        with gr.Row():\n            num_boxes = gr.Slider(0, 5, 2, step=1, label=\"Number of boxes\")\n            num_segments = gr.Slider(0, 5, 1, step=1, label=\"Number of segments\")\n    \n        with gr.Row():\n            img_input = gr.Image()\n            img_output = gr.AnnotatedImage(\n                color_map={\"banana\": \"a89a00\", \"carrot\": \"ffae00\"}\n            )\n    \n        section_btn = gr.Button(\"Identify Sections\")\n        selected_section = gr.Textbox(label=\"Selected Section\")\n    \n        def section(img, num_boxes, num_segments):\n            sections = []\n            for a in range(num_boxes):\n                x = random.randint(0, img.shape[1])\n                y = random.randint(0, img.shape[0])\n                w = random.randint(0, img.shape[1] - x)\n                h = random.randint(0, img.shape[0] - y)\n                sections.append(((x, y, x + w, y + h), section_labels[a]))\n            for b in range(num_segments):\n                x = random.randint(0, img.shape[1])\n                y = random.randint(0, img.shape[0])\n                r = random.randint(0, min(x, y, img.shape[1] - x, img.shape[0] - y))\n                mask = np.zeros(img.shape[:2])\n                for i in range(img.shape[0]):\n                    for j in range(img.shape[1]):\n                        dist_square = (i - y) ** 2 + (j - x) ** 2\n                        if dist_square < r**2:\n                            mask[i, j] = round((r**2 - dist_square) / r**2 * 4) / 4\n                sections.append((mask, section_labels[b + num_boxes]))\n            return (img, sections)\n    \n        section_btn.click(section, [img_input, num_boxes, num_segments], img_output)\n    \n        def select_section(evt: gr.SelectData):\n            return section_labels[evt.index]\n    \n        img_output.select(select_s", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "ick(section, [img_input, num_boxes, num_segments], img_output)\n    \n        def select_section(evt: gr.SelectData):\n            return section_labels[evt.index]\n    \n        img_output.select(select_section, None, selected_section)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe AnnotatedImage component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`AnnotatedImage.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the AnnotatedImage. Uses event data gradio.SelectData to carry `value` referring to the label of the AnnotatedImage, and `selected` to refer to state of the AnnotatedImage. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (d", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "ault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return sho", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/annotatedimage", "source_page_title": "Gradio - Annotatedimage Docs"}, {"text": "Interface is Gradio's main high-level class, and allows you to create a\nweb-based GUI / demo around a machine learning model (or any Python function)\nin a few lines of code. You must specify three parameters: (1) the function to\ncreate a GUI for (2) the desired input components and (3) the desired output\ncomponents. Additional parameters can be used to control the appearance and\nbehavior of the demo.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "import gradio as gr\n    \n    def image_classifier(inp):\n        return {'cat': 0.3, 'dog': 0.7}\n    \n    demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\")\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    fn: Callable\n\nthe function to wrap an interface around. Often a machine learning model's\nprediction function. Each parameter of the function corresponds to one input\ncomponent, and the function should return a single value or a tuple of values,\nwith each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of input components should match the number of parameters in fn. If\nset to None, then only the output components will be displayed.\n\n\n    \n    \n    outputs: str | Component | list[str | Component] | None\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThe number of output components should match the number of values returned by\nfn. If set to None, then only the input components will be displayed.\n\n\n    \n    \n    examples: list[Any] | list[list[Any]] | str | None\n\ndefault `= None`\n\nsample inputs for the function; if provided, appear below the UI components\nand can be clicked to populate the interface. Should be nested list, in which\nthe outer list consists of samples and each inner list consists of an input\ncorresponding to each input component. A string path to a directory of\nexamples can also be provided, but it should be within the directory with the\npython file running the gradio app. If there are multiple input components and\na directory is provided, a log.csv file must be present in the directory to\nlink corresponding inputs.\n\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "\n\nIf True, caches examples in the server for fast runtime in examples. If\n\"lazy\", then examples are cached (for all users of the app) after their first\nuse (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES\nenvironment variable, which should be either \"true\" or \"false\". In HuggingFace\nSpaces, this parameter defaults to True (as long as `fn` and `outputs` are\nalso provided). Note that examples are cached separately from Gradio's queue()\nso certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will\nnot be displayed in Gradio's UI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"lazy\", examples are cached after their first use. If \"eager\", all examples\nare cached at app launch. If None, will use the GRADIO_CACHE_MODE environment\nvariable if defined, or default to \"eager\". In HuggingFace Spaces, this\nparameter defaults to \"eager\" except for ZeroGPU Spaces, in which case it\ndefaults to \"lazy\".\n\n\n    \n    \n    examples_per_page: int\n\ndefault `= 10`\n\nif examples are provided, how many to display per page.\n\n\n    \n    \n    example_labels: list[str] | None\n\ndefault `= None`\n\na list of labels for each example. If provided, the length of this list should\nbe the same as the number of examples, and these labels will be used in the UI\ninstead of rendering the example values.\n\n\n    \n    \n    preload_example: int | Literal[False]\n\ndefault `= False`\n\nIf an integer is provided (and examples are being cached), the example at that\nindex in the examples list will be preloaded when the Gradio app is first\nloaded. If False, no example will be preloaded.\n\n\n    \n    \n    live: bool\n\ndefault `= False`\n\nwhether the interface should automatically rerun if any of the inputs change.\n\n\n    \n    \n    title: str | I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": " I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above the input and output\ncomponents in large font. Also used as the tab title when opened in a browser\nwindow.\n\n\n    \n    \n    description: str | None\n\ndefault `= None`\n\na description for the interface; if provided, appears above the input and\noutput components and beneath the title in regular font. Accepts Markdown and\nHTML content.\n\n\n    \n    \n    article: str | None\n\ndefault `= None`\n\nan expanded article explaining the interface; if provided, appears below the\ninput and output components in regular font. Accepts Markdown and HTML\ncontent. If it is an HTTP(S) link to a downloadable remote file, the content\nof this file is displayed.\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\na Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    flagging_mode: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\none of \"never\", \"auto\", or \"manual\". If \"never\" or \"auto\", users will not see\na button to flag an input and output. If \"manual\", users will see a button to\nflag. If \"auto\", every input the user submits will be automatically flagged,\nalong with the generated output. If \"manual\", both the input and outputs are\nflagged when the user clicks flag button. This parameter can be set with\nenvironmental variable GRADIO_FLAGGING_MODE; otherwise defaults to \"manual\".\n\n\n    \n    \n    flagging_options: list[str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nif provided, allows user to select from the list of options when flagging.\nOnly applies if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "es if flagging_mode is \"manual\". Can either be a list of tuples of\nthe form (label, value), where label is the string that will be displayed on\nthe button and value is the string that will be stored in the flagging CSV; or\nit can be a list of strings [\"X\", \"Y\"], in which case the values will be the\nlist of strings and the labels will [\"Flag as X\", \"Flag as Y\"], etc.\n\n\n    \n    \n    flagging_dir: str\n\ndefault `= \".gradio/flagged\"`\n\npath to the the directory where flagged data is stored. If the directory does\nnot exist, it will be created.\n\n\n    \n    \n    flagging_callback: FlaggingCallback | None\n\ndefault `= None`\n\neither None or an instance of a subclass of FlaggingCallback which will be\ncalled when a sample is flagged. If set to None, an instance of\ngradio.flagging.CSVLogger will be created and logs will be saved to a local\nCSV file in flagging_dir. Default to None.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nif True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nthe maximum number of inputs to batch together if this is called from the\nqueue (only relevant if batch=True)\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show the prediction endpoint in the \"view API\" page of the Gradio\napp, or in the \".view_api()\" method of the Gradio clients. Unlike setting\napi_name to False, setting show_api to False will still allow downstream apps\nas well as the Clients to use this event.\n\n\n    \n    \n ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": " in the \".view_api()\" method of the Gradio clients. Unlike setting\napi_name to False, setting show_api to False will still allow downstream apps\nas well as the Clients to use this event.\n\n\n    \n    \n    api_name: str | Literal[False] | None\n\ndefault `= \"predict\"`\n\ndefines how the prediction endpoint appears in the API docs. Can be a string,\nNone, or False. If set to a string, the endpoint will be exposed in the API\ndocs with the given name. If None, the name of the prediction function will be\nused as the API endpoint. If False, the endpoint will not be exposed in the\nAPI docs and downstream apps (including those that `gr.load` this app) will\nnot be able to use this prediction endpoint.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    allow_duplication: bool\n\ndefault `= False`\n\nif True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nif set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`.queue()`, which itself is 1 by default).\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n    \n    \n    css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo web", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a code string. The custom js should be in the form of a single js\nfunction. This function will automatically be executed when the page loads.\nFor more flexibility, use the head parameter to insert js inside <script>\ntags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html code to insert into the head of the demo webpage. This can be used\nto add custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n\n    \n    \n    head_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` parameter is also set, the html from `head` will\nbe included first.\n\n\n    \n    \n    additional_inputs: str | Component | list[str | Component] | None\n\ndefault `= None`\n\na single Gradio component, or list of Gradio components. Components can either\nbe passed as instantiated objects, or referred to by their string shortcuts.\nThese components will be rendered in an accordion below the main input\ncomponents. By default, no additional input components will be displayed.\n\n\n    \n    \n    additional_inputs_accordion: str | Accordion | None\n\ndefault `= None`\n\nif a string is provided, this is the label of the `gr.Accordion` to use to\ncontain additional inputs. A `gr.Accordion` object can be provided as well to\nconfigure other properties of the container holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    submit_btn: ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ntainer holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    submit_btn: str | Button\n\ndefault `= \"Submit\"`\n\nthe button to use for submitting inputs. Defaults to a `gr.Button(\"Submit\",\nvariant=\"primary\")`. This parameter does not apply if the Interface is output-\nonly, in which case the submit button always displays \"Generate\". Can be set\nto a string (which becomes the button label) or a `gr.Button` object (which\nallows for more customization).\n\n\n    \n    \n    stop_btn: str | Button\n\ndefault `= \"Stop\"`\n\nthe button to use for stopping the interface. Defaults to a `gr.Button(\"Stop\",\nvariant=\"stop\", visible=False)`. Can be set to a string (which becomes the\nbutton label) or a `gr.Button` object (which allows for more customization).\n\n\n    \n    \n    clear_btn: str | Button | None\n\ndefault `= \"Clear\"`\n\nthe button to use for clearing the inputs. Defaults to a `gr.Button(\"Clear\",\nvariant=\"secondary\")`. Can be set to a string (which becomes the button label)\nor a `gr.Button` object (which allows for more customization). Can be set to\nNone, which hides the button.\n\n\n    \n    \n    delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\na tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "gress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    fill_width: bool\n\ndefault `= False`\n\nwhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width.\n\n\n    \n    \n    allow_flagging: Literal['never'] | Literal['auto'] | Literal['manual'] | None\n\ndefault `= None`\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= 30`\n\nThe time limit for the stream to run. Default is 30 seconds. Parameter only\nused for streaming images or audio if the interface is live and the input\ncomponents are set to \"streaming=True\".\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\nThe latency (in seconds) at which stream chunks are sent to the backend.\nDefaults to 0.5 seconds. Parameter only used for streaming images or audio if\nthe interface is live and the input components are set to \"streaming=True\".\n\n\n    \n    \n    deep_link: str | DeepLinkButton | bool | None\n\ndefault `= None`\n\na string or `gr.DeepLinkButton` object that creates a unique URL you can use\nto share your app and all components **as they currently are** with others.\nAutomatically enabled on Hugging Face Spaces unless explicitly set to False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "hello_worldhello_world_2hello_world_3\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name): return \"Hello \" + name + \"!\"\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\") if __name__\n== \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    \n    def greet(name):\n        return \"Hello \" + name + \"!\"\n    \n    \n    demo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, intensity): return \"Hello, \" +\nname + \"!\" * intensity demo = gr.Interface( fn=greet, inputs=[\"text\",\ngr.Slider(value=2, minimum=1, maximum=10, step=1)],\noutputs=[gr.Textbox(label=\"greeting\", lines=3)], ) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, intensity):\n        return \"Hello, \" + name + \"!\" * intensity\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", gr.Slider(value=2, minimum=1, maximum=10, step=1)],\n        outputs=[gr.Textbox(label=\"greeting\", lines=3)],\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, is_morning, temperature):\nsalutation = \"Good morning\" if is_morning else \"Good evening\" greeting =\nf\"{salutation} {name}. It is {temperature} degrees today\" celsius =\n(temperature - 32) * 5 / 9 return greeting, round(celsius, 2) demo =\ngr.Interface( fn=greet, inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\noutputs=[\"text\", \"number\"], ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, is_morning, temperature):\n        salutation = \"Good morning\" if is_morning else \"Good evening\"\n        greeting = f\"{salutation} {name}. It is {temperature} degrees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 10", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "grees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n        outputs=[\"text\", \"number\"],\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.launch(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nLaunches a simple web server that serves the demo. Can also be used to create\na public link used by anyone to access the demo from their browser by setting\nshare=True.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    def reverse(text):\n        return text[::-1]\n    demo = gr.Interface(reverse, \"text\", \"text\")\n    demo.launch(share=True, auth=(\"username\", \"password\"))\n\nParameters \u25bc\n\n\n    \n    \n    inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the de", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "pp inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the default\nbrowser.\n\n\n    \n    \n    share: bool | None\n\ndefault `= None`\n\nwhether to create a publicly shareable link for the gradio app. Creates an SSH\ntunnel to make your UI accessible from anywhere. If not provided, it is set to\nFalse by default every time, except when running in Google Colab. When\nlocalhost is not accessible (e.g. Google Colab), setting share=False is not\nsupported. Can be set by environment variable GRADIO_SHARE=True.\n\n\n    \n    \n    debug: bool\n\ndefault `= False`\n\nif True, blocks the main thread from running. If running in Google Colab, this\nis needed to print the errors in the cell output.\n\n\n    \n    \n    max_threads: int\n\ndefault `= 40`\n\nthe maximum number of total threads that the Gradio app can generate in\nparallel. The default is inherited from the starlette library (currently 40).\n\n\n    \n    \n    auth: Callable[[str, str], bool] | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access app. Can also provide function that takes username and\npassword and returns True if valid login.\n\n\n    \n    \n    auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page.\n\n\n    \n    \n    prevent_thread_lock: bool\n\ndefault `= False`\n\nBy default, the gradio app blocks the main thread while the server is running.\nIf set to True, the gradio app will not block and the gradio server will\nterminate as soon as the script finishes.\n\n\n    \n    \n    show_error: bool\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= No", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= None`\n\nto make app accessible on local network, set this to \"0.0.0.0\". Can be set by\nenvironment variable GRADIO_SERVER_NAME. If None, will use \"127.0.0.1\".\n\n\n    \n    \n    server_port: int | None\n\ndefault `= None`\n\nwill start gradio app on this port (if available). Can be set by environment\nvariable GRADIO_SERVER_PORT. If None, will search for an available port\nstarting at 7860.\n\n\n    \n    \n    height: int\n\ndefault `= 500`\n\nThe height in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    width: int | str\n\ndefault `= \"100%\"`\n\nThe width in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    favicon_path: str | Path | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for the web page.\n\n\n    \n    \n    ssl_keyfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the private key file to\ncreate a local server running on https.\n\n\n    \n    \n    ssl_certfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the signed certificate for\nhttps. Needs to be provided if ssl_keyfile is provided.\n\n\n    \n    \n    ssl_keyfile_password: str | None\n\ndefault `= None`\n\nIf a password is provided, will use this with the ssl certificate for https.\n\n\n    \n    \n    ssl_verify: bool\n\ndefault `= True`\n\nIf False, skips certificate validation which allows self-signed certificates\nto be used.\n\n\n    \n    \n    quiet: bool\n\ndefault `= False`\n\nIf True, suppresses most print statements.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent di", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": ": bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is allowed to\nserve. Must be absolute paths. Warning: if you provide directories, any files\nin these directories or their subdirectories are accessible to all users of\nyour app. Can be set by comma separated environment variable\nGRADIO_ALLOWED_PATHS. These files are generally assumed to be secure and will\nbe displayed in the browser when possible.\n\n\n    \n    \n    blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is not allowed to\nserve (i.e. users of your app are not allowed to access). Must be absolute\npaths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default. Can be set by comma separated\nenvironment variable GRADIO_BLOCKED_PATHS.\n\n\n    \n    \n    root_path: str | None\n\ndefault `= None`\n\nThe root path (or \"mount point\") of the application, if it's not served from\nthe root (\"/\") of the domain. Often used when the application is behind a\nreverse proxy that forwards requests to the application. For example, if the\napplication is served at \"https://example.com/myapp\", the `root_path` should\nbe set to \"/myapp\". A full URL beginning with http:// or https:// can be\nprovided, which will be used as the root path in its entirety. Can be set by\nenvironment variable GRADIO_ROOT_PATH. Defaults to \"\".\n\n\n    \n    \n    app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capac", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capacity to reduce memory usage when using gradio.State or returning\nupdated components from functions. Defaults to 10000.\n\n\n    \n    \n    share_server_address: str | None\n\ndefault `= None`\n\nUse this to specify a custom FRP server and port for sharing Gradio apps (only\napplies if share=True). If not provided, will use the default FRP server at\nhttps://gradio.live. See https://github.com/huggingface/frp for more\ninformation.\n\n\n    \n    \n    share_server_protocol: Literal['http', 'https'] | None\n\ndefault `= None`\n\nUse this to specify the protocol to use for the share links. Defaults to\n\"https\", unless a custom share_server_address is provided, in which case it\ndefaults to \"http\". If you are using a custom share_server_address and want to\nuse https, you must set this to \"https\".\n\n\n    \n    \n    share_server_tls_certificate: str | None\n\ndefault `= None`\n\nThe path to a TLS certificate file to use when connecting to a custom share\nserver. This parameter is not used with the default FRP server at\nhttps://gradio.live. Otherwise, you must provide a valid TLS certificate file\n(e.g. a \"cert.pem\") relative to the current working directory, or the\nconnection will not use TLS encryption, which is insecure.\n\n\n    \n    \n    auth_dependency: Callable[[fastapi.Request], str | None] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the app (they will see a 401 Unauthorized response). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any ", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ed with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    enable_monitoring: bool | None\n\ndefault `= None`\n\nEnables traffic monitoring of the app through the /monitoring endpoint. By\ndefault is None, which enables this endpoint. If explicitly True, will also\nprint the monitoring URL to the console. If False, will disable monitoring\naltogether.\n\n\n    \n    \n    strict_cors: bool\n\ndefault `= True`\n\nIf True, prevents external domains from making requests to a Gradio server\nrunning on localhost. If False, allows requests to localhost that originate\nfrom localhost but also, crucially, from \"null\". This parameter should\nnormally be True to prevent CSRF attacks but may need to be False when\nembedding a *locally-running Gradio app* using web components.\n\n\n    \n    \n    node_server_name: str | None\n\ndefault `= None`\n\n\n    \n    \n    node_port: int | None\n\ndefault `= None`\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 20+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n    \n    \n    pwa: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an installable PWA (Progressive Web\nApp). If set to None (default behavior), then the PWA feature will be enabled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_M", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "   mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_MCP_SERVER environment variable will be used to determine if the MCP\nserver should be enabled (which is \"True\" on Hugging Face Spaces).\n\n\n    \n    \n    i18n: I18n | None\n\ndefault `= None`\n\nAn I18n instance containing custom translations, which are used to translate\nstrings in our components (e.g. the labels of components or Markdown strings).\nThis feature can only be used to translate static text in the frontend, not\nvalues in the backend.\n\n", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.load(block, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%2", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Interface initially loads in the browser.\n\nParameters \u25bc\n\n\n    \n    \n    block: Block | None\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple correspondin", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": " prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime ", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "w the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click met", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "f other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api w", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ew_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.from_pipeline(pipeline, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nClass method that constructs an Interface from a Hugging Face\ntransformers.Pipeline or diffusers.DiffusionPipeline object. The input and\noutput components are automatically determined from the pipeline.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Co", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    from transformers import pipeline\n    pipe = pipeline(\"image-classification\")\n    gr.Interface.from_pipeline(pipe).launch()\n\nParameters \u25bc\n\n\n    \n    \n    pipeline: Pipeline | DiffusionPipeline\n\nthe pipeline object to use.\n\n", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ne object to use.\n\n", "heading1": "from_pipeline", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.integrate(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20I", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nA catch-all method for integrating with other libraries. This method should be\nrun after launch()\n\nParameters \u25bc\n\n\n    \n    \n    comet_ml: <class 'inspect._empty'>\n\ndefault `= None`\n\nIf a comet_ml Experiment object is provided, will integrate with the\nexperiment and appear on Comet dashboard\n\n\n    \n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "\n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n\nIf the mlflow module is provided, will integrate with the experiment and\nappear on ML Flow dashboard\n\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Interface.queue(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nBy enabling the queue you can control when users know their position in the\nqueue, and set a limit on maximum number of events allowed.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "ro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    demo = gr.Interface(image_generator, gr.Textbox(), gr.Image())\n    demo.queue(max_size=20)\n    demo.launch()\n\nParameters \u25bc\n\n\n    \n    \n    status_update_rate: float | Literal['auto']\n\ndefault `= \"auto\"`\n\nIf \"auto\", Queue will send status estimations to all clients whenever a job is\nfinished. Otherwise Queue will send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowin", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": " send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowing requests made\ndirectly to those endpoints to skip the queue.\n\n\n    \n    \n    max_size: int | None\n\ndefault `= None`\n\nThe maximum number of events the queue will store at any given moment. If the\nqueue is full, new events will not be added and a user will receive a message\nsaying that the queue is full. If None, the queue size will be unlimited.\n\n\n    \n    \n    default_concurrency_limit: int | None | Literal['not_set']\n\ndefault `= \"not_set\"`\n\nThe default value of `concurrency_limit` to use for event listeners that don't\nspecify a value. Can be set by environment variable\nGRADIO_DEFAULT_CONCURRENCY_LIMIT. Defaults to 1 if not set otherwise.\n\n", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/interface#interface-queue", "source_page_title": "Gradio - Interface#Interface Queue Docs"}, {"text": "Displays an interactive table of parameters and their descriptions and\ndefault values with syntax highlighting. For each parameter, the user should\nprovide a type (e.g. a `str`), a human-readable description, and a default\nvalue. As this component does not accept user input, it is rarely used as an\ninput component. Internally, this component is used to display the parameters\nof components in the Custom Component Gallery (https://www.gradio.app/custom-\ncomponents/gallery).\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "**As input component** : (Rarely used) passes value as a `dict[str, dict]`.\nThe key in the outer dictionary is the parameter name, while the inner\ndictionary has keys \"type\", \"description\", and \"default\" for each parameter.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: dict[str, Parameter]\n    )\n    \t...\n\n  \n\n**As output component** : Expects value as a `dict[str, dict]`. The key in\nthe outer dictionary is the parameter name, while the inner dictionary has\nkeys \"type\", \"description\", and \"default\" for each parameter.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> dict[str, Parameter]\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: dict[str, Parameter] | None\n\ndefault `= None`\n\nA dictionary of dictionaries. The key in the outer dictionary is the parameter\nname, while the inner dictionary has keys \"type\", \"description\", and \"default\"\nfor each parameter. Markdown links are supported in \"description\".\n\n\n    \n    \n    language: Literal['python', 'typescript']\n\ndefault `= \"python\"`\n\nThe language to display the code in. One of \"python\" or \"typescript\".\n\n\n    \n    \n    linkify: list[str] | None\n\ndefault `= None`\n\nA list of strings to linkify. If any of these strings is found in the\ndescription, it will be rendered as a link.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "er()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    header: str | None\n\ndefault `= \"Parameters\"`\n\nThe header to display above the table of parameters, also includes a toggle\nbutton that closes/opens all details at once. If None, no header will be\ndisplayed.\n\n\n    \n    \n    anchor_links: bool | str\n\ndefault `= False`\n\nIf True, creates anchor links for each parameter that can be used to link\ndirectly to that parameter. If a string, creates anchor links with the given\nstring as the prefix to prevent conflicts with other ParamViewer components.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ParamViewer` | \"paramviewer\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ParamViewer component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ParamViewer.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the ParamViewer changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`ParamViewer.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the ParamViewer.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, o", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "on returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nsh", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method t", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/paramviewer", "source_page_title": "Gradio - Paramviewer Docs"}, {"text": "Creates a plot component to display various kinds of plots (matplotlib,\nplotly, altair, or bokeh plots are supported). As this component does not\naccept user input, it is rarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "**As input component** : (Rarely used) passes the data displayed in the\nplot as an PlotData dataclass, which includes the plot information as a JSON\nstring, as well as the type of chart and the plotting library.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: PlotData | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects plot data in one of these formats: a\nmatplotlib.Figure, bokeh.Model, plotly.Figure, or altair.Chart object.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> Any\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: Any | None\n\ndefault `= None`\n\nOptionally, supply a default plot object to display, must be a matplotlib,\nplotly, altair, or bokeh figure, or a callable. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFile format in which to send matplotlib plots to the front end, such as 'jpg'\nor 'png'.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a cert", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "o top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Plot` | \"plot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "blocks_kinematicsstock_forecast\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd import numpy as np import gradio as gr def\nplot(v, a): g = 9.81 theta = a / 180 * 3.14 tmax = ((2 * v) * np.sin(theta)) /\ng timemat = tmax * np.linspace(0, 1, 40) x = (v * timemat) * np.cos(theta) y =\n((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2)) df =\npd.DataFrame({\"x\": x, \"y\": y}) return df demo = gr.Blocks() with demo:\ngr.Markdown( r\"Let's do some kinematics! Choose the speed and angle to see the\ntrajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n) with gr.Row(): speed = gr.Slider(1, 30, 25, label=\"Speed\") angle =\ngr.Slider(0, 90, 45, label=\"Angle\") output = gr.LinePlot( x=\"x\", y=\"y\",\noverlay_point=True, tooltip=[\"x\", \"y\"], x_lim=[0, 100], y_lim=[0, 60],\nwidth=350, height=300, ) btn = gr.Button(value=\"Run\") btn.click(plot, [speed,\nangle], output) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import pandas as pd\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot(v, a):\n        g = 9.81\n        theta = a / 180 * 3.14\n        tmax = ((2 * v) * np.sin(theta)) / g\n        timemat = tmax * np.linspace(0, 1, 40)\n    \n        x = (v * timemat) * np.cos(theta)\n        y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))\n        df = pd.DataFrame({\"x\": x, \"y\": y})\n        return df\n    \n    demo = gr.Blocks()\n    \n    with demo:\n        gr.Markdown(\n            r\"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n        )\n    \n        with gr.Row():\n            speed = gr.Slider(1, 30, 25, label=\"Speed\")\n            angle = gr.Slider(0, 90, 45, label=\"Angle\")\n        output = gr.LinePlot(\n            x=\"x\",\n            y=\"y\",\n            overlay_point=True,\n            tooltip=[\"x\", \"y\"],\n            x_lim=[0, 100],\n            y_lim=[0, 60],\n            width=350,\n            height=300,\n        )\n        btn = gr.Button(value=\"Run\")\n ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "erlay_point=True,\n            tooltip=[\"x\", \"y\"],\n            x_lim=[0, 100],\n            y_lim=[0, 60],\n            width=350,\n            height=300,\n        )\n        btn = gr.Button(value=\"Run\")\n        btn.click(plot, [speed, angle], output)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import matplotlib.pyplot as plt import numpy as np import gradio\nas gr def plot_forecast(final_year, companies, noise, show_legend,\npoint_style): start_year = 2020 x = np.arange(start_year, final_year + 1)\nyear_count = x.shape[0] plt_format = ({\"cross\": \"X\", \"line\": \"-\", \"circle\":\n\"o--\"})[point_style] fig = plt.figure() ax = fig.add_subplot(111) for i,\ncompany in enumerate(companies): series = np.arange(0, year_count,\ndtype=float) series = series**2 * (i + 1) series += np.random.rand(year_count)\n* noise ax.plot(x, series, plt_format) if show_legend: plt.legend(companies)\nreturn fig demo = gr.Interface( plot_forecast, [ gr.Radio([2025, 2030, 2035,\n2040], label=\"Project to:\"), gr.CheckboxGroup([\"Google\", \"Microsoft\",\n\"Gradio\"], label=\"Company Selection\"), gr.Slider(1, 100, label=\"Noise Level\"),\ngr.Checkbox(label=\"Show Legend\"), gr.Dropdown([\"cross\", \"line\", \"circle\"],\nlabel=\"Style\"), ], gr.Plot(label=\"forecast\", format=\"png\"), ) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    import matplotlib.pyplot as plt\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot_forecast(final_year, companies, noise, show_legend, point_style):\n        start_year = 2020\n        x = np.arange(start_year, final_year + 1)\n        year_count = x.shape[0]\n        plt_format = ({\"cross\": \"X\", \"line\": \"-\", \"circle\": \"o--\"})[point_style]\n        fig = plt.figure()\n        ax = fig.add_subplot(111)\n        for i, company in enumerate(companies):\n            series = np.arange(0, year_count, dtype=float)\n            series = series**2 * (i + 1)\n            series += np.random.rand(year_count) * noise\n            ax.plot(x, series, plt_format)\n       ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "     series = np.arange(0, year_count, dtype=float)\n            series = series**2 * (i + 1)\n            series += np.random.rand(year_count) * noise\n            ax.plot(x, series, plt_format)\n        if show_legend:\n            plt.legend(companies)\n        return fig\n    \n    demo = gr.Interface(\n        plot_forecast,\n        [\n            gr.Radio([2025, 2030, 2035, 2040], label=\"Project to:\"),\n            gr.CheckboxGroup([\"Google\", \"Microsoft\", \"Gradio\"], label=\"Company Selection\"),\n            gr.Slider(1, 100, label=\"Noise Level\"),\n            gr.Checkbox(label=\"Show Legend\"),\n            gr.Dropdown([\"cross\", \"line\", \"circle\"], label=\"Style\"),\n        ],\n        gr.Plot(label=\"forecast\", format=\"png\"),\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Plot component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`Plot.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Plot changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the fun", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": " how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). Th", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "n the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "teral[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/plot", "source_page_title": "Gradio - Plot Docs"}, {"text": "Constructs a Gradio app automatically from a Hugging Face model/Space repo\nname or a 3rd-party API provider. Note that if a Space repo is loaded, certain\nhigh-level attributes of the Blocks (e.g. custom `css`, `js`, and `head`\nattributes) will not be loaded.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/load", "source_page_title": "Gradio - Load Docs"}, {"text": "import gradio as gr\n    demo = gr.load(\"gradio/question-answering\", src=\"spaces\")\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/load", "source_page_title": "Gradio - Load Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    name: str\n\nthe name of the model (e.g. \"google/vit-base-patch16-224\") or Space (e.g.\n\"flax-community/spanish-gpt2\"). This is the first parameter passed into the\n`src` function. Can also be formatted as {src}/{repo name} (e.g.\n\"models/google/vit-base-patch16-224\") if `src` is not provided.\n\n\n    \n    \n    src: Callable[[str, str | None], Blocks] | Literal['models', 'spaces'] | None\n\ndefault `= None`\n\nfunction that accepts a string model `name` and a string or None `token` and\nreturns a Gradio app. Alternatively, this parameter takes one of two strings\nfor convenience: \"models\" (for loading a Hugging Face model through the\nInference API) or \"spaces\" (for loading a Hugging Face Space). If None, uses\nthe prefix of the `name` parameter to determine `src`.\n\n\n    \n    \n    token: str | None\n\ndefault `= None`\n\noptional token that is passed as the second parameter to the `src` function.\nIf not explicitly provided, will use the HF_TOKEN environment variable or\nfallback to the locally-saved HF token when loading models but not Spaces\n(when loading Spaces, only provide a token if you are loading a trusted\nprivate Space as the token can be read by the Space you are loading). Find\nyour HF tokens here: https://huggingface.co/settings/tokens.\n\n\n    \n    \n    hf_token: str | None\n\ndefault `= None`\n\n\n    \n    \n    accept_token: bool | LoginButton\n\ndefault `= False`\n\nif True, a Textbox component is first rendered to allow the user to provide a\ntoken, which will be used instead of the `token` parameter when calling the\nloaded model or Space. Can also provide an instance of a gr.LoginButton in the\nsame Blocks scope, which allows the user to login with a Hugging Face account\nwhose token will be used instead of the `token` parameter when calling the\nloaded model or Space.\n\n\n    \n    \n    provider: PROVIDER_T | None\n\ndefault `= None`\n\nthe name of the third-party (non-Hugging Face) providers to use for model\ninference (e.g. \"replicate\", \"sambanova\", \"fal-ai\", et", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/load", "source_page_title": "Gradio - Load Docs"}, {"text": "r Space.\n\n\n    \n    \n    provider: PROVIDER_T | None\n\ndefault `= None`\n\nthe name of the third-party (non-Hugging Face) providers to use for model\ninference (e.g. \"replicate\", \"sambanova\", \"fal-ai\", etc). Should be one of the\nproviders supported by `huggingface_hub.InferenceClient`. This parameter is\nonly used when `src` is \"models\"\n\n\n    \n    \n    kwargs: <class 'inspect._empty'>\n\nadditional keyword parameters to pass into the `src` function. If `src` is\n\"models\" or \"Spaces\", these parameters are passed into the `gr.Interface` or\n`gr.ChatInterface` constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/load", "source_page_title": "Gradio - Load Docs"}, {"text": "dict() -> new empty dictionary dict(mapping) -> new dictionary initialized\nfrom a mapping object's (key, value) pairs dict(iterable) -> new dictionary\ninitialized as if via: d = `` for k, v in iterable: d[k] = v dict(__kwargs) -\n> new dictionary initialized with the name=value pairs in the keyword argument\nlist. For example: dict(one=1, two=2)\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dependency", "source_page_title": "Gradio - Dependency Docs"}, {"text": "Set the static paths to be served by the gradio app.  \nStatic files are are served directly from the file system instead of being\ncopied. They are served to users with The Content-Disposition HTTP header set\nto \"inline\" when sending these files to users. This indicates that the file\nshould be displayed directly in the browser window if possible. This function\nis useful when you want to serve files that you know will not be modified\nduring the lifetime of the gradio app (like files used in gr.Examples). By\nsetting static paths, your app will launch faster and it will consume less\ndisk space. Calling this function will set the static paths for all gradio\napplications defined in the same interpreter session until it is called again\nor the session ends.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/set_static_paths", "source_page_title": "Gradio - Set_Static_Paths Docs"}, {"text": "import gradio as gr\n    \n    Paths can be a list of strings or pathlib.Path objects\n    corresponding to filenames or directories.\n    gr.set_static_paths(paths=[\"test/test_files/\"])\n    \n    The example files and the default value of the input\n    will not be copied to the gradio cache and will be served directly.\n    demo = gr.Interface(\n        lambda s: s.rotate(45),\n        gr.Image(value=\"test/test_files/cheetah1.jpg\", type=\"pil\"),\n        gr.Image(),\n        examples=[\"test/test_files/bus.png\"],\n    )\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/set_static_paths", "source_page_title": "Gradio - Set_Static_Paths Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    paths: str | Path | list[str | Path]\n\nfilepath or list of filepaths or directory names to be served by the gradio\napp. If it is a directory name, ALL files located within that directory will\nbe considered static and not moved to the gradio cache. This also means that\nALL files in that directory will be accessible over the network.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/set_static_paths", "source_page_title": "Gradio - Set_Static_Paths Docs"}, {"text": "This function allows you to pass custom warning messages to the user. You\ncan do so simply by writing `gr.Warning('message here')` in your function, and\nwhen that line is executed the custom message will appear in a modal on the\ndemo. The modal is yellow by default and has the heading: \"Warning.\" Queue\nmust be enabled for this behavior; otherwise, the warning will be printed to\nthe console using the `warnings` library.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "import gradio as gr\n    def hello_world():\n        gr.Warning('This is a warning message.')\n        return \"hello world\"\n    with gr.Blocks() as demo:\n        md = gr.Markdown()\n        demo.load(hello_world, inputs=None, outputs=[md])\n    demo.queue().launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    message: str\n\ndefault `= \"Warning issued.\"`\n\nThe warning message to be displayed to the user. Can be HTML, which will be\nrendered in the modal.\n\n\n    \n    \n    duration: float | None\n\ndefault `= 10`\n\nThe duration in seconds that the warning message should be displayed for. If\nNone or 0, the message will be displayed indefinitely until the user closes\nit.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the error message should be displayed in the UI.\n\n\n    \n    \n    title: str\n\ndefault `= \"Warning\"`\n\nThe title to be displayed to the user at the top of the modal.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "blocks_chained_events\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def failure(): raise gr.Error(\"This should\nfail!\") def exception(): raise ValueError(\"Something went wrong\") def\nsuccess(): return True def warning_fn(): gr.Warning(\"This is a warning!\") def\ninfo_fn(): gr.Info(\"This is some info\") with gr.Blocks() as demo:\ngr.Markdown(\"Used in E2E tests of success event trigger. The then event\ncovered in chatbot E2E tests.\" \" Also testing that the status modals show\nup.\") with gr.Row(): result = gr.Textbox(label=\"Result\") result_2 =\ngr.Textbox(label=\"Consecutive Event\") with gr.Row(): success_btn =\ngr.Button(value=\"Trigger Success\") success_btn_2 = gr.Button(value=\"Trigger\nConsecutive Success\") failure_btn = gr.Button(value=\"Trigger Failure\")\nfailure_exception = gr.Button(value=\"Trigger Failure With ValueError\") with\ngr.Row(): trigger_warning = gr.Button(value=\"Trigger Warning\") trigger_info =\ngr.Button(value=\"Trigger Info\") success_btn_2.click(success, None,\nNone).success(lambda: \"First Event Trigered\", None, result).success(lambda:\n\"Consecutive Event Triggered\", None, result_2) success_btn.click(success,\nNone, None).success(lambda: \"Success event triggered\", inputs=None,\noutputs=result) failure_btn.click(failure, None, None).success(lambda: \"Should\nnot be triggered\", inputs=None, outputs=result)\nfailure_exception.click(exception, None, None)\ntrigger_warning.click(warning_fn, None, None) trigger_info.click(info_fn,\nNone, None) if __name__ == \"__main__\": demo.launch(show_error=True)\n\n    \n    \n    import gradio as gr\n    \n    def failure():\n        raise gr.Error(\"This should fail!\")\n    \n    def exception():\n        raise ValueError(\"Something went wrong\")\n    \n    def success():\n        return True\n    \n    def warning_fn():\n        gr.Warning(\"This is a warning!\")\n    \n    def info_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n        ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "fo_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n                    \" Also testing that the status modals show up.\")\n        with gr.Row():\n            result = gr.Textbox(label=\"Result\")\n            result_2 = gr.Textbox(label=\"Consecutive Event\")\n        with gr.Row():\n            success_btn = gr.Button(value=\"Trigger Success\")\n            success_btn_2 = gr.Button(value=\"Trigger Consecutive Success\")\n            failure_btn = gr.Button(value=\"Trigger Failure\")\n            failure_exception = gr.Button(value=\"Trigger Failure With ValueError\")\n        with gr.Row():\n            trigger_warning = gr.Button(value=\"Trigger Warning\")\n            trigger_info = gr.Button(value=\"Trigger Info\")\n    \n            success_btn_2.click(success, None, None).success(lambda: \"First Event Trigered\", None, result).success(lambda: \"Consecutive Event Triggered\", None, result_2)\n            success_btn.click(success, None, None).success(lambda: \"Success event triggered\", inputs=None, outputs=result)\n            failure_btn.click(failure, None, None).success(lambda: \"Should not be triggered\", inputs=None, outputs=result)\n            failure_exception.click(exception, None, None)\n            trigger_warning.click(warning_fn, None, None)\n            trigger_info.click(info_fn, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch(show_error=True)\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/warning", "source_page_title": "Gradio - Warning Docs"}, {"text": "Creates an image component that, as an input, can be used to upload and\nedit images using simple editing tools such as brushes, strokes, cropping, and\nlayers. Or, as an output, this component can be used to display images.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "**As input component** : Passes the uploaded images as an instance of\nEditorValue, which is just a `dict` with keys: 'background', 'layers', and\n'composite'. The values corresponding to 'background' and 'composite' are\nimages, while 'layers' is a `list` of images. The images are of type\n`PIL.Image`, `np.array`, or `str` filepath, depending on the `type` parameter.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: EditorValue | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a EditorValue, which is just a dictionary\nwith keys: 'background', 'layers', and 'composite'. The values corresponding\nto 'background' and 'composite' should be images or None, while `layers`\nshould be a list of images. Images can be of type `PIL.Image`, `np.array`, or\n`str` filepath/URL. Or, the value can be simply a single image (`ImageType`),\nin which case it will be used as the background.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> EditorValue | ImageType | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: EditorValue | ImageType | None\n\ndefault `= None`\n\nOptional initial image(s) to populate the image editor. Should be a dictionary\nwith keys: `background`, `layers`, and `composite`. The values corresponding\nto `background` and `composite` should be images or None, while `layers`\nshould be a list of images. Images can be of type PIL.Image, np.array, or str\nfilepath/URL. Or, the value can be a callable, in which case the function will\nbe called whenever the app loads to set the initial value of the component.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfiles or numpy arrays, but will affect the displayed images. Beware of\nconflicting values with the canvas_size paramter. If the canvas_size is larger\nthan the height, the editing canvas will not fit in the component.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfiles or numpy arrays, but will affect the displayed images. Beware of\nconflicting values with the canvas_size paramter. If the canvas_size is larger\nthan the height, the editing canvas will not fit in the component.\n\n\n    \n    \n    image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F']\n\ndefault `= \"RGBA\"`\n\n\"RGB\" if color, or \"L\" if black and white. See\nhttps://pillow.readthedocs.io/en/stable/handbook/concepts.html for other\nsupported image modes and their meaning.\n\n\n    \n    \n    sources: Iterable[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None\n\ndefault `= ('upload', 'webcam', 'clipboard')`\n\nList of sources that can be used to set the background image. \"upload\" creates\na box where user can drop an image file, \"webc", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "bcam', 'clipboard'] | None\n\ndefault `= ('upload', 'webcam', 'clipboard')`\n\nList of sources that can be used to set the background image. \"upload\" creates\na box where user can drop an image file, \"webcam\" allows user to take snapshot\nfrom their webcam, \"clipboard\" allows users to paste an image from the\nclipboard.\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe format the images are converted to before being passed into the prediction\nfunction. \"numpy\" converts the images to numpy arrays with shape (height,\nwidth, 3) and values from 0 to 255, \"pil\" converts the images to PIL image\nobjects, \"filepath\" passes images as str filepaths to temporary copies of the\nimages.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_download_button: bool\n\ndefault `= True`\n\nIf True, will display button to download image.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Componen", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "ue`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload and edit an image; if False, can only be\nused to display images. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "mponent, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\nCustom text for the upload area. Overrides default upload messages when\nprovided. Accepts new lines and `` to designate a heading.\n\n\n    \n    \n    mirror_webcam: bool | None\n\ndefault `= None`\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    crop_size: tuple[int | float, int | float] | str | None\n\ndefault `= None`\n\nDeprecated. Used to set the `canvas_size` parameter.\n\n\n    \n    \n    transforms: Iterable[Literal['crop', 'resize']] | None\n\ndefault `= ('crop', 'resize')`\n\nThe transforms tools to make available to users. \"crop\" allows the user to\ncrop the image.\n\n\n    \n    \n    eraser: Eraser | None | Literal[False]\n\ndefault `= None`\n\nThe options for the eraser tool in the image editor. Should be an instance of\nthe `gr.Eraser` class, or None to use the default settings. Can also be False\nto hide the eraser tool. See `gr.Eraser` docs.\n\n\n    \n    \n    brush: Brush | None | Literal[False]\n\ndefault `= None`\n\nThe options for the brush tool in the image editor. Should be an instance of\nthe `gr.Brush` class, or None to use the default settings. Can also be False\nto hide the brush tool, which will also hide t", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "The options for the brush tool in the image editor. Should be an instance of\nthe `gr.Brush` class, or None to use the default settings. Can also be False\nto hide the brush tool, which will also hide the eraser tool. See `gr.Brush`\ndocs.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFormat to save image if it does not already have a valid format (e.g. if the\nimage is being returned to the frontend as a numpy array or PIL Image). The\nformat should be supported by the PIL library. This parameter has no effect on\nSVG files.\n\n\n    \n    \n    layers: bool | LayerOptions\n\ndefault `= True`\n\nThe options for the layer tool in the image editor. Can be a boolean or an\ninstance of the `gr.LayerOptions` class. If True, will allow users to add\nlayers to the image. If False, the layers option will be hidden. If an\ninstance of `gr.LayerOptions`, it will be used to configure the layer tool.\nSee `gr.LayerOptions` docs.\n\n\n    \n    \n    canvas_size: tuple[int, int]\n\ndefault `= (800, 800)`\n\nThe initial size of the canvas in pixels. The first value is the width and the\nsecond value is the height. If `fixed_canvas` is `True`, uploaded images will\nbe rescaled to fit the canvas size while preserving the aspect ratio.\nOtherwise, the canvas size will change to match the size of an uploaded image.\n\n\n    \n    \n    fixed_canvas: bool\n\ndefault `= False`\n\nIf True, the canvas size will not change based on the size of the background\nimage and the image will be rescaled to fit (while preserving the aspect\nratio) and placed in the center of the canvas.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will display button to view image in fullscreen mode.\n\n\n    \n    \n    webcam_options: WebcamOptions | None\n\ndefault `= None`\n\nThe options for the webcam tool in the image editor. Can be an instance of the\n`gr.WebcamOptions` class, or None to use the default settings. See\n`gr.WebcamOptions` docs.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "be an instance of the\n`gr.WebcamOptions` class, or None to use the default settings. See\n`gr.WebcamOptions` docs.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ImageEditor` | \"imageeditor\" | Uses default values  \n`gradio.Sketchpad` | \"sketchpad\" | Uses sources=(), brush=Brush(colors=[\"000000\"], color_mode=\"fixed\")  \n`gradio.Paint` | \"paint\" | Uses sources=()  \n`gradio.ImageMask` | \"imagemask\" | Uses brush=Brush(colors=[\"000000\"], color_mode=\"fixed\")  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "image_editor\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import time def sleep(im): time.sleep(5)\nreturn [im[\"background\"], im[\"layers\"][0], im[\"layers\"][1], im[\"composite\"]]\ndef predict(im): return im[\"composite\"] with gr.Blocks() as demo: with\ngr.Row(): im = gr.ImageEditor( type=\"numpy\", crop_size=\"1:1\", ) im_preview =\ngr.Image() n_upload = gr.Number(0, label=\"Number of upload events\", step=1)\nn_change = gr.Number(0, label=\"Number of change events\", step=1) n_input =\ngr.Number(0, label=\"Number of input events\", step=1) im.upload(lambda x: x +\n1, outputs=n_upload, inputs=n_upload) im.change(lambda x: x + 1,\noutputs=n_change, inputs=n_change) im.input(lambda x: x + 1, outputs=n_input,\ninputs=n_input) im.change(predict, outputs=im_preview, inputs=im,\nshow_progress=\"hidden\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import time\n    \n    \n    def sleep(im):\n        time.sleep(5)\n        return [im[\"background\"], im[\"layers\"][0], im[\"layers\"][1], im[\"composite\"]]\n    \n    \n    def predict(im):\n        return im[\"composite\"]\n    \n    \n    with gr.Blocks() as demo:\n        with gr.Row():\n            im = gr.ImageEditor(\n                type=\"numpy\",\n                crop_size=\"1:1\",\n            )\n            im_preview = gr.Image()\n        n_upload = gr.Number(0, label=\"Number of upload events\", step=1)\n        n_change = gr.Number(0, label=\"Number of change events\", step=1)\n        n_input = gr.Number(0, label=\"Number of input events\", step=1)\n    \n        im.upload(lambda x: x + 1, outputs=n_upload, inputs=n_upload)\n        im.change(lambda x: x + 1, outputs=n_change, inputs=n_change)\n        im.input(lambda x: x + 1, outputs=n_input, inputs=n_input)\n        im.change(predict, outputs=im_preview, inputs=im, show_progress=\"hidden\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ImageEditor component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ImageEditor.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the ImageEditor using the clear button for the component.  \n`ImageEditor.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the ImageEditor changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`ImageEditor.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the ImageEditor.  \n`ImageEditor.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the ImageEditor. Uses event data gradio.SelectData to carry `value` referring to the label of the ImageEditor, and `selected` to refer to state of the ImageEditor. See EventData documentation on how to use this event data  \n`ImageEditor.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the ImageEditor.  \n`ImageEditor.apply(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user applies changes to the ImageEditor through an integrated UI action.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each ele", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "ten a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"mi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "default `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "ne\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this ev", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "gradio.Brush(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the brush tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `brush` parameter of\n`gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    default_size: int | Literal['auto']\n\ndefault `= \"auto\"`\n\nThe default radius, in pixels, of the brush tool. Defaults to \"auto\" in which\ncase the radius is automatically determined based on the size of the image\n(generally 1/50th of smaller dimension).\n\n\n    \n    \n    colors: list[str | tuple[str, float]] | str | tuple[str, float] | None\n\ndefault `= None`\n\nA list of colors to make available to the user when using the brush. Defaults\nto a list of 5 colors.\n\n\n    \n    \n    default_color: str | tuple[str, float] | None\n\ndefault `= None`\n\nThe default color of the brush. Defaults to the first color in the `colors`\nlist.\n\n\n    \n    \n    color_mode: Literal['fixed', 'defaults']\n\ndefault `= \"defaults\"`\n\nIf set to \"fixed\", user can only select from among the colors in `colors`. If\n\"defaults\", the colors in `colors` are provided as a default palette, but the\nuser can also select any color using a color picker.\n\n", "heading1": "Brush", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "gradio.Eraser(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the eraser tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `eraser` parameter\nof `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    default_size: int | Literal['auto']\n\ndefault `= \"auto\"`\n\nThe default radius, in pixels, of the eraser tool. Defaults to \"auto\" in which\ncase the radius is automatically determined based on the size of the image\n(generally 1/50th of smaller dimension).\n\n", "heading1": "Eraser", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "gradio.LayerOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the layer tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `layers` parameter\nof `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    allow_additional_layers: bool\n\ndefault `= True`\n\nIf True, users can add additional layers to the image. If False, the add layer\nbutton will not be shown.\n\n\n    \n    \n    layers: list[str] | None\n\ndefault `= None`\n\nA list of layers to make available to the user when using the layer tool. One\nlayer must be provided, if the length of the list is 0 then a layer will be\ngenerated automatically.\n\n\n    \n    \n    disabled: bool\n\ndefault `= False`\n\n", "heading1": "Layer Options", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "gradio.WebcamOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the webcam tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `webcam_options`\nparameter of `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    mirror: bool\n\ndefault `= True`\n\nIf True, the webcam will be mirrored.\n\n\n    \n    \n    constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary of constraints for the webcam.\n\n", "heading1": "Webcam Options", "source_page_url": "https://gradio.app/docs/gradio/imageeditor", "source_page_title": "Gradio - Imageeditor Docs"}, {"text": "Blocks is Gradio's low-level API that allows you to create more custom web\napplications and demos than Interfaces (yet still entirely in Python).  \n  \nCompared to the Interface class, Blocks offers more flexibility and control\nover: (1) the layout of components (2) the events that trigger the execution\nof functions (3) data flows (e.g. inputs can trigger outputs, which can\ntrigger the next level of outputs). Blocks also offers ways to group together\nrelated demos such as with tabs.  \n  \nThe basic usage of Blocks is as follows: create a Blocks object, then use it\nas a context (with the \"with\" statement), and then define layouts, components,\nor events within the Blocks context. Finally, call the launch() method to\nlaunch the demo.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "import gradio as gr\n    def update(name):\n        return f\"Welcome to Gradio, {name}!\"\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Start typing below and then click **Run** to see the output.\")\n        with gr.Row():\n            inp = gr.Textbox(placeholder=\"What is your name?\")\n            out = gr.Textbox()\n        btn = gr.Button(\"Run\")\n        btn.click(fn=update, inputs=inp, outputs=out)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\nA Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nWhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable or default to True.\n\n\n    \n    \n    mode: str\n\ndefault `= \"blocks\"`\n\nA human-friendly name for the kind of Blocks or Interface being created. Used\ninternally for analytics.\n\n\n    \n    \n    title: str | I18nData\n\ndefault `= \"Gradio\"`\n\nThe tab title to display when this is opened in a browser window.\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n    \n    \n    css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a code string. The custom js should be in the form of a single js\nfunction. This function will automatically be executed when the page loads.\nFor more flexibility, use the head parameter to insert js inside <script>\ntags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html code to insert into the head of the demo webpage. This can be used\nto add custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n\n    \n    \n    head_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "e\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` parameter is also set, the html from `head` will\nbe included first.\n\n\n    \n    \n    fill_height: bool\n\ndefault `= False`\n\nWhether to vertically expand top-level child components to the height of the\nwindow. If True, expansion occurs when the scale value of the child components\n>= 1.\n\n\n    \n    \n    fill_width: bool\n\ndefault `= False`\n\nWhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width. Only applies if this is the outermost\n`Blocks` in your Gradio app.\n\n\n    \n    \n    delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\nA tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "blocks_helloblocks_flipperblocks_kinematics\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def welcome(name): return f\"Welcome to Gradio,\n{name}!\" with gr.Blocks() as demo: gr.Markdown( \"\"\" Hello World! Start\ntyping below to see the output. \"\"\") inp = gr.Textbox(placeholder=\"What is\nyour name?\") out = gr.Textbox() inp.change(welcome, inp, out) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def welcome(name):\n        return f\"Welcome to Gradio, {name}!\"\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\n        \"\"\"\n        Hello World!\n        Start typing below to see the output.\n        \"\"\")\n        inp = gr.Textbox(placeholder=\"What is your name?\")\n        out = gr.Textbox()\n        inp.change(welcome, inp, out)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr def flip_text(x): return\nx[::-1] def flip_image(x): return np.fliplr(x) with gr.Blocks() as demo:\ngr.Markdown(\"Flip text or image files using this demo.\") with gr.Tab(\"Flip\nText\"): text_input = gr.Textbox() text_output = gr.Textbox() text_button =\ngr.Button(\"Flip\") with gr.Tab(\"Flip Image\"): with gr.Row(): image_input =\ngr.Image() image_output = gr.Image() image_button = gr.Button(\"Flip\") with\ngr.Accordion(\"Open for More!\", open=False): gr.Markdown(\"Look at me...\")\ntemp_slider = gr.Slider( 0, 1, value=0.1, step=0.1, interactive=True,\nlabel=\"Slide me\", ) text_button.click(flip_text, inputs=text_input,\noutputs=text_output) image_button.click(flip_image, inputs=image_input,\noutputs=image_output) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    import gradio as gr\n    \n    def flip_text(x):\n        return x[::-1]\n    \n    def flip_image(x):\n        return np.fliplr(x)\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Flip text or image files using this demo.\")\n        with gr.Tab(\"Flip Text\"):\n            text_input = gr.Textbox()\n            text_output = gr.Textbox()\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "gr.Blocks() as demo:\n        gr.Markdown(\"Flip text or image files using this demo.\")\n        with gr.Tab(\"Flip Text\"):\n            text_input = gr.Textbox()\n            text_output = gr.Textbox()\n            text_button = gr.Button(\"Flip\")\n        with gr.Tab(\"Flip Image\"):\n            with gr.Row():\n                image_input = gr.Image()\n                image_output = gr.Image()\n            image_button = gr.Button(\"Flip\")\n    \n        with gr.Accordion(\"Open for More!\", open=False):\n            gr.Markdown(\"Look at me...\")\n            temp_slider = gr.Slider(\n                0, 1,\n                value=0.1,\n                step=0.1,\n                interactive=True,\n                label=\"Slide me\",\n            )\n    \n        text_button.click(flip_text, inputs=text_input, outputs=text_output)\n        image_button.click(flip_image, inputs=image_input, outputs=image_output)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd import numpy as np import gradio as gr def\nplot(v, a): g = 9.81 theta = a / 180 * 3.14 tmax = ((2 * v) * np.sin(theta)) /\ng timemat = tmax * np.linspace(0, 1, 40) x = (v * timemat) * np.cos(theta) y =\n((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2)) df =\npd.DataFrame({\"x\": x, \"y\": y}) return df demo = gr.Blocks() with demo:\ngr.Markdown( r\"Let's do some kinematics! Choose the speed and angle to see the\ntrajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n) with gr.Row(): speed = gr.Slider(1, 30, 25, label=\"Speed\") angle =\ngr.Slider(0, 90, 45, label=\"Angle\") output = gr.LinePlot( x=\"x\", y=\"y\",\noverlay_point=True, tooltip=[\"x\", \"y\"], x_lim=[0, 100], y_lim=[0, 60],\nwidth=350, height=300, ) btn = gr.Button(value=\"Run\") btn.click(plot, [speed,\nangle], output) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import pandas as pd\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot(v, a):\n        g = 9.81\n        theta = a / 180 * 3.14\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "_name__ == \"__main__\": demo.launch()\n\n    \n    \n    import pandas as pd\n    import numpy as np\n    \n    import gradio as gr\n    \n    def plot(v, a):\n        g = 9.81\n        theta = a / 180 * 3.14\n        tmax = ((2 * v) * np.sin(theta)) / g\n        timemat = tmax * np.linspace(0, 1, 40)\n    \n        x = (v * timemat) * np.cos(theta)\n        y = ((v * timemat) * np.sin(theta)) - ((0.5 * g) * (timemat**2))\n        df = pd.DataFrame({\"x\": x, \"y\": y})\n        return df\n    \n    demo = gr.Blocks()\n    \n    with demo:\n        gr.Markdown(\n            r\"Let's do some kinematics! Choose the speed and angle to see the trajectory. Remember that the range $R = v_0^2 \\cdot \\frac{\\sin(2\\theta)}{g}$\"\n        )\n    \n        with gr.Row():\n            speed = gr.Slider(1, 30, 25, label=\"Speed\")\n            angle = gr.Slider(0, 90, 45, label=\"Angle\")\n        output = gr.LinePlot(\n            x=\"x\",\n            y=\"y\",\n            overlay_point=True,\n            tooltip=[\"x\", \"y\"],\n            x_lim=[0, 100],\n            y_lim=[0, 60],\n            width=350,\n            height=300,\n        )\n        btn = gr.Button(value=\"Run\")\n        btn.click(plot, [speed, angle], output)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.launch(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "-!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nLaunches a simple web server that serves the demo. Can also be used to create\na public link used by anyone to access the demo from their browser by setting\nshare=True.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    def reverse(text):\n        return text[::-1]\n    with gr.Blocks() as demo:\n        button = gr.Button(value=\"Reverse\")\n        button.click(reverse, gr.Textbox(), gr.Textbox())\n    demo.launch(share=True, auth=(\"username\", \"password\"))\n\nParameters \u25bc\n\n\n    \n    \n    inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault ", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "  \n    inline: bool | None\n\ndefault `= None`\n\nwhether to display in the gradio app inline in an iframe. Defaults to True in\npython notebooks; False otherwise.\n\n\n    \n    \n    inbrowser: bool\n\ndefault `= False`\n\nwhether to automatically launch the gradio app in a new tab on the default\nbrowser.\n\n\n    \n    \n    share: bool | None\n\ndefault `= None`\n\nwhether to create a publicly shareable link for the gradio app. Creates an SSH\ntunnel to make your UI accessible from anywhere. If not provided, it is set to\nFalse by default every time, except when running in Google Colab. When\nlocalhost is not accessible (e.g. Google Colab), setting share=False is not\nsupported. Can be set by environment variable GRADIO_SHARE=True.\n\n\n    \n    \n    debug: bool\n\ndefault `= False`\n\nif True, blocks the main thread from running. If running in Google Colab, this\nis needed to print the errors in the cell output.\n\n\n    \n    \n    max_threads: int\n\ndefault `= 40`\n\nthe maximum number of total threads that the Gradio app can generate in\nparallel. The default is inherited from the starlette library (currently 40).\n\n\n    \n    \n    auth: Callable[[str, str], bool] | tuple[str, str] | list[tuple[str, str]] | None\n\ndefault `= None`\n\nIf provided, username and password (or list of username-password tuples)\nrequired to access app. Can also provide function that takes username and\npassword and returns True if valid login.\n\n\n    \n    \n    auth_message: str | None\n\ndefault `= None`\n\nIf provided, HTML message provided on login page.\n\n\n    \n    \n    prevent_thread_lock: bool\n\ndefault `= False`\n\nBy default, the gradio app blocks the main thread while the server is running.\nIf set to True, the gradio app will not block and the gradio server will\nterminate as soon as the script finishes.\n\n\n    \n    \n    show_error: bool\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "\n\ndefault `= False`\n\nIf True, any errors in the gradio app will be displayed in an alert modal and\nprinted in the browser console log. They will also be displayed in the alert\nmodal of downstream apps that gr.load() this app.\n\n\n    \n    \n    server_name: str | None\n\ndefault `= None`\n\nto make app accessible on local network, set this to \"0.0.0.0\". Can be set by\nenvironment variable GRADIO_SERVER_NAME. If None, will use \"127.0.0.1\".\n\n\n    \n    \n    server_port: int | None\n\ndefault `= None`\n\nwill start gradio app on this port (if available). Can be set by environment\nvariable GRADIO_SERVER_PORT. If None, will search for an available port\nstarting at 7860.\n\n\n    \n    \n    height: int\n\ndefault `= 500`\n\nThe height in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    width: int | str\n\ndefault `= \"100%\"`\n\nThe width in pixels of the iframe element containing the gradio app (used if\ninline=True)\n\n\n    \n    \n    favicon_path: str | Path | None\n\ndefault `= None`\n\nIf a path to a file (.png, .gif, or .ico) is provided, it will be used as the\nfavicon for the web page.\n\n\n    \n    \n    ssl_keyfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the private key file to\ncreate a local server running on https.\n\n\n    \n    \n    ssl_certfile: str | None\n\ndefault `= None`\n\nIf a path to a file is provided, will use this as the signed certificate for\nhttps. Needs to be provided if ssl_keyfile is provided.\n\n\n    \n    \n    ssl_keyfile_password: str | None\n\ndefault `= None`\n\nIf a password is provided, will use this with the ssl certificate for https.\n\n\n    \n    \n    ssl_verify: bool\n\ndefault `= True`\n\nIf False, skips certificate validation which allows self-signed certificates\nto be used.\n\n\n    \n    \n    quiet: bool\n\ndefault `= False`\n\nIf True, suppresses most print statements.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_pa", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "t `= False`\n\nIf True, suppresses most print statements.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nIf True, shows the api docs in the footer of the app. Default True.\n\n\n    \n    \n    allowed_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is allowed to\nserve. Must be absolute paths. Warning: if you provide directories, any files\nin these directories or their subdirectories are accessible to all users of\nyour app. Can be set by comma separated environment variable\nGRADIO_ALLOWED_PATHS. These files are generally assumed to be secure and will\nbe displayed in the browser when possible.\n\n\n    \n    \n    blocked_paths: list[str] | None\n\ndefault `= None`\n\nList of complete filepaths or parent directories that gradio is not allowed to\nserve (i.e. users of your app are not allowed to access). Must be absolute\npaths. Warning: takes precedence over `allowed_paths` and all other\ndirectories exposed by Gradio by default. Can be set by comma separated\nenvironment variable GRADIO_BLOCKED_PATHS.\n\n\n    \n    \n    root_path: str | None\n\ndefault `= None`\n\nThe root path (or \"mount point\") of the application, if it's not served from\nthe root (\"/\") of the domain. Often used when the application is behind a\nreverse proxy that forwards requests to the application. For example, if the\napplication is served at \"https://example.com/myapp\", the `root_path` should\nbe set to \"/myapp\". A full URL beginning with http:// or https:// can be\nprovided, which will be used as the root path in its entirety. Can be set by\nenvironment variable GRADIO_ROOT_PATH. Defaults to \"\".\n\n\n    \n    \n    app_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nAdditional keyword arguments to pass to the underlying FastAPI app as a\ndictionary of parameter keys and argument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "ument values. For example, `{\"docs_url\":\n\"/docs\"}`\n\n\n    \n    \n    state_session_capacity: int\n\ndefault `= 10000`\n\nThe maximum number of sessions whose information to store in memory. If the\nnumber of sessions exceeds this number, the oldest sessions will be removed.\nReduce capacity to reduce memory usage when using gradio.State or returning\nupdated components from functions. Defaults to 10000.\n\n\n    \n    \n    share_server_address: str | None\n\ndefault `= None`\n\nUse this to specify a custom FRP server and port for sharing Gradio apps (only\napplies if share=True). If not provided, will use the default FRP server at\nhttps://gradio.live. See https://github.com/huggingface/frp for more\ninformation.\n\n\n    \n    \n    share_server_protocol: Literal['http', 'https'] | None\n\ndefault `= None`\n\nUse this to specify the protocol to use for the share links. Defaults to\n\"https\", unless a custom share_server_address is provided, in which case it\ndefaults to \"http\". If you are using a custom share_server_address and want to\nuse https, you must set this to \"https\".\n\n\n    \n    \n    share_server_tls_certificate: str | None\n\ndefault `= None`\n\nThe path to a TLS certificate file to use when connecting to a custom share\nserver. This parameter is not used with the default FRP server at\nhttps://gradio.live. Otherwise, you must provide a valid TLS certificate file\n(e.g. a \"cert.pem\") relative to the current working directory, or the\nconnection will not use TLS encryption, which is insecure.\n\n\n    \n    \n    auth_dependency: Callable[[fastapi.Request], str | None] | None\n\ndefault `= None`\n\nA function that takes a FastAPI request and returns a string user ID or None.\nIf the function returns None for a specific request, that user is not\nauthorized to access the app (they will see a 401 Unauthorized response). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that c", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "ponse). To\nbe used with external authentication systems like OAuth. Cannot be used with\n`auth`.\n\n\n    \n    \n    max_file_size: str | int | None\n\ndefault `= None`\n\nThe maximum file size in bytes that can be uploaded. Can be a string of the\nform \"<value><unit>\", where value is any positive integer and unit is one of\n\"b\", \"kb\", \"mb\", \"gb\", \"tb\". If None, no limit is set.\n\n\n    \n    \n    enable_monitoring: bool | None\n\ndefault `= None`\n\nEnables traffic monitoring of the app through the /monitoring endpoint. By\ndefault is None, which enables this endpoint. If explicitly True, will also\nprint the monitoring URL to the console. If False, will disable monitoring\naltogether.\n\n\n    \n    \n    strict_cors: bool\n\ndefault `= True`\n\nIf True, prevents external domains from making requests to a Gradio server\nrunning on localhost. If False, allows requests to localhost that originate\nfrom localhost but also, crucially, from \"null\". This parameter should\nnormally be True to prevent CSRF attacks but may need to be False when\nembedding a *locally-running Gradio app* using web components.\n\n\n    \n    \n    node_server_name: str | None\n\ndefault `= None`\n\n\n    \n    \n    node_port: int | None\n\ndefault `= None`\n\n\n    \n    \n    ssr_mode: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be rendered using server-side rendering mode,\nwhich is typically more performant and provides better SEO, but this requires\nNode 20+ to be installed on the system. If False, the app will be rendered\nusing client-side rendering mode. If None, will use GRADIO_SSR_MODE\nenvironment variable or default to False.\n\n\n    \n    \n    pwa: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an installable PWA (Progressive Web\nApp). If set to None (default behavior), then the PWA feature will be enabled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfun", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "abled\nif this Gradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    mcp_server: bool | None\n\ndefault `= None`\n\nIf True, the Gradio app will be set up as an MCP server and documented\nfunctions will be added as MCP tools. If None (default behavior), then the\nGRADIO_MCP_SERVER environment variable will be used to determine if the MCP\nserver should be enabled (which is \"True\" on Hugging Face Spaces).\n\n\n    \n    \n    i18n: I18n | None\n\ndefault `= None`\n\nAn I18n instance containing custom translations, which are used to translate\nstrings in our components (e.g. the labels of components or Markdown strings).\nThis feature can only be used to translate static text in the frontend, not\nvalues in the backend.\n\n", "heading1": "launch", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.queue(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20-", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nBy enabling the queue you can control when users know their position in the\nqueue, and set a limit on maximum number of events allowed.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%2", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    with gr.Blocks() as demo:\n        button = gr.Button(label=\"Generate Image\")\n        button.click(fn=image_generator, inputs=gr.Textbox(), outputs=gr.Image())\n    demo.queue(max_size=10)\n    demo.launch()\n\nParameters \u25bc\n\n\n    \n    \n    status_update_rate: float | Literal['auto']\n\ndefault `= \"auto\"`\n\nIf \"auto\", Queue will send status estimations to all clients whenever a job is\nfinished. Otherwise Queue will send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open:", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": " will send status estimations to all clients whenever a job is\nfinished. Otherwise Queue will send status at regular intervals set by this\nparameter as the number of seconds.\n\n\n    \n    \n    api_open: bool | None\n\ndefault `= None`\n\nIf True, the REST routes of the backend will be open, allowing requests made\ndirectly to those endpoints to skip the queue.\n\n\n    \n    \n    max_size: int | None\n\ndefault `= None`\n\nThe maximum number of events the queue will store at any given moment. If the\nqueue is full, new events will not be added and a user will receive a message\nsaying that the queue is full. If None, the queue size will be unlimited.\n\n\n    \n    \n    default_concurrency_limit: int | None | Literal['not_set']\n\ndefault `= \"not_set\"`\n\nThe default value of `concurrency_limit` to use for event listeners that don't\nspecify a value. Can be set by environment variable\nGRADIO_DEFAULT_CONCURRENCY_LIMIT. Defaults to 1 if not set otherwise.\n\n", "heading1": "queue", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.integrate(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nA catch-all method for integrating with other libraries. This method should be\nrun after launch()\n\nParameters \u25bc\n\n\n    \n    \n    comet_ml: <class 'inspect._empty'>\n\ndefault `= None`\n\nIf a comet_ml Experiment object is provided, will integrate with the\nexperiment and appear on Comet dashboard\n\n\n    \n    \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n\nIf", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "  \n    wandb: ModuleType | None\n\ndefault `= None`\n\nIf the wandb module is provided, will integrate with it and appear on WandB\ndashboard\n\n\n    \n    \n    mlflow: ModuleType | None\n\ndefault `= None`\n\nIf the mlflow module is provided, will integrate with the experiment and\nappear on ML Flow dashboard\n\n", "heading1": "integrate", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.load(block, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20In", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Blocks initially loads in the browser.\n\nParameters \u25bc\n\n\n    \n    \n    block: Block | None\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to o", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "ction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime displa", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. F", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "r events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will au", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "load", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Blocks.unload(fn, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc", "heading1": "unload", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the user closes or refreshes the tab, ending\nthe user session. It is useful for cleaning up resources when the app is\nclosed.\n\nExample Usage\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%2", "heading1": "unload", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    import gradio as gr\n    with gr.Blocks() as demo:\n        gr.Markdown(\"When you close the tab, hello will be printed to the console\")\n        demo.unload(lambda: print(\"hello\"))\n    demo.launch()\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable[..., Any]\n\nCallable function to run to clear resources. The function should not take any\narguments and the output is not used.\n\n", "heading1": "unload", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "ources. The function should not take any\narguments and the output is not used.\n\n", "heading1": "unload", "source_page_url": "https://gradio.app/docs/gradio/blocks", "source_page_title": "Gradio - Blocks Docs"}, {"text": "Component to select a date and (optionally) a time.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "**As input component** : Passes text value as a `str` into the function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: float | datetime | str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a tuple pair of datetimes.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> float | datetime | str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: float | str | datetime | None\n\ndefault `= None`\n\ndefault value for datetime.\n\n\n    \n    \n    include_time: bool\n\ndefault `= True`\n\nIf True, the component will include time selection. If False, only date\nselection will be available.\n\n\n    \n    \n    type: Literal['timestamp', 'datetime', 'string']\n\ndefault `= \"timestamp\"`\n\nThe type of the value. Can be \"timestamp\", \"datetime\", or \"string\". If\n\"timestamp\", the value will be a number representing the start and end date in\nseconds since epoch. If \"datetime\", the value will be a datetime object. If\n\"string\", the value will be the date entered by the user.\n\n\n    \n    \n    timezone: str | None\n\ndefault `= None`\n\nThe timezone to use for timestamps, such as \"US/Pacific\" or \"Europe/Paris\". If\nNone, the timezone will be the local timezone.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: float | None\n\ndefault `= None`\n\nIf `value` is a callable, run the function 'every' number of seconds while the\nclient connection is open. Has no effect otherwise. The event can be accessed\n(e.g. to cancel it) via this component's .load_event attribute.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin B", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "nents. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.DateTime` | \"datetime\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe DateTime component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`DateTime.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the DateTime changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`DateTime.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the DateTime is focused.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, Non", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "nction returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that i", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "eue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js meth", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/datetime", "source_page_title": "Gradio - Datetime Docs"}, {"text": "When gr.EventData or one of its subclasses is added as a type hint to an\nargument of a prediction function, a gr.EventData object will automatically be\npassed as the value of that argument. The attributes of this object contains\ninformation about the event that triggered the listener. The gr.EventData\nobject itself contains a `.target` attribute that refers to the component that\ntriggered the event, while subclasses of gr.EventData contains additional\nattributes that are different for each class.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "import gradio as gr\n    \n    with gr.Blocks() as demo:\n        table = gr.Dataframe([[1, 2, 3], [4, 5, 6]])\n        gallery = gr.Gallery([(\"cat.jpg\", \"Cat\"), (\"dog.jpg\", \"Dog\")])\n        textbox = gr.Textbox(\"Hello World!\")\n        statement = gr.Textbox()\n    \n        def on_select(value, evt: gr.EventData):\n            return f\"The {evt.target} component was selected, and its value was {value}.\"\n    \n        table.select(on_select, table, statement)\n        gallery.select(on_select, gallery, statement)\n        textbox.select(on_select, textbox, statement)\n    \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    target: Block | None\n\nThe component object that triggered the event. Can be used to distinguish\nmultiple components bound to the same listener.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "gallery_selectionstictactoe\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np with gr.Blocks() as demo:\nimgs = gr.State() gallery = gr.Gallery(allow_preview=False) def\ndeselect_images(): return gr.Gallery(selected_index=None) def\ngenerate_images(): images = [] for _ in range(9): image = np.ones((100, 100,\n3), dtype=np.uint8) * np.random.randint( 0, 255, 3 ) image is a solid single\ncolor images.append(image) return images, images demo.load(generate_images,\nNone, [gallery, imgs]) with gr.Row(): selected = gr.Number(show_label=False)\ndarken_btn = gr.Button(\"Darken selected\") deselect_button =\ngr.Button(\"Deselect\") deselect_button.click(deselect_images, None, gallery)\ndef get_select_index(evt: gr.SelectData): return evt.index\ngallery.select(get_select_index, None, selected) def darken_img(imgs, index):\nindex = int(index) imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\nreturn imgs, imgs darken_btn.click(darken_img, [imgs, selected], [imgs,\ngallery]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    \n    with gr.Blocks() as demo:\n        imgs = gr.State()\n        gallery = gr.Gallery(allow_preview=False)\n    \n        def deselect_images():\n            return gr.Gallery(selected_index=None)\n    \n        def generate_images():\n            images = []\n            for _ in range(9):\n                image = np.ones((100, 100, 3), dtype=np.uint8) * np.random.randint(\n                    0, 255, 3\n                )  image is a solid single color\n                images.append(image)\n            return images, images\n    \n        demo.load(generate_images, None, [gallery, imgs])\n    \n        with gr.Row():\n            selected = gr.Number(show_label=False)\n            darken_btn = gr.Button(\"Darken selected\")\n        deselect_button = gr.Button(\"Deselect\")\n    \n        deselect_button.click(deselect_images, None, gallery)\n    \n        def get_select_index(evt: gr.SelectData):\n            return evt.index\n    \n  ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "  deselect_button = gr.Button(\"Deselect\")\n    \n        deselect_button.click(deselect_images, None, gallery)\n    \n        def get_select_index(evt: gr.SelectData):\n            return evt.index\n    \n        gallery.select(get_select_index, None, selected)\n    \n        def darken_img(imgs, index):\n            index = int(index)\n            imgs[index] = np.round(imgs[index] * 0.8).astype(np.uint8)\n            return imgs, imgs\n    \n        darken_btn.click(darken_img, [imgs, selected], [imgs, gallery])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr with gr.Blocks() as demo: turn =\ngr.Textbox(\"X\", interactive=False, label=\"Turn\") board =\ngr.Dataframe(value=[[\"\", \"\", \"\"]] * 3, interactive=False, type=\"array\") def\nplace(board: list[list[int]], turn, evt: gr.SelectData): if evt.value: return\nboard, turn board[evt.index[0]][evt.index[1]] = turn turn = \"O\" if turn == \"X\"\nelse \"X\" return board, turn board.select(place, [board, turn], [board, turn],\nshow_progress=\"hidden\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    with gr.Blocks() as demo:\n        turn = gr.Textbox(\"X\", interactive=False, label=\"Turn\")\n        board = gr.Dataframe(value=[[\"\", \"\", \"\"]] * 3, interactive=False, type=\"array\")\n    \n        def place(board: list[list[int]], turn, evt: gr.SelectData):  \n            if evt.value:\n                return board, turn\n            board[evt.index[0]][evt.index[1]] = turn\n            turn = \"O\" if turn == \"X\" else \"X\"\n            return board, turn\n    \n        board.select(place, [board, turn], [board, turn], show_progress=\"hidden\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/eventdata", "source_page_title": "Gradio - Eventdata Docs"}, {"text": "Creates a code editor for viewing code (as an output component), or for\nentering and editing code (as an input component).\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "**As input component** : Passes the code entered as a `str`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` of code.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> tuple[str] | str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Callable | None\n\ndefault `= None`\n\nDefault value to show in the code editor. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    language: Literal['python', 'c', 'cpp', 'markdown', 'latex', 'json', 'html', 'css', 'javascript', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-msSQL', 'sql-mySQL', 'sql-mariaDB', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgSQL', 'sql-gql', 'sql-gpSQL', 'sql-sparkSQL', 'sql-esper'] | None\n\ndefault `= None`\n\nThe language to display the code as. Supported languages listed in\n`gr.Code.languages`.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    lines: int\n\ndefault `= 5`\n\nMinimum number of visible lines to show in the code editor.\n\n\n    \n    \n    max_lines: int | None\n\ndefault `= None`\n\nMaximum number of visible lines to show in the code editor. Defaults to None\nand will fill the height of the container.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nWhether user should be able to enter code or only view it.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= No", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "\ncomponent is assigned to.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nWhether user should be able to enter code or only view it.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": " treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    wrap_lines: bool\n\ndefault `= False`\n\nIf True, will wrap lines to the width of the container when overflow occurs.\nDefaults to False.\n\n\n    \n    \n    show_line_numbers: bool\n\ndefault `= True`\n\nIf True, displays line numbers, and if False, hides line numbers.\n\n\n    \n    \n    autocomplete: bool\n\ndefault `= False`\n\nIf True, will show autocomplete suggestions for supported languages. Defaults\nto False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Code` | \"code\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Code component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`Code.languages(fn, \u00b7\u00b7\u00b7)` | ['python', 'c', 'cpp', 'markdown', 'latex', 'json', 'html', 'css', 'javascript', 'jinja2', 'typescript', 'yaml', 'dockerfile', 'shell', 'r', 'sql', 'sql-msSQL', 'sql-mySQL', 'sql-mariaDB', 'sql-sqlite', 'sql-cassandra', 'sql-plSQL', 'sql-hive', 'sql-pgSQL', 'sql-gql', 'sql-gpSQL', 'sql-sparkSQL', 'sql-esper', None]  \n`Code.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Code changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Code.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Code.  \n`Code.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Code is focused.  \n`Code.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Code is unfocused/blurred.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/code", "source_page_title": "Gradio - Code Docs"}, {"text": "Creates an image component that can be used to upload images (as an input)\nor display images (as an output).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "**As input component** : Passes the uploaded image as a `numpy.array`,\n`PIL.Image` or `str` filepath depending on `type`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: np.ndarray | PIL.Image.Image | str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `numpy.array`, `PIL.Image`, or `str` or\n`pathlib.Path` filepath to an image which is displayed.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> np.ndarray | PIL.Image.Image | str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | PIL.Image.Image | np.ndarray | Callable | None\n\ndefault `= None`\n\nA PIL Image, numpy array, path or URL for the default value that Image\ncomponent is going to take. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFile format (e.g. \"png\" or \"gif\"). Used to save image if it does not already\nhave a valid format (e.g. if the image is being returned to the frontend as a\nnumpy array or PIL Image). The format should be supported by the PIL library.\nApplies both when this component is used as an input or output. This parameter\nhas no effect on SVG files.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfile or numpy array, but will affect the displayed image.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed image\nfile or numpy array, but will affect the displayed image.\n\n\n    \n    \n    image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None\n\ndefault `= \"RGB\"`\n\nThe pixel format and color depth that the image should be loaded and\npreprocessed as. \"RGB\" will load the image as a color image, or \"L\" as black-\nand-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html\nfor other supported image modes and their meaning. This parameter has no\neffect on SVG or GIF files. If set to None, the image_mode will be inferred\nfrom the image file type (e.g. \"RGBA\" for a .png image, \"RGB\" in most other\ncases).\n\n\n    \n    \n    sources: list[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None\n\ndefault `= None`\n\nLi", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": ".g. \"RGBA\" for a .png image, \"RGB\" in most other\ncases).\n\n\n    \n    \n    sources: list[Literal['upload', 'webcam', 'clipboard']] | Literal['upload', 'webcam', 'clipboard'] | None\n\ndefault `= None`\n\nList of sources for the image. \"upload\" creates a box where user can drop an\nimage file, \"webcam\" allows user to take snapshot from their webcam,\n\"clipboard\" allows users to paste an image from the clipboard. If None,\ndefaults to [\"upload\", \"webcam\", \"clipboard\"] if streaming is False, otherwise\ndefaults to [\"webcam\"].\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe format the image is converted before being passed into the prediction\nfunction. \"numpy\" converts the image to a numpy array with shape (height,\nwidth, 3) and values from 0 to 255, \"pil\" converts the image to a PIL image\nobject, \"filepath\" passes a str path to a temporary file containing the image.\nTo support animated GIFs in input, the `type` should be set to \"filepath\" or\n\"pil\". To support SVGs, the `type` should be set to \"filepath\".\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_d", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_download_button: bool\n\ndefault `= True`\n\nIf True, will display button to download image. Only applies if interactive is\nFalse (e.g. if the component is used as an output).\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload and edit an image; if False, can only be\nused to display images. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    streaming: bool\n\ndefault `= False`\n\nIf True when used in a `live` interface, will automatically stream webcam\nfeed. Only valid is source is 'webcam'. If the component is an output\ncomponent, will automatically convert images to base64.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as th", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": " id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    mirror_webcam: bool | None\n\ndefault `= None`\n\nIf True webcam will be mirrored. Default is True.\n\n\n    \n    \n    webcam_options: WebcamOptions | None\n\ndefault `= None`\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\nCustom text for the upload area. Overrides default upload messages when\nprovided. Accepts new lines and `` to designate a heading.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a fullscreen i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ea. Overrides default upload messages when\nprovided. Accepts new lines and `` to designate a heading.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a fullscreen icon in the corner of the component that\nallows user to view the image in fullscreen mode. If False, icon does not\nappear.\n\n\n    \n    \n    webcam_constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary that allows developers to specify custom media constraints for\nthe webcam stream. This parameter provides flexibility to control the video\nstream's properties, such as resolution and front or rear camera on mobile\ndevices. See\n[demo/webcam_constraints](https://gradio.app/playground?demo=Blank&code=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", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "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&reqs=b3BlbmN2LXB5dGhvbg%3D%3D)\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Image` | \"image\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "The `gr.Image` component can process or display any image format that is\n[supported by the PIL\nlibrary](https://pillow.readthedocs.io/en/stable/handbook/image-file-\nformats.html), including animated GIFs. In addition, it also supports the SVG\nimage format.\n\nWhen the `gr.Image` component is used as an input component, the image is\nconverted into a `str` filepath, a `PIL.Image` object, or a `numpy.array`,\ndepending on the `type` parameter. However, animated GIF and SVG images are\ntreated differently:\n\n  * Animated `GIF` images can only be converted to `str` filepaths or `PIL.Image` objects. If they are converted to a `numpy.array` (which is the default behavior), only the first frame will be used. So if your demo expects an input `GIF` image, make sure to set the `type` parameter accordingly, e.g.\n\n    \n    \n    import gradio as gr\n    \n    demo = gr.Interface(\n        fn=lambda x:x, \n        inputs=gr.Image(type=\"filepath\"), \n        outputs=gr.Image()\n    )\n        \n    demo.launch()\n\n  * For `SVG` images, the `type` parameter is ignored altogether and the image is always returned as an image filepath. This is because `SVG` images cannot be processed as `PIL.Image` or `numpy.array` objects.\n\n", "heading1": "`GIF` and `SVG` Image Formats", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "sepia_filterfake_diffusion\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr def sepia(input_img):\nsepia_filter = np.array([ [0.393, 0.769, 0.189], [0.349, 0.686, 0.168],\n[0.272, 0.534, 0.131] ]) sepia_img = input_img.dot(sepia_filter.T) sepia_img\n/= sepia_img.max() return sepia_img demo = gr.Interface(sepia, gr.Image(),\n\"image\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    import gradio as gr\n    \n    def sepia(input_img):\n        sepia_filter = np.array([\n            [0.393, 0.769, 0.189],\n            [0.349, 0.686, 0.168],\n            [0.272, 0.534, 0.131]\n        ])\n        sepia_img = input_img.dot(sepia_filter.T)\n        sepia_img /= sepia_img.max()\n        return sepia_img\n    \n    demo = gr.Interface(sepia, gr.Image(), \"image\")\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np import time def\nfake_diffusion(steps): rng = np.random.default_rng() for i in range(steps):\ntime.sleep(1) image = rng.random(size=(600, 600, 3)) yield image image =\nnp.ones((1000,1000,3), np.uint8) image[:] = [255, 124, 0] yield image demo =\ngr.Interface(fake_diffusion, inputs=gr.Slider(1, 10, 3, step=1),\noutputs=\"image\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    import time\n    \n    def fake_diffusion(steps):\n        rng = np.random.default_rng()\n        for i in range(steps):\n            time.sleep(1)\n            image = rng.random(size=(600, 600, 3))\n            yield image\n        image = np.ones((1000,1000,3), np.uint8)\n        image[:] = [255, 124, 0]\n        yield image\n    \n    demo = gr.Interface(fake_diffusion,\n                        inputs=gr.Slider(1, 10, 3, step=1),\n                        outputs=\"image\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Image component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Image.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Image using the clear button for the component.  \n`Image.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Image changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Image.stream(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user streams the Image.  \n`Image.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Image. Uses event data gradio.SelectData to carry `value` referring to the label of the Image, and `selected` to refer to state of the Image. See EventData documentation on how to use this event data  \n`Image.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Image.  \n`Image.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Image.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockCo", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ion should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | lis", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ent area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctio", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ill cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n   ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "ll still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "gradio.WebcamOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the webcam tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `webcam_options`\nparameter of `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    mirror: bool\n\ndefault `= True`\n\nIf True, the webcam will be mirrored.\n\n\n    \n    \n    constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary of constraints for the webcam.\n\n", "heading1": "Webcam Options", "source_page_url": "https://gradio.app/docs/gradio/image", "source_page_title": "Gradio - Image Docs"}, {"text": "Creates a checkbox that can be set to `True` or `False`. Can be used as an\ninput to pass a boolean value to a function or as an output to display a\nboolean value.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "**As input component** : Passes the status of the checkbox as a `bool`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: bool | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `bool` value that is set as the status\nof the checkbox\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> bool | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: bool | Callable\n\ndefault `= False`\n\nif True, checked by default. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, this checkbox can be checked; if False, checking will be disabled. If\nnot provided, this is inferred based on whether the component is used as an\ninput or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "n event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Checkbox` | \"checkbox\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "sentence_builderhello_world_3\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name, is_morning, temperature):\nsalutation = \"Good morning\" if is_morning else \"Good evening\" greeting =\nf\"{salutation} {name}. It is {temperature} degrees today\" celsius =\n(temperature - 32) * 5 / 9 return greeting, round(celsius, 2) demo =\ngr.Interface( fn=greet, inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\noutputs=[\"text\", \"number\"], ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def greet(name, is_morning, temperature):\n        salutation = \"Good morning\" if is_morning else \"Good evening\"\n        greeting = f\"{salutation} {name}. It is {temperature} degrees today\"\n        celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n        outp", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "    celsius = (temperature - 32) * 5 / 9\n        return greeting, round(celsius, 2)\n    \n    demo = gr.Interface(\n        fn=greet,\n        inputs=[\"text\", \"checkbox\", gr.Slider(0, 100)],\n        outputs=[\"text\", \"number\"],\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Checkbox component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Checkbox.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Checkbox changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Checkbox.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Checkbox.  \n`Checkbox.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Checkbox. Uses event data gradio.SelectData to carry `value` referring to the label of the Checkbox, and `selected` to refer to state of the Checkbox. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] |", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "ts to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue h", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "ess animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "iteral['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event a", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "ult `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/checkbox", "source_page_title": "Gradio - Checkbox Docs"}, {"text": "Creates a video component that can be used to upload/record videos (as an\ninput) or display videos (as an output). For the video to be playable in the\nbrowser it must have a compatible container and codec combination. Allowed\ncombinations are .mp4 with h264 codec, .ogg with theora codec, and .webm with\nvp9 codec. If the component detects that the output video would not be\nplayable in the browser it will attempt to convert it to a playable mp4 video.\nIf the conversion fails, the original video is returned.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "**As input component** : Passes the uploaded video as a `str` filepath or\nURL whose extension can be modified by `format`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` or `pathlib.Path` filepath to a video which is displayed, or a `Tuple[str | pathlib.Path, str | pathlib.Path | None]` where the first element is a filepath to a video and the second element is an optional filepath to a subtitle file.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | tuple[str | Path, str | Path | None] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Path | tuple[str | Path, str | Path | None] | Callable | None\n\ndefault `= None`\n\npath or URL for the default value that Video component is going to take. Can\nalso be a tuple consisting of (video filepath, subtitle filepath). If a\nsubtitle file is provided, it should be of type .srt or .vtt. Or can be\ncallable, in which case the function will be called whenever the app loads to\nset the initial value of the component.\n\n\n    \n    \n    format: str | None\n\ndefault `= None`\n\nthe file extension with which to save video, such as 'avi' or 'mp4'. This\nparameter applies both when this component is used as an input to determine\nwhich file format to convert user-provided video to, and when this component\nis used as an output to determine the format of video returned to the user. If\nNone, no file format conversion is done and the video is kept as is. Use 'mp4'\nto ensure browser playability.\n\n\n    \n    \n    sources: list[Literal['upload', 'webcam']] | Literal['upload', 'webcam'] | None\n\ndefault `= None`\n\nlist of sources permitted for video. \"upload\" creates a box where user can\ndrop a video file, \"webcam\" allows user to record a video from their webcam.\nIf None, defaults to both [\"upload, \"webcam\"].\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed video\nfile, but will affect the displayed video.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed video\nfile, but will affect the displayed video.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interf", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "| None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload a video; if False, can only be used to\ndisplay videos. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    elem_id: str | No", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "d, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    mirror_webcam: bool | None\n\ndefault `= None`\n\n\n    \n    \n    webcam_options: WebcamOptions | None\n\ndefault `= None`\n\nA `gr.WebcamOptions` instance that allows developers to specify custom media\nconstraints for the webcam stream. This parameter provides flexibility to\ncontrol the video stream's properties, such as resolution and front or rear\ncamera on mobile devices. See\n[demo/webcam_constraints](https://gradio.app/playground?demo=Blank&code=aW1wb3J0IGdyYWRpbyBhcyBncgppbXBvcnQgY3YyCgpkZWYgZ2V0X3ZpZGVvX3NoYXBlKHZpZGVvKToKICAgIGNhcCA9IG", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "ear\ncamera on mobile devices. See\n[demo/webcam_constraints](https://gradio.app/playground?demo=Blank&code=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&reqs=b3BlbmN2LXB5dGhvbg%3D%3D)\n\n\n    \n    \n    include_audio: bool | None\n\ndefault `= None`\n\nwhether the component should record/retain the audio track for a video. By\ndefault, audio is excluded for webcam videos and included for uploaded videos.\n\n\n    \n    \n    autoplay: bool\n\ndefaul", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": " None`\n\nwhether the component should record/retain the audio track for a video. By\ndefault, audio is excluded for webcam videos and included for uploaded videos.\n\n\n    \n    \n    autoplay: bool\n\ndefault `= False`\n\nwhether to automatically play the video when the component is used as an\noutput. Note: browsers will not autoplay video files if the user has not\ninteracted with the page yet.\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nif True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    show_download_button: bool | None\n\ndefault `= None`\n\nif True, will show a download icon in the corner of the component that allows\nuser to download the output. If False, icon does not appear. By default, it\nwill be True for output components and False for input components.\n\n\n    \n    \n    min_length: int | None\n\ndefault `= None`\n\nthe minimum length of video (in seconds) that the user can pass into the\nprediction function. If None, there is no minimum length.\n\n\n    \n    \n    max_length: int | None\n\ndefault `= None`\n\nthe maximum length of video (in seconds) that the user can pass into the\nprediction function. If None, there is no maximum length.\n\n\n    \n    \n    loop: bool\n\ndefault `= False`\n\nif True, the video will loop when it reaches the end and continue playing from\nthe beginning.\n\n\n    \n    \n    streaming: bool\n\ndefault `= False`\n\nwhen used set as an output, takes video chunks yielded from the backend and\ncombines them into one streaming video output. Each chunk should be a video\nfile with a .ts extension using an h.264 encoding. Mp4 files are also accepted\nbut they will be converted to h.264 encoding.\n\n\n    \n    \n    watermark: str | Path | None\n\ndefault `= None`\n\nan image file to be included as a watermark on the video. Th", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": ". Mp4 files are also accepted\nbut they will be converted to h.264 encoding.\n\n\n    \n    \n    watermark: str | Path | None\n\ndefault `= None`\n\nan image file to be included as a watermark on the video. The image is not\nscaled and is displayed on the bottom right of the video. Valid formats for\nthe image are: jpeg, png.\n\n\n    \n    \n    webcam_constraints: dict[str, Any] | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Video` | \"video\" | Uses default values  \n`gradio.PlayableVideo` | \"playablevideo\" | Uses format=\"mp4\"  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "video_identity_2\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def video_identity(video): return video demo =\ngr.Interface(video_identity, gr.Video(), \"playable_video\", ) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def video_identity(video):\n        return video\n    \n    demo = gr.Interface(video_identity,\n                        gr.Video(),\n                        \"playable_video\",\n                        )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Video component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Video.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Video changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Video.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Video using the clear button for the component.  \n`Video.start_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user starts recording with the Video.  \n`Video.stop_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user stops recording with the Video.  \n`Video.stop(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the Video.  \n`Video.play(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user plays the media in the Video.  \n`Video.pause(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the media in the Video stops for any reason.  \n`Video.end(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the Video.  \n`Video.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Video.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": " function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well a", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "w_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the cli", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "ancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow dow", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "ther to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "gradio.WebcamOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the webcam tool in the ImageEditor\ncomponent. An instance of this class can be passed to the `webcam_options`\nparameter of `gr.ImageEditor`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    mirror: bool\n\ndefault `= True`\n\nIf True, the webcam will be mirrored.\n\n\n    \n    \n    constraints: dict[str, Any] | None\n\ndefault `= None`\n\nA dictionary of constraints for the webcam.\n\n", "heading1": "Webcam Options", "source_page_url": "https://gradio.app/docs/gradio/video", "source_page_title": "Gradio - Video Docs"}, {"text": "The FileData class is a subclass of the GradioModel class that represents a\nfile object within a Gradio interface. It is used to store file data and\nmetadata when a file is uploaded.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/filedata", "source_page_title": "Gradio - Filedata Docs"}, {"text": "from gradio_client import Client, FileData, handle_file\n    \n    def get_url_on_server(data: FileData):\n        print(data['url'])\n    \n    client = Client(\"gradio/gif_maker_main\", download_files=False)\n    job = client.submit([handle_file(\"./cheetah.jpg\")], api_name=\"/predict\")\n    data = job.result()\n    video: FileData = data['video']\n    \n    get_url_on_server(video)\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/filedata", "source_page_title": "Gradio - Filedata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    path: str\n\nThe server file path where the file is stored.\n\n\n    \n    \n    url: Optional[str]\n\nThe normalized server URL pointing to the file.\n\n\n    \n    \n    size: Optional[int]\n\nThe size of the file in bytes.\n\n\n    \n    \n    orig_name: Optional[str]\n\nThe original filename before upload.\n\n\n    \n    \n    mime_type: Optional[str]\n\nThe MIME type of the file.\n\n\n    \n    \n    is_stream: bool\n\nIndicates whether the file is a stream.\n\n\n    \n    \n    meta: dict\n\nAdditional metadata used internally (should not be changed).\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/filedata", "source_page_title": "Gradio - Filedata Docs"}, {"text": "Displays text that contains spans that are highlighted by category or\nnumerical value.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "**As input component** : Passes the value as a list of tuples as a `list[tuple]` into the function. Each `tuple` consists of a `str` substring of the text (so the entire text is included) and `str | float | None` label, which is the category or confidence of that substring.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: list[tuple[str, str | float | None]] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a list of (word, category) tuples, or a\ndictionary of two keys: \"text\", and \"entities\", which itself is a list of\ndictionaries, each of which have the keys: \"entity\" (or \"entity_group\"),\n\"start\", and \"end\"\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> list[tuple[str, str | float | None]] | dict | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: list[tuple[str, str | float | None]] | dict | Callable | None\n\ndefault `= None`\n\nDefault value to show. If a function is provided, the function will be called\neach time the app loads to set the initial value of this component.\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nA dictionary mapping labels to colors. The colors may be specified as hex\ncodes or by their names. For example: {\"person\": \"red\", \"location\": \"FFEE22\"}\n\n\n    \n    \n    show_legend: bool\n\ndefault `= False`\n\nwhether to show span categories in a separate legend or inline.\n\n\n    \n    \n    show_inline_category: bool\n\ndefault `= True`\n\nIf False, will not display span category label. Only applies if\nshow_legend=False and interactive=False.\n\n\n    \n    \n    combine_adjacent: bool\n\ndefault `= False`\n\nIf True, will merge the labels of adjacent tokens belonging to the same\ncategory.\n\n\n    \n    \n    adjacent_separator: str\n\ndefault `= \"\"`\n\nSpecifies the separator to be used between tokens if combine_adjacent is True.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    con", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constru", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, the component will be editable, and allow user to select spans of\ntext and label them.\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True, will display the text in right-to-left direction, and the labels in\nthe legend will also be aligned to the right.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.HighlightedText` | \"highlightedtext\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "diff_texts\n\nOpen in \ud83c\udfa2 \u2197 from difflib import Differ import gradio as gr def\ndiff_texts(text1, text2): d = Differ() return [ (token[2:], token[0] if\ntoken[0] != \" \" else None) for token in d.compare(text1, text2) ] demo =\ngr.Interface( diff_texts, [ gr.Textbox( label=\"Text 1\", info=\"Initial text\",\nlines=3, value=\"The quick brown fox jumped over the lazy dogs.\", ),\ngr.Textbox( label=\"Text 2\", info=\"Text to compare\", lines=3, value=\"The fast\nbrown fox jumps over lazy dogs.\", ), ], gr.HighlightedText( label=\"Diff\",\ncombine_adjacent=True, show_legend=True, color_map={\"+\": \"red\", \"-\":\n\"green\"}), theme=gr.themes.Base() ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    from difflib import Differ\n    \n    import gradio as gr\n    \n    def diff_texts(text1, text2):\n        d = Differ()\n        return [\n            (token[2:], token[0] if token[0] != \" \" else None)\n            for token in d.compare(text1, text2)\n        ]\n    \n    demo = gr.Interface(\n        diff_texts,\n        [\n            gr.Textbox(\n                label=\"Text 1\",\n                info=\"Initial text\",\n                lines=3,\n                value=\"The quick brown fox jumped over the lazy dogs.\",\n            ),\n            gr.Textbox(\n                label=\"Text 2\",\n                info=\"Text to compare\",\n                lines=3,\n                value=\"The fast brown fox jumps over lazy dogs.\",\n            ),\n        ],\n        gr.HighlightedText(\n            label=\"Diff\",\n            combine_adjacent=True,\n            show_legend=True,\n            color_map={\"+\": \"red\", \"-\": \"green\"}),\n        theme=gr.themes.Base()\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe HighlightedText component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`HighlightedText.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the HighlightedText changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`HighlightedText.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the HighlightedText. Uses event data gradio.SelectData to carry `value` referring to the label of the HighlightedText, and `selected` to refer to state of the HighlightedText. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "s should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "on on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": " `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "| str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/highlightedtext", "source_page_title": "Gradio - Highlightedtext Docs"}, {"text": "The Progress class provides a custom progress tracker that is used in a\nfunction signature. To attach a Progress tracker to a function, simply add a\nparameter right after the input parameters that has a default value set to a\n`gradio.Progress()` instance. The Progress tracker can then be updated in the\nfunction by calling the Progress object or using the `tqdm` method on an\nIterable.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "import gradio as gr\n    import time\n    def my_function(x, progress=gr.Progress()):\n        progress(0, desc=\"Starting...\")\n        time.sleep(1)\n        for i in progress.tqdm(range(100)):\n            time.sleep(0.1)\n        return x\n    gr.Interface(my_function, gr.Textbox(), gr.Textbox()).queue().launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    track_tqdm: bool\n\ndefault `= False`\n\nIf True, the Progress object will track any tqdm.tqdm iterations with the tqdm\nlibrary in the function.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Progress.__call__(progress, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fontic", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nUpdates progress tracker with progress and message text.\n\nParameters \u25bc\n\n\n    \n    \n    progress: float | tuple[int, int | None] | None\n\nIf float, should be between 0 and 1 representing completion. If Tuple, first\nnumber represents steps completed, and second value represents total steps or\nNone if unknown. If None, hides progress bar.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total ", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "\nNone if unknown. If None, hides progress bar.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Progress.tqdm(iterable, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nAttaches progress tracker to iterable, like tqdm.\n\nParameters \u25bc\n\n\n    \n    \n    iterable: Iterable | None\n\niterable to attach progress tracker to.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Progress.__call__(progress, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fontic", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nUpdates progress tracker with progress and message text.\n\nParameters \u25bc\n\n\n    \n    \n    progress: float | tuple[int, int | None] | None\n\nIf float, should be between 0 and 1 representing completion. If Tuple, first\nnumber represents steps completed, and second value represents total steps or\nNone if unknown. If None, hides progress bar.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total ", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "\nNone if unknown. If None, hides progress bar.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "__call__", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Progress.tqdm(iterable, \u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "2'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nAttaches progress tracker to iterable, like tqdm.\n\nParameters \u25bc\n\n\n    \n    \n    iterable: Iterable | None\n\niterable to attach progress tracker to.\n\n\n    \n    \n    desc: str | None\n\ndefault `= None`\n\ndescription to display.\n\n\n    \n    \n    total: int | float | None\n\ndefault `= None`\n\nestimated total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "total number of steps.\n\n\n    \n    \n    unit: str\n\ndefault `= \"steps\"`\n\nunit of iterations.\n\n", "heading1": "tqdm", "source_page_url": "https://gradio.app/docs/gradio/progress", "source_page_title": "Gradio - Progress Docs"}, {"text": "Any code in a `if gr.NO_RELOAD` code-block will not be re-evaluated when\nthe source file is reloaded. This is helpful for importing modules that do not\nlike to be reloaded (tiktoken, numpy) as well as database connections and long\nrunning set up code.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/NO_RELOAD", "source_page_title": "Gradio - No_Reload Docs"}, {"text": "import gradio as gr\n    \n    if gr.NO_RELOAD:\n    \tfrom transformers import pipeline\n    \tpipe = pipeline(\"text-classification\", model=\"cardiffnlp/twitter-roberta-base-sentiment-latest\")\n    \n    gr.Interface.from_pipeline(pipe).launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/NO_RELOAD", "source_page_title": "Gradio - No_Reload Docs"}, {"text": "Creates a scatter plot component to display data from a pandas DataFrame.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "**As input component** : The data to display in a line plot.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: AltairPlotData | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a pandas DataFrame containing the data to\ndisplay in the line plot. The DataFrame should contain at least two columns,\none for the x-axis (corresponding to this component's `x` argument) and one\nfor the y-axis (corresponding to `y`).\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> pd.DataFrame | dict | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: pd.DataFrame | Callable | None\n\ndefault `= None`\n\nThe pandas dataframe containing the data to display in the plot.\n\n\n    \n    \n    x: str | None\n\ndefault `= None`\n\nColumn corresponding to the x axis. Column can be numeric, datetime, or\nstring/category.\n\n\n    \n    \n    y: str | None\n\ndefault `= None`\n\nColumn corresponding to the y axis. Column must be numeric.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nColumn corresponding to series, visualized by color. Column must be\nstring/category.\n\n\n    \n    \n    title: str | None\n\ndefault `= None`\n\nThe title to display on top of the chart.\n\n\n    \n    \n    x_title: str | None\n\ndefault `= None`\n\nThe title given to the x axis. By default, uses the value of the x parameter.\n\n\n    \n    \n    y_title: str | None\n\ndefault `= None`\n\nThe title given to the y axis. By default, uses the value of the y parameter.\n\n\n    \n    \n    color_title: str | None\n\ndefault `= None`\n\nThe title given to the color legend. By default, uses the value of color\nparameter.\n\n\n    \n    \n    x_bin: str | float | None\n\ndefault `= None`\n\nGrouping used to cluster x values. If x column is numeric, should be number to\nbin the x values. If x column is datetime, should be string such as \"1h\",\n\"15m\", \"10s\", using \"s\", \"m\", \"h\", \"d\" suffixes.\n\n\n    \n    \n    y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None\n\ndefault `= None`\n\nAggregation function used to aggregate y values, used if x_bin is provided or\nx is a string/category. Must be one of \"sum\", \"mean\", \"median\", \"min\", \"max\".\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nMapping of series to color names or codes. For example, {\"success\": \"green\",\n\"fail\": \"FF8888\"}.\n\n\n    \n    \n    x_lim: list[float] | None\n\ndefault `= None`\n\nA tuple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "ple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA tuple of list containing the limits for the y-axis, specified as [y_min,\ny_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum to value.\n\n\n    \n    \n    x_label_angle: float\n\ndefault `= 0`\n\nThe angle of the x-axis labels in degrees offset clockwise.\n\n\n    \n    \n    y_label_angle: float\n\ndefault `= 0`\n\nThe angle of the y-axis labels in degrees offset clockwise.\n\n\n    \n    \n    x_axis_labels_visible: bool\n\ndefault `= True`\n\nWhether the x-axis labels should be visible. Can be hidden when many x-axis\nlabels are present.\n\n\n    \n    \n    caption: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) caption to display below the plot.\n\n\n    \n    \n    sort: Literal['x', 'y', '-x', '-y'] | list[str] | None\n\ndefault `= None`\n\nThe sorting order of the x values, if x column is type string/category. Can be\n\"x\", \"y\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n    \n    \n    tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hovering on a point. \"axis\" shows the values for\nthe axis columns, \"all\" shows all column values, and \"none\" shows no tooltips.\nCan also provide a list of strings representing columns to show in the\ntooltip, which will be displayed along with axis values.\n\n\n    \n    \n    height: int | None\n\ndefault `= None`\n\nThe height of the plot in pixels.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) label to display on the top left corner of the plot.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nWhether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Whether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the plot should be visible.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n  ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "default `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True, will show a button to make plot visible in fullscreen mode.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ScatterPlot` | \"scatterplot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "scatter_plot_demo\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd from random import randint, random import\ngradio as gr temp_sensor_data = pd.DataFrame( { \"time\":\npd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200), \"temperature\":\n[randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n\"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in\nrange(200)], \"location\": [\"indoor\", \"outdoor\"] * 100, } ) food_rating_data =\npd.DataFrame( { \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in\nrange(100)], \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n\"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)], \"wait\":\n[random() for i in range(100)], } ) with gr.Blocks() as scatter_plots: with\ngr.Row(): start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\") end =\ngr.DateTime(\"2021-01-05 00:00:00\", label=\"End\") apply_btn = gr.Button(\"Apply\",\nscale=0) with gr.Row(): group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"],\nvalue=\"None\", label=\"Group by\") aggregate = gr.Radio([\"sum\", \"mean\", \"median\",\n\"min\", \"max\"], value=\"sum\", label=\"Aggregation\") temp_by_time =\ngr.ScatterPlot( temp_sensor_data, x=\"time\", y=\"temperature\", )\ntemp_by_time_location = gr.ScatterPlot( temp_sensor_data, x=\"time\",\ny=\"temperature\", color=\"location\", ) time_graphs = [temp_by_time,\ntemp_by_time_location] group_by.change( lambda group:\n[gr.ScatterPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\ngroup_by, time_graphs ) aggregate.change( lambda aggregate:\n[gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs), aggregate,\ntime_graphs ) price_by_cuisine = gr.ScatterPlot( food_rating_data,\nx=\"cuisine\", y=\"price\", ) with gr.Row(): price_by_rating = gr.ScatterPlot(\nfood_rating_data, x=\"rating\", y=\"price\", color=\"wait\",\nshow_actions_button=True, ) price_by_rating_color = gr.ScatterPlot(\nfood_rating_data, x=\"rating\", y=\"price\", color=\"cuisine\", ) if __name__ ==\n\"__main__\": scatter_plots.launch()\n\n    \n    \n    import pandas as pd\n    from random i", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "ice_by_rating_color = gr.ScatterPlot(\nfood_rating_data, x=\"rating\", y=\"price\", color=\"cuisine\", ) if __name__ ==\n\"__main__\": scatter_plots.launch()\n\n    \n    \n    import pandas as pd\n    from random import randint, random\n    import gradio as gr\n    \n    \n    temp_sensor_data = pd.DataFrame(\n        {\n            \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n            \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"location\": [\"indoor\", \"outdoor\"] * 100,\n        }\n    )\n    \n    food_rating_data = pd.DataFrame(\n        {\n            \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n            \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n            \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n            \"wait\": [random() for i in range(100)],\n        }\n    )\n    \n    with gr.Blocks() as scatter_plots:\n        with gr.Row():\n            start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n            end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n            apply_btn = gr.Button(\"Apply\", scale=0)\n        with gr.Row():\n            group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n            aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n    \n        temp_by_time = gr.ScatterPlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n        )\n        temp_by_time_location = gr.ScatterPlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n            color=\"location\",\n        )\n    \n        time_graphs = [temp_by_time, temp_by_time_location]\n        group_by.change(\n            lambda group: [gr.ScatterPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n            group_by", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "me_graphs = [temp_by_time, temp_by_time_location]\n        group_by.change(\n            lambda group: [gr.ScatterPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n            group_by,\n            time_graphs\n        )\n        aggregate.change(\n            lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),\n            aggregate,\n            time_graphs\n        )\n    \n        price_by_cuisine = gr.ScatterPlot(\n            food_rating_data,\n            x=\"cuisine\",\n            y=\"price\",\n        )\n        with gr.Row():\n            price_by_rating = gr.ScatterPlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                color=\"wait\",\n                show_actions_button=True,\n            )\n            price_by_rating_color = gr.ScatterPlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                color=\"cuisine\",\n            )\n    \n    if __name__ == \"__main__\":\n        scatter_plots.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ScatterPlot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ScatterPlot.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the NativePlot. Uses event data gradio.SelectData to carry `value` referring to the label of the NativePlot, and `selected` to refer to state of the NativePlot. See EventData documentation on how to use this event data  \n`ScatterPlot.double_click(fn, \u00b7\u00b7\u00b7)` | Triggered when the NativePlot is double clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the e", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": " be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "e the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input a", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": ") would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/scatterplot", "source_page_title": "Gradio - Scatterplot Docs"}, {"text": "Gradio features a built-in theming engine that lets you customize the look\nand feel of your app. You can choose from a variety of themes, or create your\nown. To do so, pass the `theme=` kwarg to the `Blocks` or `Interface`\nconstructor. For example:\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Soft()) as demo:\n        ...\n\nGradio comes with a set of prebuilt themes which you can load from\n`gr.themes.*`. These are:\n\n  * \u2014 `gr.themes.Base()`\n  * \u2014 `gr.themes.Default()`\n  * \u2014 `gr.themes.Glass()`\n  * \u2014 `gr.themes.Monochrome()`\n  * \u2014 `gr.themes.Soft()`\n\nEach of these themes set values for hundreds of CSS variables. You can use\nprebuilt themes as a starting point for your own custom themes, or you can\ncreate your own themes from scratch. Let\u2019s take a look at each approach.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The easiest way to build a theme is using the Theme Builder. To launch the\nTheme Builder locally, run the following code:\n\n    \n    \n    import gradio as gr\n    \n    gr.themes.builder()\n\nYou can use the Theme Builder running on Spaces above, though it runs much\nfaster when you launch it locally via `gr.themes.builder()`.\n\nAs you edit the values in the Theme Builder, the app will preview updates\nin real time. You can download the code to generate the theme you\u2019ve created\nso you can use it in any Gradio app.\n\nIn the rest of the guide, we will cover building themes programmatically.\n\n", "heading1": "Using the Theme Builder", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Constructor\n\nAlthough each theme has hundreds of CSS variables, the values for most\nthese variables are drawn from 8 core variables which can be set through the\nconstructor of each prebuilt theme. Modifying these 8 arguments allows you to\nquickly change the look and feel of your app.\n\n", "heading1": "Extending Themes via the", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The first 3 constructor arguments set the colors of the theme and are\n`gradio.themes.Color` objects. Internally, these Color objects hold brightness\nvalues for the palette of a single hue, ranging from 50, 100, 200\u2026, 800, 900,\n950. Other CSS variables are derived from these 3 colors.\n\nThe 3 color constructor arguments are:\n\n  * \u2014 `primary_hue`: This is the color draws attention in your theme. In the default theme, this is set to `gradio.themes.colors.orange`.\n  * \u2014 `secondary_hue`: This is the color that is used for secondary elements in your theme. In the default theme, this is set to `gradio.themes.colors.blue`.\n  * \u2014 `neutral_hue`: This is the color that is used for text and other neutral elements in your theme. In the default theme, this is set to `gradio.themes.colors.gray`.\n\nYou could modify these values using their string shortcuts, such as\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(primary_hue=\"red\", secondary_hue=\"pink\")) as demo:\n        ...\n\nor you could use the `Color` objects directly, like this:\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.pink)) as demo:\n        ...\n\nPredefined colors are:\n\n  * \u2014 `slate`\n  * \u2014 `gray`\n  * \u2014 `zinc`\n  * \u2014 `neutral`\n  * \u2014 `stone`\n  * \u2014 `red`\n  * \u2014 `orange`\n  * \u2014 `amber`\n  * \u2014 `yellow`\n  * \u2014 `lime`\n  * \u2014 `green`\n  * \u2014 `emerald`\n  * \u2014 `teal`\n  * \u2014 `cyan`\n  * \u2014 `sky`\n  * \u2014 `blue`\n  * \u2014 `indigo`\n  * \u2014 `violet`\n  * \u2014 `purple`\n  * \u2014 `fuchsia`\n  * \u2014 `pink`\n  * \u2014 `rose`\n\nYou could also create your own custom `Color` objects and pass them in.\n\n", "heading1": "Core Colors", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The next 3 constructor arguments set the sizing of the theme and are\n`gradio.themes.Size` objects. Internally, these Size objects hold pixel size\nvalues that range from `xxs` to `xxl`. Other CSS variables are derived from\nthese 3 sizes.\n\n  * \u2014 `spacing_size`: This sets the padding within and spacing between elements. In the default theme, this is set to `gradio.themes.sizes.spacing_md`.\n  * \u2014 `radius_size`: This sets the roundedness of corners of elements. In the default theme, this is set to `gradio.themes.sizes.radius_md`.\n  * \u2014 `text_size`: This sets the font size of text. In the default theme, this is set to `gradio.themes.sizes.text_md`.\n\nYou could modify these values using their string shortcuts, such as\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(spacing_size=\"sm\", radius_size=\"none\")) as demo:\n        ...\n\nor you could use the `Size` objects directly, like this:\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(spacing_size=gr.themes.sizes.spacing_sm, radius_size=gr.themes.sizes.radius_none)) as demo:\n        ...\n\nThe predefined size objects are:\n\n  * \u2014 `radius_none`\n  * \u2014 `radius_sm`\n  * \u2014 `radius_md`\n  * \u2014 `radius_lg`\n  * \u2014 `spacing_sm`\n  * \u2014 `spacing_md`\n  * \u2014 `spacing_lg`\n  * \u2014 `text_sm`\n  * \u2014 `text_md`\n  * \u2014 `text_lg`\n\nYou could also create your own custom `Size` objects and pass them in.\n\n", "heading1": "Core Sizing", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The final 2 constructor arguments set the fonts of the theme. You can pass\na list of fonts to each of these arguments to specify fallbacks. If you\nprovide a string, it will be loaded as a system font. If you provide a\n`gradio.themes.GoogleFont`, the font will be loaded from Google Fonts.\n\n  * \u2014 `font`: This sets the primary font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"Source Sans Pro\")`.\n  * \u2014 `font_mono`: This sets the monospace font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Mono\")`.\n\nYou could modify these values such as the following:\n\n    \n    \n    with gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont(\"Inconsolata\"), \"Arial\", \"sans-serif\"])) as demo:\n        ...\n\n", "heading1": "Core Fonts", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "You can also modify the values of CSS variables after the theme has been\nloaded. To do so, use the `.set()` method of the theme object to get access to\nthe CSS variables. For example:\n\n    \n    \n    theme = gr.themes.Default(primary_hue=\"blue\").set(\n        loader_color=\"FF0000\",\n        slider_color=\"FF0000\",\n    )\n    \n    with gr.Blocks(theme=theme) as demo:\n        ...\n\nIn the example above, we\u2019ve set the `loader_color` and `slider_color`\nvariables to `FF0000`, despite the overall `primary_color` using the blue\ncolor palette. You can set any CSS variable that is defined in the theme in\nthis manner.\n\nYour IDE type hinting should help you navigate these variables. Since there\nare so many CSS variables, let\u2019s take a look at how these variables are named\nand organized.\n\n", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Conventions\n\nCSS variable names can get quite long, like\n`button_primary_background_fill_hover_dark`! However they follow a common\nnaming convention that makes it easy to understand what they do and to find\nthe variable you\u2019re looking for. Separated by underscores, the variable name\nis made up of:\n\n  * \u2014 1. The target element, such as `button`, `slider`, or `block`.\n  * \u2014 2. The target element type or sub-element, such as `button_primary`, or `block_label`.\n  * \u2014 3. The property, such as `button_primary_background_fill`, or `block_label_border_width`.\n  * \u2014 4. Any relevant state, such as `button_primary_background_fill_hover`.\n  * \u2014 5. If the value is different in dark mode, the suffix `_dark`. For example, `input_border_color_focus_dark`.\n\nOf course, many CSS variable names are shorter than this, such as\n`table_border_color`, or `input_shadow`.\n\n", "heading1": "CSS Variable Naming", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Though there are hundreds of CSS variables, they do not all have to have\nindividual values. They draw their values by referencing a set of core\nvariables and referencing each other. This allows us to only have to modify a\nfew variables to change the look and feel of the entire theme, while also\ngetting finer control of individual elements that we may want to modify.\n\nReferencing Core Variables\n\nTo reference one of the core constructor variables, precede the variable\nname with an asterisk. To reference a core color, use the `*primary_`,\n`*secondary_`, or `*neutral_` prefix, followed by the brightness value. For\nexample:\n\n    \n    \n    theme = gr.themes.Default(primary_hue=\"blue\").set(\n        button_primary_background_fill=\"*primary_200\",\n        button_primary_background_fill_hover=\"*primary_300\",\n    )\n\nIn the example above, we\u2019ve set the `button_primary_background_fill` and\n`button_primary_background_fill_hover` variables to `*primary_200` and\n`*primary_300`. These variables will be set to the 200 and 300 brightness\nvalues of the blue primary color palette, respectively.\n\nSimilarly, to reference a core size, use the `*spacing_`, `*radius_`, or\n`*text_` prefix, followed by the size value. For example:\n\n    \n    \n    theme = gr.themes.Default(radius_size=\"md\").set(\n        button_primary_border_radius=\"*radius_xl\",\n    )\n\nIn the example above, we\u2019ve set the `button_primary_border_radius` variable\nto `*radius_xl`. This variable will be set to the `xl` setting of the medium\nradius size range.\n\n", "heading1": "CSS Variable Organization", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Variables can also reference each other. For example, look at the example\nbelow:\n\n    \n    \n    theme = gr.themes.Default().set(\n        button_primary_background_fill=\"FF0000\",\n        button_primary_background_fill_hover=\"FF0000\",\n        button_primary_border=\"FF0000\",\n    )\n\nHaving to set these values to a common color is a bit tedious. Instead, we\ncan reference the `button_primary_background_fill` variable in the\n`button_primary_background_fill_hover` and `button_primary_border` variables,\nusing a `*` prefix.\n\n    \n    \n    theme = gr.themes.Default().set(\n        button_primary_background_fill=\"FF0000\",\n        button_primary_background_fill_hover=\"*button_primary_background_fill\",\n        button_primary_border=\"*button_primary_background_fill\",\n    )\n\nNow, if we change the `button_primary_background_fill` variable, the\n`button_primary_background_fill_hover` and `button_primary_border` variables\nwill automatically update as well.\n\nThis is particularly useful if you intend to share your theme - it makes it\neasy to modify the theme without having to change every variable.\n\nNote that dark mode variables automatically reference each other. For\nexample:\n\n    \n    \n    theme = gr.themes.Default().set(\n        button_primary_background_fill=\"FF0000\",\n        button_primary_background_fill_dark=\"AAAAAA\",\n        button_primary_border=\"*button_primary_background_fill\",\n        button_primary_border_dark=\"*button_primary_background_fill_dark\",\n    )\n\n`button_primary_border_dark` will draw its value from\n`button_primary_background_fill_dark`, because dark mode always draw from the\ndark version of the variable.\n\n", "heading1": "Referencing Other Variables", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Let\u2019s say you want to create a theme from scratch! We\u2019ll go through it step\nby step - you can also see the source of prebuilt themes in the gradio source\nrepo for reference - [here\u2019s the source](https://github.com/gradio-\napp/gradio/blob/main/gradio/themes/monochrome.py) for the Monochrome theme.\n\nOur new theme class will inherit from `gradio.themes.Base`, a theme that\nsets a lot of convenient defaults. Let\u2019s make a simple demo that creates a\ndummy theme called Seafoam, and make a simple app that uses it.\n\n$code_theme_new_step_1\n\nThe Base theme is very barebones, and uses `gr.themes.Blue` as it primary\ncolor - you\u2019ll note the primary button and the loading animation are both blue\nas a result. Let\u2019s change the defaults core arguments of our app. We\u2019ll\noverwrite the constructor and pass new defaults for the core constructor\narguments.\n\nWe\u2019ll use `gr.themes.Emerald` as our primary color, and set secondary and\nneutral hues to `gr.themes.Blue`. We\u2019ll make our text larger using `text_lg`.\nWe\u2019ll use `Quicksand` as our default font, loaded from Google Fonts.\n\n$code_theme_new_step_2\n\nSee how the primary button and the loading animation are now green? These CSS\nvariables are tied to the `primary_hue` variable.\n\nLet\u2019s modify the theme a bit more directly. We\u2019ll call the `set()` method to\noverwrite CSS variable values explicitly. We can use any CSS logic, and\nreference our core constructor arguments using the `*` prefix.\n\n$code_theme_new_step_3\n\nLook how fun our theme looks now! With just a few variable changes, our theme\nlooks completely different.\n\nYou may find it helpful to explore the [source code of the other prebuilt\nthemes](https://github.com/gradio-app/gradio/blob/main/gradio/themes) to see\nhow they modified the base theme. You can also find your browser\u2019s Inspector\nuseful to select elements from the UI and see what CSS variables are being\nused in the styles panel.\n\nSharing Themes\n\nOnce you have created a theme, you can upload it to the HuggingFace Hub to", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "ctor\nuseful to select elements from the UI and see what CSS variables are being\nused in the styles panel.\n\nSharing Themes\n\nOnce you have created a theme, you can upload it to the HuggingFace Hub to let\nothers view it, use it, and build off of it!\n\n", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "There are two ways to upload a theme, via the theme class instance or the\ncommand line. We will cover both of them with the previously created `seafoam`\ntheme.\n\n  * Via the class instance\n\nEach theme instance has a method called `push_to_hub` we can use to upload a\ntheme to the HuggingFace hub.\n\n    \n    \n    seafoam.push_to_hub(repo_name=\"seafoam\",\n                        version=\"0.0.1\",\n    \t\t\t\t\thf_token=\"<token>\")\n\n  * Via the command line\n\nFirst save the theme to disk\n\n    \n    \n    seafoam.dump(filename=\"seafoam.json\")\n\nThen use the `upload_theme` command:\n\n    \n    \n    upload_theme\\\n    \"seafoam.json\"\\\n    \"seafoam\"\\\n    --version \"0.0.1\"\\\n    --hf_token \"<token>\"\n\nIn order to upload a theme, you must have a HuggingFace account and pass your\n[Access Token](https://huggingface.co/docs/huggingface_hub/quick-startlogin)\nas the `hf_token` argument. However, if you log in via the [HuggingFace\ncommand line](https://huggingface.co/docs/huggingface_hub/quick-startlogin)\n(which comes installed with `gradio`), you can omit the `hf_token` argument.\n\nThe `version` argument lets you specify a valid [semantic\nversion](https://www.geeksforgeeks.org/introduction-semantic-versioning/)\nstring for your theme. That way your users are able to specify which version\nof your theme they want to use in their apps. This also lets you publish\nupdates to your theme without worrying about changing how previously created\napps look. The `version` argument is optional. If omitted, the next patch\nversion is automatically applied.\n\n", "heading1": "Uploading a Theme", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "By calling `push_to_hub` or `upload_theme`, the theme assets will be stored in\na [HuggingFace space](https://huggingface.co/docs/hub/spaces-overview).\n\nThe theme preview for our seafoam theme is here: [seafoam\npreview](https://huggingface.co/spaces/gradio/seafoam).\n\n", "heading1": "Theme Previews", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "The [Theme Gallery](https://huggingface.co/spaces/gradio/theme-gallery) shows\nall the public gradio themes. After publishing your theme, it will\nautomatically show up in the theme gallery after a couple of minutes.\n\nYou can sort the themes by the number of likes on the space and from most to\nleast recently created as well as toggling themes between light and dark mode.\n\n", "heading1": "Discovering Themes", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "To use a theme from the hub, use the `from_hub` method on the `ThemeClass` and\npass it to your app:\n\n    \n    \n    my_theme = gr.Theme.from_hub(\"gradio/seafoam\")\n    \n    with gr.Blocks(theme=my_theme) as demo:\n        ....\n\nYou can also pass the theme string directly to `Blocks` or `Interface`\n(`gr.Blocks(theme=\"gradio/seafoam\")`)\n\nYou can pin your app to an upstream theme version by using semantic versioning\nexpressions.\n\nFor example, the following would ensure the theme we load from the `seafoam`\nrepo was between versions `0.0.1` and `0.1.0`:\n\n    \n    \n    with gr.Blocks(theme=\"gradio/seafoam@>=0.0.1,<0.1.0\") as demo:\n        ....\n\nEnjoy creating your own themes! If you make one you\u2019re proud of, please share\nit with the world by uploading it to the hub! If you tag us on\n[Twitter](https://twitter.com/gradio) we can give your theme a shout out!\n\n", "heading1": "Downloading", "source_page_url": "https://gradio.app/docs/gradio/themes", "source_page_title": "Gradio - Themes Docs"}, {"text": "Creates a textarea for user to enter string input or display string output.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "**As input component** : Passes text value as a `str` into the function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` returned from function and sets\ntextarea value to it.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | I18nData | Callable | None\n\ndefault `= None`\n\ntext to show in textbox. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    type: Literal['text', 'password', 'email']\n\ndefault `= \"text\"`\n\nThe type of textbox. One of: 'text' (which allows users to enter any text),\n'password' (which masks text entered by the user), 'email' (which suggests\nemail input to the browser). For \"password\" and \"email\" types, `lines` must be\n1 and `max_lines` must be None or 1.\n\n\n    \n    \n    lines: int\n\ndefault `= 1`\n\nminimum number of line rows to provide in textarea.\n\n\n    \n    \n    max_lines: int | None\n\ndefault `= None`\n\nmaximum number of line rows to provide in textarea. Must be at least `lines`.\nIf not provided, the maximum number of lines is max(lines, 20) for \"text\"\ntype, and 1 for \"password\" and \"email\" types.\n\n\n    \n    \n    placeholder: str | I18nData | None\n\ndefault `= None`\n\nplaceholder hint to provide behind textarea.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value`", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display the label. If False, the copy button is hidden as well\nas well as the label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable textbox; if False, editing will be\ndisabled. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= False`\n\nIf True, will focus on the textbox when the page loads. Use this carefully, as\nit can cause usability issues for sighted and non-sighted users.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textb", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "se this carefully, as\nit can cause usability issues for sighted and non-sighted users.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    text_align: Literal['left', 'right'] | None\n\ndefault `= None`\n\nHow to align the text in the textbox, can be: \"left\", \"right\", or None\n(default). If None, the alignment is left if `rtl` is False, or right if `rtl`\nis True. Can only be changed if `type` is \"text\".\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True and `type` is \"text\", sets the direction of the text to right-to-left\n(cursor appears on the left of the text). Default is False, which renders\ncursor on the right.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf Tru", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " direction of the text to right-to-left\n(cursor appears on the left of the text). Default is False, which renders\ncursor on the right.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, includes a copy button to copy the text in the textbox. Only applies\nif show_label is True.\n\n\n    \n    \n    max_length: int | None\n\ndefault `= None`\n\nmaximum number of characters (including newlines) allowed in the textbox. If\nNone, there is no maximum length.\n\n\n    \n    \n    submit_btn: str | bool | None\n\ndefault `= False`\n\nIf False, will not show a submit button. If True, will show a submit button\nwith an icon. If a string, will use that string as the submit button text.\nWhen the submit button is shown, the border of the textbox will be removed,\nwhich is useful for creating a chat interface.\n\n\n    \n    \n    stop_btn: str | bool | None\n\ndefault `= False`\n\nIf False, will not show a stop button. If True, will show a stop button with\nan icon. If a string, will use that string as the stop button text. When the\nstop button is shown, the border of the textbox will be removed, which is\nuseful for creating a chat interface.\n\n\n    \n    \n    html_attributes: InputHTMLAttributes | None\n\ndefault `= None`\n\nAn instance of gr.InputHTMLAttributes, which can be used to set HTML\nattributes for the input/textarea elements. Example:\nInputHTMLAttributes(autocorrect=\"off\", spellcheck=False) to disable\nautocorrect and spellcheck.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Textbox` | \"textbox\" | Uses default values  \n`gradio.TextArea` | \"textarea\" | Uses lines=7  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "hello_worlddiff_textssentence_builder\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def greet(name): return \"Hello \" + name + \"!\"\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\") if __name__\n== \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    \n    def greet(name):\n        return \"Hello \" + name + \"!\"\n    \n    \n    demo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 from difflib import Differ import gradio as gr def\ndiff_texts(text1, text2): d = Differ() return [ (token[2:], token[0] if\ntoken[0] != \" \" else None) for token in d.compare(text1, text2) ] demo =\ngr.Interface( diff_texts, [ gr.Textbox( label=\"Text 1\", info=\"Initial text\",\nlines=3, value=\"The quick brown fox jumped over the lazy dogs.\", ),\ngr.Textbox( label=\"Text 2\", info=\"Text to compare\", lines=3, value=\"The fast\nbrown fox jumps over lazy dogs.\", ), ], gr.HighlightedText( label=\"Diff\",\ncombine_adjacent=True, show_legend=True, color_map={\"+\": \"red\", \"-\":\n\"green\"}), theme=gr.themes.Base() ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    from difflib import Differ\n    \n    import gradio as gr\n    \n    def diff_texts(text1, text2):\n        d = Differ()\n        return [\n            (token[2:], token[0] if token[0] != \" \" else None)\n            for token in d.compare(text1, text2)\n        ]\n    \n    demo = gr.Interface(\n        diff_texts,\n        [\n            gr.Textbox(\n                label=\"Text 1\",\n                info=\"Initial text\",\n                lines=3,\n                value=\"The quick brown fox jumped over the lazy dogs.\",\n            ),\n            gr.Textbox(\n                label=\"Text 2\",\n                info=\"Text to compare\",\n                lines=3,\n                value=\"The fast brown fox jumps over lazy dogs.\",\n            ),\n        ],\n        gr.HighlightedText(\n            label=\"Diff\",\n            combine_adjacent=True,\n            show_legend=True,\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "  value=\"The fast brown fox jumps over lazy dogs.\",\n            ),\n        ],\n        gr.HighlightedText(\n            label=\"Diff\",\n            combine_adjacent=True,\n            show_legend=True,\n            color_map={\"+\": \"red\", \"-\": \"green\"}),\n        theme=gr.themes.Base()\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Textbox component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Textbox.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Textbox changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Textbox.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Textbox.  \n`Textbox.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Textbox. Uses event data gradio.SelectData to carry `value` referring to the label of the Textbox, and `selected` to refer to state of the Textbox. See EventData documentation on how to use this event data  \n`Textbox.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the Textbox is focused.  \n`Textbox.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Textbox is focused.  \n`Textbox.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Textbox is unfocused/blurred.  \n`Textbox.stop(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the Textbox.  \n`Textbox.copy(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user copies content from the Textbox. Uses event data gradio.CopyData to carry information about the copied content. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to cal", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Lite", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show thi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": " this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/textbox", "source_page_title": "Gradio - Textbox Docs"}, {"text": "A Gradio Interface includes a \u2018Flag\u2019 button that appears underneath the\noutput. By default, clicking on the Flag button sends the input and output\ndata back to the machine where the gradio demo is running, and saves it to a\nCSV log file. But this default behavior can be changed. To set what happens\nwhen the Flag button is clicked, you pass an instance of a subclass of\n_FlaggingCallback_ to the _flagging_callback_ parameter in the _Interface_\nconstructor. You can use one of the _FlaggingCallback_ subclasses that are\nlisted below, or you can create your own, which lets you do whatever you want\nwith the data that is being flagged.\n\nSimpleCSVLogger\n\n    \n    \n    gradio.SimpleCSVLogger(\u00b7\u00b7\u00b7)\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "A simplified implementation of the FlaggingCallback abstract class provided\nfor illustrative purposes. Each flagged sample (both the input and output\ndata) is logged to a CSV file on the machine running the gradio app.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "import gradio as gr\n    def image_classifier(inp):\n        return {'cat': 0.3, 'dog': 0.7}\n    demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\",\n                        flagging_callback=SimpleCSVLogger())\n\nCSVLogger\n\n    \n    \n    gradio.CSVLogger(\u00b7\u00b7\u00b7)\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "The default implementation of the FlaggingCallback abstract class in\ngradio>=5.0. Each flagged sample (both the input and output data) is logged to\na CSV file with headers on the machine running the gradio app. Unlike\nClassicCSVLogger, this implementation is concurrent-safe and it creates a new\ndataset file every time the headers of the CSV (derived from the labels of the\ncomponents) change. It also only creates columns for \"username\" and \"flag\" if\nthe flag_option and username are provided, respectively.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "import gradio as gr\n    def image_classifier(inp):\n        return {'cat': 0.3, 'dog': 0.7}\n    demo = gr.Interface(fn=image_classifier, inputs=\"image\", outputs=\"label\",\n                        flagging_callback=CSVLogger())\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    simplify_file_data: bool\n\ndefault `= True`\n\nIf True, the file data will be simplified before being written to the CSV\nfile. If CSVLogger is being used to cache examples, this is set to False to\npreserve the original FileData class\n\n\n    \n    \n    verbose: bool\n\ndefault `= True`\n\nIf True, prints messages to the console about the dataset file creation\n\n\n    \n    \n    dataset_file_name: str | None\n\ndefault `= None`\n\nThe name of the dataset file to be created (should end in \".csv\"). If None,\nthe dataset file will be named \"dataset1.csv\" or the next available number.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/flagging", "source_page_title": "Gradio - Flagging Docs"}, {"text": "ChatInterface is Gradio's high-level abstraction for creating chatbot UIs,\nand allows you to create a web-based demo around a chatbot model in a few\nlines of code. Only one parameter is required: fn, which takes a function that\ngoverns the response of the chatbot based on the user input and chat history.\nAdditional parameters can be used to control the appearance and behavior of\nthe demo.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "**Basic Example** : A chatbot that echoes back the users\u2019s message\n\n    \n    \n    import gradio as gr\n    \n    def echo(message, history):\n        return message\n    \n    demo = gr.ChatInterface(fn=echo, type=\"messages\", examples=[\"hello\", \"hola\", \"merhaba\"], title=\"Echo Bot\")\n    demo.launch()\n\n**Custom Chatbot** : A `gr.ChatInterface` with a custom `gr.Chatbot` that\nincludes a placeholder as well as upvote/downvote buttons. The upvote/downvote\nbuttons are automatically added when a `.like()` event is attached to a\n`gr.Chatbot`. In order to attach event listeners to your custom chatbot, wrap\nthe `gr.Chatbot` as well as the `gr.ChatInterface` inside of a `gr.Blocks`\nlike this:\n\n    \n    \n    import gradio as gr\n    \n    def yes(message, history):\n        return \"yes\"\n    \n    def vote(data: gr.LikeData):\n        if data.liked:\n            print(\"You upvoted this response: \" + data.value[\"value\"])\n        else:\n            print(\"You downvoted this response: \" + data.value[\"value\"])\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(placeholder=\"<strong>Your Personal Yes-Man</strong><br>Ask Me Anything\")\n        chatbot.like(vote, None, None)\n        gr.ChatInterface(fn=yes, type=\"messages\", chatbot=chatbot)\n        \n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    fn: Callable\n\nthe function to wrap the chat interface around. Normally (assuming `type` is set to \"messages\"), the function should accept two parameters: a `str` representing the input message and `list` of openai-style dictionaries: {\"role\": \"user\" | \"assistant\", \"content\": `str` | {\"path\": `str`} | `gr.Component`} representing the chat history. The function should return/yield a `str` (for a simple message), a supported Gradio component (e.g. gr.Image to return an image), a `dict` (for a complete openai-style message response), or a `list` of such messages.\n\n\n    \n    \n    multimodal: bool\n\ndefault `= False`\n\nif True, the chat interface will use a `gr.MultimodalTextbox` component for\nthe input, which allows for the uploading of multimedia files. If False, the\nchat interface will use a gr.Textbox component for the input. If this is True,\nthe first argument of `fn` should accept not a `str` message but a `dict`\nmessage with keys \"text\" and \"files\"\n\n\n    \n    \n    type: Literal['messages', 'tuples'] | None\n\ndefault `= None`\n\nThe format of the messages passed into the chat history parameter of `fn`. If \"messages\", passes the history as a list of dictionaries with openai-style \"role\" and \"content\" keys. The \"content\" key's value should be one of the following - (1) strings in valid Markdown (2) a dictionary with a \"path\" key and value corresponding to the file to display or (3) an instance of a Gradio component: at the moment gr.Image, gr.Plot, gr.Video, gr.Gallery, gr.Audio, and gr.HTML are supported. The \"role\" key should be one of 'user' or 'assistant'. Any other roles will not be displayed in the output. If this parameter is 'tuples' (deprecated), passes the chat history as a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message.\n\n\n    \n    \n    chatbot: Chatbot | None\n\ndefault `= None`\n\nan instance of the gr.Chatbot component to use for the chat interfac", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": " list should have 2 elements: the user message and the response message.\n\n\n    \n    \n    chatbot: Chatbot | None\n\ndefault `= None`\n\nan instance of the gr.Chatbot component to use for the chat interface, if you\nwould like to customize the chatbot properties. If not provided, a default\ngr.Chatbot component will be created.\n\n\n    \n    \n    textbox: Textbox | MultimodalTextbox | None\n\ndefault `= None`\n\nan instance of the gr.Textbox or gr.MultimodalTextbox component to use for the\nchat interface, if you would like to customize the textbox properties. If not\nprovided, a default gr.Textbox or gr.MultimodalTextbox component will be\ncreated.\n\n\n    \n    \n    additional_inputs: str | Component | list[str | Component] | None\n\ndefault `= None`\n\nan instance or list of instances of gradio components (or their string\nshortcuts) to use as additional inputs to the chatbot. If the components are\nnot already rendered in a surrounding Blocks, then the components will be\ndisplayed under the chatbot, in an accordion. The values of these components\nwill be passed into `fn` as arguments in order after the chat history.\n\n\n    \n    \n    additional_inputs_accordion: str | Accordion | None\n\ndefault `= None`\n\nif a string is provided, this is the label of the `gr.Accordion` to use to\ncontain additional inputs. A `gr.Accordion` object can be provided as well to\nconfigure other properties of the container holding the additional inputs.\nDefaults to a `gr.Accordion(label=\"Additional Inputs\", open=False)`. This\nparameter is only used if `additional_inputs` is provided.\n\n\n    \n    \n    additional_outputs: Component | list[Component] | None\n\ndefault `= None`\n\nan instance or list of instances of gradio components to use as additional\noutputs from the chat function. These must be components that are already\ndefined in the same Blocks scope. If provided, the chat function should return\nadditional values for these components. See\n[demo/chatinterface_artifacts](https://gradio.app/playground?demo=Blank&code=a", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "efined in the same Blocks scope. If provided, the chat function should return\nadditional values for these components. See\n[demo/chatinterface_artifacts](https://gradio.app/playground?demo=Blank&code=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%2BPGgxPldyaXRlIFB5dGhvbiBvciBKYXZhU2NyaXB0PC9oMT48L2NlbnRlcj4iKQogICAgICAgICAgICBnci5DaGF0SW50ZXJmYWNlKAogICAgICAgICAgICAgICAgY2hhdCwKICAgICAgICAgICAgICAgIGV4YW1wbGVzPVsiUHl0aG9uIiwgIkphdmFTY3JpcHQiXSwKICAgICAgICAgICAgICAgIGFkZGl0aW9uYWxfb3V0cHV0cz1bY29kZV0sCiAgICAgICAgICAgICAgICB0eXBlPSJtZXNzYWdlcyIKICAgICAgICAgICAgKQogICAgICAgIHdpdGggZ3IuQ29sdW1uKCk6CiAgICAgICAgICAgIGdyLk1hcmtkb3duKCI8Y2VudGVyPjxoMT5Db2RlIEFydGlmYWN0czwvaDE%2BPC9jZW50ZXI%2BIikKICAgICAgICAgICAgY29kZS5yZW5kZXIoKQoKZGVtby5sYXVuY2goKQo%3D).\n\n\n    \n    \n    editable: bool\n\ndefault `= False`\n\nif True, users can edit past messages to regenerate responses.\n\n\n    \n    \n    examples: list[str] | list[MultimodalValue] | list[list] | N", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "oKQo%3D).\n\n\n    \n    \n    editable: bool\n\ndefault `= False`\n\nif True, users can edit past messages to regenerate responses.\n\n\n    \n    \n    examples: list[str] | list[MultimodalValue] | list[list] | None\n\ndefault `= None`\n\nsample inputs for the function; if provided, appear within the chatbot and can\nbe clicked to populate the chatbot input. Should be a list of strings\nrepresenting text-only examples, or a list of dictionaries (with keys `text`\nand `files`) representing multimodal examples. If `additional_inputs` are\nprovided, the examples must be a list of lists, where the first element of\neach inner list is the string or dictionary example message and the remaining\nelements are the example values for the additional inputs -- in this case, the\nexamples will appear under the chatbot.\n\n\n    \n    \n    example_labels: list[str] | None\n\ndefault `= None`\n\nlabels for the examples, to be displayed instead of the examples themselves.\nIf provided, should be a list of strings with the same length as the examples\nlist. Only applies when examples are displayed within the chatbot (i.e. when\n`additional_inputs` is not provided).\n\n\n    \n    \n    example_icons: list[str] | None\n\ndefault `= None`\n\nicons for the examples, to be displayed above the examples. If provided,\nshould be a list of string URLs or local paths with the same length as the\nexamples list. Only applies when examples are displayed within the chatbot\n(i.e. when `additional_inputs` is not provided).\n\n\n    \n    \n    run_examples_on_click: bool\n\ndefault `= True`\n\nif True, clicking on an example will run the example through the chatbot fn\nand the response will be displayed in the chatbot. If False, clicking on an\nexample will only populate the chatbot input with the example message. Has no\neffect if `cache_examples` is True\n\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nif True, caches examples in the server for fast runtime in examples. The\ndefault option in HuggingFace Spaces is True. The default option", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "\n\n    \n    \n    cache_examples: bool | None\n\ndefault `= None`\n\nif True, caches examples in the server for fast runtime in examples. The\ndefault option in HuggingFace Spaces is True. The default option elsewhere is\nFalse. Note that examples are cached separately from Gradio's queue() so\ncertain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will\nnot be displayed in Gradio's UI for cached examples.\n\n\n    \n    \n    cache_mode: Literal['eager', 'lazy'] | None\n\ndefault `= None`\n\nif \"eager\", all examples are cached at app launch. If \"lazy\", examples are\ncached for all users after the first use by any user of the app. If None, will\nuse the GRADIO_CACHE_MODE environment variable if defined, or default to\n\"eager\".\n\n\n    \n    \n    title: str | I18nData | None\n\ndefault `= None`\n\na title for the interface; if provided, appears above chatbot in large font.\nAlso used as the tab title when opened in a browser window.\n\n\n    \n    \n    description: str | None\n\ndefault `= None`\n\na description for the interface; if provided, appears above the chatbot and\nbeneath the title in regular font. Accepts Markdown and HTML content.\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\na Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    flagging_mode: Literal['never', 'manual'] | None\n\ndefault `= None`\n\none of \"never\", \"manual\". If \"never\", users will not see a button to flag an\ninput and output. If \"manual\", users will see a button to flag.\n\n\n    \n    \n    flagging_options: list[str] | tuple[str, ...] | None\n\ndefault `= ('Like', 'Dislike')`\n\na list of strings representing the options that users can choose from when\nflagging a message. Defaults to [\"Like\", \"Dislike\"]. These two case-sensitive\nstrings will render as \"thumbs up\" and \"thumbs down\" icon resp", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "gs representing the options that users can choose from when\nflagging a message. Defaults to [\"Like\", \"Dislike\"]. These two case-sensitive\nstrings will render as \"thumbs up\" and \"thumbs down\" icon respectively next to\neach bot message, but any other strings appear under a separate flag icon.\n\n\n    \n    \n    flagging_dir: str\n\ndefault `= \".gradio/flagged\"`\n\npath to the the directory where flagged data is stored. If the directory does\nnot exist, it will be created.\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a code string. This css will be included in the demo webpage.\n\n\n    \n    \n    css_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom css as a pathlib.Path to a css file or a list of such paths. This css\nfiles will be read, concatenated, and included in the demo webpage. If the\n`css` parameter is also set, the css from `css` will be included first.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a code string. The custom js should be in the form of a single js\nfunction. This function will automatically be executed when the page loads.\nFor more flexibility, use the head parameter to insert js inside <script>\ntags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html code to insert into the head of the demo webpage. This can be used\nto add custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n\n    \n    \n    head_paths: str | Path | list[str | Path] | None\n\ndefault `= None`\n\nCustom html code as a pathlib.Path to a html file or a list of such paths.\nThis html files will be read, concatenated, and included in the head of the\ndemo webpage. If the `head` parameter is also set, the html from `head` will\nbe included first.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= True`\n\nif True, autofo", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "e`\n\nwhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable if defined, or default to True.\n\n\n    \n    \n    autofocus: bool\n\ndefault `= True`\n\nif True, autofocuses to the textbox when the page loads.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the chatbot when a new\nmessage appears, unless the user scrolls up. If False, will not scroll to the\nbottom of the chatbot automatically.\n\n\n    \n    \n    submit_btn: str | bool | None\n\ndefault `= True`\n\nIf True, will show a submit button with a submit icon within the textbox. If a\nstring, will use that string as the submit button text in place of the icon.\nIf False, will not show a submit button.\n\n\n    \n    \n    stop_btn: str | bool | None\n\ndefault `= True`\n\nIf True, will show a button with a stop icon during generator executions, to\nstop generating. If a string, will use that string as the submit button text\nin place of the stop icon. If False, will not show a stop button.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nif set, this is the maximum number of chatbot submissions that can be running\nsimultaneously. Can be set to None to mean no limit (any number of chatbot\nsubmissions can be running simultaneously). Set to \"default\" to use the\ndefault concurrency limit (defined by the `default_concurrency_limit`\nparameter in `.queue()`, which is 1 by default).\n\n\n    \n    \n    delete_cache: tuple[int, int] | None\n\ndefault `= None`\n\na tuple corresponding [frequency, age] both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance\nwill be deleted if more than `age` seconds have passed since the file was\ncreated. For example, setting this to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n    \n    \n    show_progress: Literal['ful", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "to (86400, 86400) will delete temporary\nfiles every day. The cache will be deleted entirely when the server restarts.\nIf None, no cache deletion will occur.\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"minimal\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    fill_height: bool\n\ndefault `= True`\n\nif True, the chat interface will expand to the height of window.\n\n\n    \n    \n    fill_width: bool\n\ndefault `= False`\n\nWhether to horizontally expand to fill container fully. If False, centers and\nconstrains app to a maximum width.\n\n\n    \n    \n    api_name: str | Literal[False]\n\ndefault `= \"chat\"`\n\ndefines how the chat endpoint appears in the API docs. Can be a string or\nFalse. If set to a string, the chat endpoint will be exposed in the API docs\nwith the given name. If False, the chat endpoint will not be exposed in the\nAPI docs and downstream apps (including those that `gr.load` this app) will\nnot be able to call this chat endpoint.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show the chat endpoint in the \"view API\" page of the Gradio app, or\nin the \".view_api()\" method of the Gradio clients. Unlike setting api_name to\nFalse, setting show_api to False will still allow downstream apps as well as\nthe Clients to use this event. If fn is None, show_api will automatically be\nset to False.\n\n\n    \n    \n    save_history: bool", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "lse, setting show_api to False will still allow downstream apps as well as\nthe Clients to use this event. If fn is None, show_api will automatically be\nset to False.\n\n\n    \n    \n    save_history: bool\n\ndefault `= False`\n\nif True, will save the chat history to the browser's local storage and display\nprevious conversations in a side panel.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "chatinterface_random_responsechatinterface_streaming_echochatinterface_artifacts\n\nOpen in \ud83c\udfa2 \u2197 import random import gradio as gr def random_response(message,\nhistory): return random.choice([\"Yes\", \"No\"]) demo =\ngr.ChatInterface(random_response, type=\"messages\", autofocus=False) if\n__name__ == \"__main__\": demo.launch()\n\n    \n    \n    import random\n    import gradio as gr\n    \n    def random_response(message, history):\n        return random.choice([\"Yes\", \"No\"])\n    \n    demo = gr.ChatInterface(random_response, type=\"messages\", autofocus=False)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import time import gradio as gr def slow_echo(message, history):\nfor i in range(len(message)): time.sleep(0.05) yield \"You typed: \" + message[:\ni + 1] demo = gr.ChatInterface( slow_echo, type=\"messages\",\nflagging_mode=\"manual\", flagging_options=[\"Like\", \"Spam\", \"Inappropriate\",\n\"Other\"], save_history=True, ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import time\n    import gradio as gr\n    \n    def slow_echo(message, history):\n        for i in range(len(message)):\n            time.sleep(0.05)\n            yield \"You typed: \" + message[: i + 1]\n    \n    demo = gr.ChatInterface(\n        slow_echo,\n        type=\"messages\",\n        flagging_mode=\"manual\",\n        flagging_options=[\"Like\", \"Spam\", \"Inappropriate\", \"Other\"],\n        save_history=True,\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr python_code = \"\"\" def fib(n): if n <= 0:\nreturn 0 elif n == 1: return 1 else: return fib(n-1) + fib(n-2) \"\"\" js_code =\n\"\"\" function fib(n) { if (n <= 0) return 0; if (n === 1) return 1; return\nfib(n - 1) + fib(n - 2); } \"\"\" def chat(message, history): if \"python\" in\nmessage.lower(): return \"Type Python or JavaScript to see the code.\",\ngr.Code(language=\"python\", value=python_code) elif \"javascript\" in\nmessage.lower(): return \"Type Python or JavaScript to see the code.\",\ngr.Code(language=\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "Type Python or JavaScript to see the code.\",\ngr.Code(language=\"python\", value=python_code) elif \"javascript\" in\nmessage.lower(): return \"Type Python or JavaScript to see the code.\",\ngr.Code(language=\"javascript\", value=js_code) else: return \"Please ask about\nPython or JavaScript.\", None with gr.Blocks() as demo: code =\ngr.Code(render=False) with gr.Row(): with gr.Column():\ngr.Markdown(\"<center><h1>Write Python or JavaScript</h1></center>\")\ngr.ChatInterface( chat, examples=[\"Python\", \"JavaScript\"],\nadditional_outputs=[code], type=\"messages\" ) with gr.Column():\ngr.Markdown(\"<center><h1>Code Artifacts</h1></center>\") code.render()\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    python_code = \"\"\"\n    def fib(n):\n        if n <= 0:\n            return 0\n        elif n == 1:\n            return 1\n        else:\n            return fib(n-1) + fib(n-2)\n    \"\"\"\n    \n    js_code = \"\"\"\n    function fib(n) {\n        if (n <= 0) return 0;\n        if (n === 1) return 1;\n        return fib(n - 1) + fib(n - 2);\n    }\n    \"\"\"\n    \n    def chat(message, history):\n        if \"python\" in message.lower():\n            return \"Type Python or JavaScript to see the code.\", gr.Code(language=\"python\", value=python_code)\n        elif \"javascript\" in message.lower():\n            return \"Type Python or JavaScript to see the code.\", gr.Code(language=\"javascript\", value=js_code)\n        else:\n            return \"Please ask about Python or JavaScript.\", None\n    \n    with gr.Blocks() as demo:\n        code = gr.Code(render=False)\n        with gr.Row():\n            with gr.Column():\n                gr.Markdown(\"\n    \n    Write Python or JavaScript\n    \n    \")\n                gr.ChatInterface(\n                    chat,\n                    examples=[\"Python\", \"JavaScript\"],\n                    additional_outputs=[code],\n                    type=\"messages\"\n                )\n            with gr.Column():\n                gr.Markdown(\"\n    \n    Code Artifacts\n    \n    \")\n                code.rend", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "tional_outputs=[code],\n                    type=\"messages\"\n                )\n            with gr.Column():\n                gr.Markdown(\"\n    \n    Code Artifacts\n    \n    \")\n                code.render()\n    \n    demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatinterface", "source_page_title": "Gradio - Chatinterface Docs"}, {"text": "This component displays a table of value spreadsheet-like component. Can be\nused to display data as an output component, or as an input to collect data\nfrom the user.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "**As input component** : Passes the uploaded spreadsheet data as a\n`pandas.DataFrame`, `numpy.array`, `polars.DataFrame`, or native 2D Python\n`list[list]` depending on `type`\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: pd.DataFrame | np.ndarray | pl.DataFrame | list[list]\n    )\n    \t...\n\n  \n\n**As output component** : Expects data in any of these formats:\n`pandas.DataFrame`, `pandas.Styler`, `numpy.array`, `polars.DataFrame`,\n`list[list]`, `list`, or a `dict` with keys 'data' (and optionally 'headers'),\nor `str` path to a csv, which is rendered as the spreadsheet.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | Callable | None\n\ndefault `= None`\n\nDefault value to display in the DataFrame. Supports pandas, numpy, polars, and\nlist of lists. If a Styler is provided, it will be used to set the displayed\nvalue in the DataFrame (e.g. to set precision of numbers) if the `interactive`\nis False. If a Callable function is provided, the function will be called\nwhenever the app loads to set the initial value of the component.\n\n\n    \n    \n    headers: list[str] | None\n\ndefault `= None`\n\nList of str header names. These are used to set the column headers of the\ndataframe if the value does not have headers. If None, no headers are shown.\n\n\n    \n    \n    row_count: int | tuple[int, str]\n\ndefault `= (1, 'dynamic')`\n\nLimit number of rows for input and decide whether user can create new rows or\ndelete existing rows. The first element of the tuple is an `int`, the row\ncount; the second should be 'fixed' or 'dynamic', the new row behaviour. If an\n`int` is passed the rows default to 'dynamic'\n\n\n    \n    \n    col_count: int | tuple[int, str] | None\n\ndefault `= None`\n\nLimit number of columns for input and decide whether user can create new\ncolumns or delete existing columns. The first element of the tuple is an\n`int`, the number of columns; the second should be 'fixed' or 'dynamic', the\nnew column behaviour. If an `int` is passed the columns default to 'dynamic'\n\n\n    \n    \n    datatype: Literal['str', 'number', 'bool', 'date', 'markdown', 'html', 'image', 'auto'] | list[Literal['str', 'number', 'bool', 'date', 'markdown', 'html']]\n\ndefault `= \"str\"`\n\nDatatype of values in sheet. Can be provided per column as a list of strings,\nor for the entire sheet as a single string. Valid datatypes are \"str\",\n\"number\", \"bool\", \"date\", and \"markdown\". Boolean columns will display as\ncheckboxes. If the datatype \"auto\" is used, the column datatypes are\nautomatically selected based on the value ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "pes are \"str\",\n\"number\", \"bool\", \"date\", and \"markdown\". Boolean columns will display as\ncheckboxes. If the datatype \"auto\" is used, the column datatypes are\nautomatically selected based on the value input if possible.\n\n\n    \n    \n    type: Literal['pandas', 'numpy', 'array', 'polars']\n\ndefault `= \"pandas\"`\n\nType of value to be returned by component. \"pandas\" for pandas dataframe,\n\"numpy\" for numpy array, \"polars\" for polars dataframe, or \"array\" for a\nPython list of lists.\n\n\n    \n    \n    latex_delimiters: list[dict[str, str | bool]] | None\n\ndefault `= None`\n\nA list of dicts of the form {\"left\": open delimiter (str), \"right\": close\ndelimiter (str), \"display\": whether to display in newline (bool)} that will be\nused to render LaTeX expressions. If not provided, `latex_delimiters` is set\nto `[{ \"left\": \"$$\", \"right\": \"$$\", \"display\": True }]`, so only expressions\nenclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass\nin an empty list to disable LaTeX rendering. For more information, see the\n[KaTeX documentation](https://katex.org/docs/autorender.html). Only applies to\ncolumns whose datatype is \"markdown\".\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "l for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    max_height: int | str\n\ndefault `= 500`\n\nThe maximum height of the dataframe, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If more rows are created than\ncan fit in the height, a scrollbar will appear.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to edit the dataframe; if False, can only be used to\ndisplay data. If not provided, this is inferred based on whether the component\nis used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign eve", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "e used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    wrap: bool\n\ndefault `= False`\n\nIf True, the text in table cells will wrap when appropriate. If False and the\n`column_width` parameter is not set, the column widths will expand based on\nthe cell contents and the table may need to be horizontally scrolled. If\n`column_width` is set, then any overflow text will be hidden.\n\n\n    \n    \n    line_breaks: bool\n\ndefault `= True`\n\nIf True (default), will enable Github-flavored Markdown line breaks in chatbot\nmessages. If False, single new lines will be ignored. Only applies for columns\nof type \"markdown.\"\n\n\n    \n    \n    column_widths: list[str | int] | None\n\ndefault `= None`\n\nAn optional list representing the width of each column. The elements of the\nlist should be in the format \"100px\" (ints are also accepted and converted to\npixel values) or \"10%\". The percentage width is calculated based on the\nviewport width of the table. If not provided, the column widths will be\nautomatically determined based on the content of the cells.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "port width of the table. If not provided, the column widths will be\nautomatically determined based on the content of the cells.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True, will show a button to view the values in the table in fullscreen\nmode.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, will show a button to copy the table data to the clipboard.\n\n\n    \n    \n    show_row_numbers: bool\n\ndefault `= False`\n\nIf True, will display row numbers in a separate column.\n\n\n    \n    \n    max_chars: int | None\n\ndefault `= None`\n\nMaximum number of characters to display in each cell before truncating\n(single-clicking a cell value will still reveal the full content). If None, no\ntruncation is applied.\n\n\n    \n    \n    show_search: Literal['none', 'search', 'filter']\n\ndefault `= \"none\"`\n\nShow a search input in the toolbar. If \"search\", a search input is shown. If\n\"filter\", a search input and filter buttons are shown. If \"none\", no search\ninput is shown.\n\n\n    \n    \n    pinned_columns: int | None\n\ndefault `= None`\n\nIf provided, will pin the specified number of columns from the left.\n\n\n    \n    \n    static_columns: list[int] | None\n\ndefault `= None`\n\nList of column indices (int) that should not be editable. Only applies when\ninteractive=True. When specified, col_count is automatically set to \"fixed\"\nand columns cannot be inserted or deleted.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Dataframe` | \"dataframe\" | Uses default values  \n`gradio.Numpy` | \"numpy\" | Uses type=\"numpy\"  \n`gradio.Matrix` | \"matrix\" | Uses type=\"array\"  \n`gradio.List` | \"list\" | Uses type=\"array\", col_count=1  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "filter_recordsmatrix_transposetax_calculatorsort_records\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def filter_records(records, gender): return\nrecords[records[\"gender\"] == gender] demo = gr.Interface( filter_records, [\ngr.Dataframe( headers=[\"name\", \"age\", \"gender\"], datatype=[\"str\", \"number\",\n\"str\"], row_count=5, col_count=(3, \"fixed\"), ), gr.Dropdown([\"M\", \"F\", \"O\"]),\n], \"dataframe\", description=\"Enter gender as 'M', 'F', or 'O' for other.\", )\nif __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def filter_records(records, gender):\n        return records[records[\"gender\"] == gender]\n    \n    demo = gr.Interface(\n        filter_records,\n        [\n            gr.Dataframe(\n                headers=[\"name\", \"age\", \"gender\"],\n                datatype=[\"str\", \"number\", \"str\"],\n                row_count=5,\n                col_count=(3, \"fixed\"),\n            ),\n            gr.Dropdown([\"M\", \"F\", \"O\"]),\n        ],\n        \"dataframe\",\n        description=\"Enter gender as 'M', 'F', or 'O' for other.\",\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr def transpose(matrix):\nreturn matrix.T demo = gr.Interface( transpose, gr.Dataframe(type=\"numpy\",\ndatatype=\"number\", row_count=5, col_count=3, show_fullscreen_button=True),\n\"numpy\", examples=[ [np.zeros((30, 30)).tolist()], [np.ones((2, 2)).tolist()],\n[np.random.randint(0, 10, (3, 10)).tolist()], [np.random.randint(0, 10, (10,\n3)).tolist()], [np.random.randint(0, 10, (10, 10)).tolist()], ],\ncache_examples=False ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    \n    import gradio as gr\n    \n    def transpose(matrix):\n        return matrix.T\n    \n    demo = gr.Interface(\n        transpose,\n        gr.Dataframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3, show_fullscreen_button=True),\n        \"numpy\",\n        examples=[\n            [np.zeros((30, 30)).tolist()],\n            [np.ones((2, ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "aframe(type=\"numpy\", datatype=\"number\", row_count=5, col_count=3, show_fullscreen_button=True),\n        \"numpy\",\n        examples=[\n            [np.zeros((30, 30)).tolist()],\n            [np.ones((2, 2)).tolist()],\n            [np.random.randint(0, 10, (3, 10)).tolist()],\n            [np.random.randint(0, 10, (10, 3)).tolist()],\n            [np.random.randint(0, 10, (10, 10)).tolist()],\n        ],\n        cache_examples=False\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def tax_calculator(income, marital_status,\nassets): tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\ntotal_deductible = sum(assets[\"Cost\"]) taxable_income = income -\ntotal_deductible total_tax = 0 for bracket, rate in tax_brackets: if\ntaxable_income > bracket: total_tax += (taxable_income - bracket) * rate / 100\nif marital_status == \"Married\": total_tax *= 0.75 elif marital_status ==\n\"Divorced\": total_tax *= 0.8 return round(total_tax) demo = gr.Interface(\ntax_calculator, [ \"number\", gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\ngr.Dataframe( headers=[\"Item\", \"Cost\"], datatype=[\"str\", \"number\"],\nlabel=\"Assets Purchased this Year\", ), ], \"number\", examples=[ [10000,\n\"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]], [80000,\n\"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]], ], ) demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def tax_calculator(income, marital_status, assets):\n        tax_brackets = [(10, 0), (25, 8), (60, 12), (120, 20), (250, 30)]\n        total_deductible = sum(assets[\"Cost\"])\n        taxable_income = income - total_deductible\n    \n        total_tax = 0\n        for bracket, rate in tax_brackets:\n            if taxable_income > bracket:\n                total_tax += (taxable_income - bracket) * rate / 100\n    \n        if marital_status == \"Married\":\n            total_tax *= 0.75\n        elif marital_status == \"Divorced\":\n            total_tax *= 0.8\n    \n        return round(to", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": " - bracket) * rate / 100\n    \n        if marital_status == \"Married\":\n            total_tax *= 0.75\n        elif marital_status == \"Divorced\":\n            total_tax *= 0.8\n    \n        return round(total_tax)\n    \n    demo = gr.Interface(\n        tax_calculator,\n        [\n            \"number\",\n            gr.Radio([\"Single\", \"Married\", \"Divorced\"]),\n            gr.Dataframe(\n                headers=[\"Item\", \"Cost\"],\n                datatype=[\"str\", \"number\"],\n                label=\"Assets Purchased this Year\",\n            ),\n        ],\n        \"number\",\n        examples=[\n            [10000, \"Married\", [[\"Suit\", 5000], [\"Laptop\", 800], [\"Car\", 1800]]],\n            [80000, \"Single\", [[\"Suit\", 800], [\"Watch\", 1800], [\"Car\", 800]]],\n        ],\n    )\n    \n    demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sort_records(records): return\nrecords.sort(\"Quantity\") demo = gr.Interface( sort_records, gr.Dataframe(\nheaders=[\"Item\", \"Quantity\"], datatype=[\"str\", \"number\"], row_count=3,\ncol_count=(2, \"fixed\"), type=\"polars\" ), \"dataframe\", description=\"Sort by\nQuantity\" ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sort_records(records):\n        return records.sort(\"Quantity\")\n    \n    demo = gr.Interface(\n        sort_records,\n        gr.Dataframe(\n            headers=[\"Item\", \"Quantity\"],\n            datatype=[\"str\", \"number\"],\n            row_count=3,\n            col_count=(2, \"fixed\"),\n            type=\"polars\"\n        ),\n        \"dataframe\",\n        description=\"Sort by Quantity\"\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Dataframe component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Dataframe.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Dataframe changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Dataframe.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Dataframe.  \n`Dataframe.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Dataframe. Uses event data gradio.SelectData to carry `value` referring to the label of the Dataframe, and `selected` to refer to state of the Dataframe. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockC", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": ".components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if th", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "r_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies a", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "ool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataframe", "source_page_title": "Gradio - Dataframe Docs"}, {"text": "This class allows you to pass custom error messages to the user. You can do\nso by raising a gr.Error(\"custom message\") anywhere in the code, and when that\nline is executed the custom message will appear in a modal on the demo.\n\nYou can control for how long the error message is displayed with the\n`duration` parameter. If it\u2019s `None`, the message will be displayed forever\nuntil the user closes it. If it\u2019s a number, it will be shown for that many\nseconds.\n\nYou can also hide the error modal from being shown in the UI by setting\n`visible=False`.\n\nBelow is a demo of how different values of duration control the error,\ninfo, and warning messages. You can see the code\n[here](https://huggingface.co/spaces/freddyaboulton/gradio-error-\nduration/blob/244331cf53f6b5fa2fd406ece3bf55c6ccb9f5f2/app.pyL17).\n\n![modal_control](https://github.com/gradio-\napp/gradio/assets/41651716/f0977bcd-eaec-4eca-a2fd-ede95fdb8fd2)\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "import gradio as gr\n    def divide(numerator, denominator):\n        if denominator == 0:\n            raise gr.Error(\"Cannot divide by zero!\")\n    gr.Interface(divide, [\"number\", \"number\"], \"number\").launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    message: str\n\ndefault `= \"Error raised.\"`\n\nThe error message to be displayed to the user. Can be HTML, which will be\nrendered in the modal.\n\n\n    \n    \n    duration: float | None\n\ndefault `= 10`\n\nThe duration in seconds to display the error message. If None or 0, the error\nmessage will be displayed until the user closes it.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the error message should be displayed in the UI.\n\n\n    \n    \n    title: str\n\ndefault `= \"Error\"`\n\nThe title to be displayed to the user at the top of the error modal.\n\n\n    \n    \n    print_exception: bool\n\ndefault `= True`\n\nWhether to print traceback of the error to the console when the error is\nraised.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "calculatorblocks_chained_events\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def calculator(num1, operation, num2): if\noperation == \"add\": return num1 + num2 elif operation == \"subtract\": return\nnum1 - num2 elif operation == \"multiply\": return num1 * num2 elif operation ==\n\"divide\": if num2 == 0: raise gr.Error(\"Cannot divide by zero!\") return num1 /\nnum2 demo = gr.Interface( calculator, [ \"number\", gr.Radio([\"add\", \"subtract\",\n\"multiply\", \"divide\"]), \"number\" ], \"number\", examples=[ [45, \"add\", 3],\n[3.14, \"divide\", 2], [144, \"multiply\", 2.5], [0, \"subtract\", 1.2], ],\ntitle=\"Toy Calculator\", description=\"Here's a sample toy calculator.\", ) if\n__name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def calculator(num1, operation, num2):\n        if operation == \"add\":\n            return num1 + num2\n        elif operation == \"subtract\":\n            return num1 - num2\n        elif operation == \"multiply\":\n            return num1 * num2\n        elif operation == \"divide\":\n            if num2 == 0:\n                raise gr.Error(\"Cannot divide by zero!\")\n            return num1 / num2\n    \n    demo = gr.Interface(\n        calculator,\n        [\n            \"number\",\n            gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]),\n            \"number\"\n        ],\n        \"number\",\n        examples=[\n            [45, \"add\", 3],\n            [3.14, \"divide\", 2],\n            [144, \"multiply\", 2.5],\n            [0, \"subtract\", 1.2],\n        ],\n        title=\"Toy Calculator\",\n        description=\"Here's a sample toy calculator.\",\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def failure(): raise gr.Error(\"This should\nfail!\") def exception(): raise ValueError(\"Something went wrong\") def\nsuccess(): return True def warning_fn(): gr.Warning(\"This is a warning!\") def\ninfo_fn(): gr.Info(\"This is some info\") with gr.Blocks() as demo:\ngr.Markdown(\"Used in E2E tests of success event trigger. The then event\ncovered", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "def warning_fn(): gr.Warning(\"This is a warning!\") def\ninfo_fn(): gr.Info(\"This is some info\") with gr.Blocks() as demo:\ngr.Markdown(\"Used in E2E tests of success event trigger. The then event\ncovered in chatbot E2E tests.\" \" Also testing that the status modals show\nup.\") with gr.Row(): result = gr.Textbox(label=\"Result\") result_2 =\ngr.Textbox(label=\"Consecutive Event\") with gr.Row(): success_btn =\ngr.Button(value=\"Trigger Success\") success_btn_2 = gr.Button(value=\"Trigger\nConsecutive Success\") failure_btn = gr.Button(value=\"Trigger Failure\")\nfailure_exception = gr.Button(value=\"Trigger Failure With ValueError\") with\ngr.Row(): trigger_warning = gr.Button(value=\"Trigger Warning\") trigger_info =\ngr.Button(value=\"Trigger Info\") success_btn_2.click(success, None,\nNone).success(lambda: \"First Event Trigered\", None, result).success(lambda:\n\"Consecutive Event Triggered\", None, result_2) success_btn.click(success,\nNone, None).success(lambda: \"Success event triggered\", inputs=None,\noutputs=result) failure_btn.click(failure, None, None).success(lambda: \"Should\nnot be triggered\", inputs=None, outputs=result)\nfailure_exception.click(exception, None, None)\ntrigger_warning.click(warning_fn, None, None) trigger_info.click(info_fn,\nNone, None) if __name__ == \"__main__\": demo.launch(show_error=True)\n\n    \n    \n    import gradio as gr\n    \n    def failure():\n        raise gr.Error(\"This should fail!\")\n    \n    def exception():\n        raise ValueError(\"Something went wrong\")\n    \n    def success():\n        return True\n    \n    def warning_fn():\n        gr.Warning(\"This is a warning!\")\n    \n    def info_fn():\n        gr.Info(\"This is some info\")\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"Used in E2E tests of success event trigger. The then event covered in chatbot E2E tests.\"\n                    \" Also testing that the status modals show up.\")\n        with gr.Row():\n            result = gr.Textbox(label=\"Result\")\n            result_2 = gr.Textbox(label=\"Consecutive Event\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "\n                    \" Also testing that the status modals show up.\")\n        with gr.Row():\n            result = gr.Textbox(label=\"Result\")\n            result_2 = gr.Textbox(label=\"Consecutive Event\")\n        with gr.Row():\n            success_btn = gr.Button(value=\"Trigger Success\")\n            success_btn_2 = gr.Button(value=\"Trigger Consecutive Success\")\n            failure_btn = gr.Button(value=\"Trigger Failure\")\n            failure_exception = gr.Button(value=\"Trigger Failure With ValueError\")\n        with gr.Row():\n            trigger_warning = gr.Button(value=\"Trigger Warning\")\n            trigger_info = gr.Button(value=\"Trigger Info\")\n    \n            success_btn_2.click(success, None, None).success(lambda: \"First Event Trigered\", None, result).success(lambda: \"Consecutive Event Triggered\", None, result_2)\n            success_btn.click(success, None, None).success(lambda: \"Success event triggered\", inputs=None, outputs=result)\n            failure_btn.click(failure, None, None).success(lambda: \"Should not be triggered\", inputs=None, outputs=result)\n            failure_exception.click(exception, None, None)\n            trigger_warning.click(warning_fn, None, None)\n            trigger_info.click(info_fn, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch(show_error=True)\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/error", "source_page_title": "Gradio - Error Docs"}, {"text": "Creates a button, that when clicked, allows a user to download a single\nfile of arbitrary type.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "**As input component** : (Rarely used) passes the file as a `str` into the\nfunction.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` or `pathlib.Path` filepath\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str\n\ndefault `= \"Download\"`\n\nText to display on the button. Defaults to \"Download\".\n\n\n    \n    \n    value: str | Path | Callable | None\n\ndefault `= None`\n\nA str or pathlib.Path filepath or URL to download, or a Callable that returns\na str or pathlib.Path filepath or URL to download.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop']\n\ndefault `= \"secondary\"`\n\n'primary' for main call-to-action, 'secondary' for a more subdued style,\n'stop' for a stop button.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    int", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "ot sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nIf False, the UploadButton will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.DownloadButton` | \"downloadbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "upload_and_download\n\nOpen in \ud83c\udfa2 \u2197 from pathlib import Path import gradio as gr def\nupload_file(filepath): name = Path(filepath).name return\n[gr.UploadButton(visible=False), gr.DownloadButton(label=f\"Download {name}\",\nvalue=filepath, visible=True)] def download_file(): return\n[gr.UploadButton(visible=True), gr.DownloadButton(visible=False)] with\ngr.Blocks() as demo: gr.Markdown(\"First upload a file and and then you'll be\nable download it (but only once!)\") with gr.Row(): u = gr.UploadButton(\"Upload\na file\", file_count=\"single\") d = gr.DownloadButton(\"Download the file\",\nvisible=False) u.upload(upload_file, u, [u, d]) d.click(download_file, None,\n[u, d]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    from pathlib import Path\n    import gradio as gr\n    \n    def upload_file(filepath):\n        name = Path(filepath).name\n        return [gr.UploadButton(visible=False), gr.DownloadButton(label=f\"Download {name}\", value=filepath, visible=True)]\n    \n    def download_file():\n        return [gr.UploadButton(visible=True), gr.DownloadButton(visible=False)]\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"First upload a file and and then you'll be able download it (but only once!)\")\n        with gr.Row():\n            u = gr.UploadButton(\"Upload a file\", file_count=\"single\")\n            d = gr.DownloadButton(\"Download the file\", visible=False)\n    \n        u.upload(upload_file, u, [u, d])\n        d.click(download_file, None, [u, d])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe DownloadButton component supports the following event listeners. Each\nevent listener takes the same parameters, which are listed in the Event\nParameters table below.\n\nListener | Description  \n---|---  \n`DownloadButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the DownloadButton is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n   ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "e used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "s then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to Non", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "nents.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/downloadbutton", "source_page_title": "Gradio - Downloadbutton Docs"}, {"text": "Special component that ticks at regular intervals when active. It is not\nvisible, and only used to trigger events at a regular interval through the\n`tick` event listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "**As input component** : The interval of the timer as a float.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: float | None\n    )\n    \t...\n\n  \n\n**As output component** : The interval of the timer as a float or None.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> float | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: float\n\ndefault `= 1`\n\nInterval in seconds between each tick.\n\n\n    \n    \n    active: bool\n\ndefault `= True`\n\nWhether the timer is active.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Timer` | \"timer\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Timer component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Timer.tick(fn, \u00b7\u00b7\u00b7)` | This listener is triggered at regular intervals defined by the Timer.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n   ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "ill be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"hidden\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "ction is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "t components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/timer", "source_page_title": "Gradio - Timer Docs"}, {"text": "Creates a color picker for user to select a color as string input. Can be\nused as an input to pass a color value to a function or as an output to\ndisplay a color value.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "**As input component** : Passes selected color value as a hex `str` into\nthe function.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a hex `str` returned from function and\nsets color picker value to it.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Callable | None\n\ndefault `= None`\n\ndefault color hex code to provide in color picker. If a function is provided,\nthe function will be called each time the app loads to set the initial value\nof this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a cer", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will be rendered as an editable color picker; if False, editing will\nbe disabled. If not provided, this is inferred based on whether the component\nis used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ColorPicker` | \"colorpicker\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "color_picker\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import numpy as np from PIL import Image,\nImageColor def change_color(icon, color): \"\"\" Function that given an icon in\n.png format changes its color Args: icon: Icon whose color needs to be\nchanged. color: Chosen color with which to edit the input icon. Returns:\nedited_image: Edited icon. \"\"\" img = icon.convert(\"LA\") img =\nimg.convert(\"RGBA\") image_np = np.array(icon) _, _, _, alpha = image_np.T mask\n= alpha > 0 image_np[..., :-1][mask.T] = ImageColor.getcolor(color, \"RGB\")\nedited_image = Image.fromarray(image_np) return edited_image inputs = [\ngr.Image(label=\"icon\", type=\"pil\", image_mode=\"RGBA\"),\ngr.ColorPicker(label=\"color\"), ] outputs = gr.Image(label=\"colored icon\") demo\n= gr.Interface( fn=change_color, inputs=inputs, outputs=outputs ) if __name__\n== \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import numpy as np\n    from PIL import Image, ImageColor\n    \n    def change_color(icon, color):\n    \n        \"\"\"\n        Function that given an icon in .png format changes its color\n        Args:\n            icon: Icon whose color needs to be changed.\n            color: Chosen color with which to edit the input icon.\n        Returns:\n            edited_image: Edited icon.\n        \"\"\"\n        img = icon.convert(\"LA\")\n        img = img.convert(\"RGBA\")\n        image_np = np.array(icon)\n        _, _, _, alpha = image_np.T\n        mask = alpha > 0\n        image_np[..., :-1][mask.T] = ImageColor.getcolor(color, \"RGB\")\n        edited_image = Image.fromarray(image_np)\n        return edited_image\n    \n    inputs = [\n        gr.Image(label=\"icon\", type=\"pil\", image_mode=\"RGBA\"),\n        gr.ColorPicker(label=\"color\"),\n    ]\n    outputs = gr.Image(label=\"colored icon\")\n    \n    demo = gr.Interface(\n        fn=change_color,\n        inputs=inputs,\n        outputs=outputs\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ColorPicker component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ColorPicker.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the ColorPicker changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`ColorPicker.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the ColorPicker.  \n`ColorPicker.submit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses the Enter key while the ColorPicker is focused.  \n`ColorPicker.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the ColorPicker is focused.  \n`ColorPicker.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the ColorPicker is unfocused/blurred.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | Bloc", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will pla", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "ent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions whi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": " to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/colorpicker", "source_page_title": "Gradio - Colorpicker Docs"}, {"text": "Creates a button that can be assigned arbitrary .click() events. The value\n(label) of the button can be used as an input to the function (rarely used) or\nset via the output of a function.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "**As input component** : (Rarely used) the `str` corresponding to the\nbutton label when the button is clicked\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : string corresponding to the button label\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | I18nData | Callable\n\ndefault `= \"Run\"`\n\ndefault text for the button to display. If a function is provided, the\nfunction will be called each time the app loads to set the initial value of\nthis component.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinuously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop', 'huggingface']\n\ndefault `= \"secondary\"`\n\nsets the background and text color of the button. Use 'primary' for main call-\nto-action buttons, 'secondary' for a more subdued style, 'stop' for a stop\nbutton, 'huggingface' for a black background with white text, consistent with\nHugging Face's button styles.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | Path | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    link: str | None\n\ndefault `= None`\n\nURL to open when the button is clicked. If None, no link will be used.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nif False, the Button will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": " is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Button` | \"button\" | Uses default values  \n`gradio.ClearButton` | \"clearbutton\" | Uses default values  \n`gradio.DeepLinkButton` | \"deeplinkbutton\" | Uses default values  \n`gradio.DuplicateButton` | \"duplicatebutton\" | Uses default values  \n`gradio.LoginButton` | \"loginbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Button component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Button.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Button is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "nt. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number o", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "urn a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "oncurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/button", "source_page_title": "Gradio - Button Docs"}, {"text": "Creates a button that redirects the user to Sign with Hugging Face using\nOAuth. If created inside of a Blocks context, it will add an event to check if\nthe user is logged in and update the button text accordingly. If created\noutside of a Blocks context, call the `LoginButton.activate()` method to add\nthe event.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "**As input component** : (Rarely used) the `str` corresponding to the\nbutton label when the button is clicked\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : string corresponding to the button label\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str\n\ndefault `= \"Sign in with Hugging Face\"`\n\n\n    \n    \n    logout_value: str\n\ndefault `= \"Logout ({})\"`\n\nThe text to display when the user is signed in. The string should contain a\nplaceholder for the username with a call-to-action to logout, e.g. \"Logout\n({})\".\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop', 'huggingface']\n\ndefault `= \"huggingface\"`\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\n\n    \n    \n    icon: str | Path | None\n\ndefault `= \"/home/runner/work/gradio/gradio/gradio/icons/huggingface-\nlogo.svg\"`\n\n\n    \n    \n    link: str | None\n\ndefault `= None`\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.LoginButton` | \"loginbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe LoginButton component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`LoginButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Button is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_descripti", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximu", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "ed* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no c", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/loginbutton", "source_page_title": "Gradio - Loginbutton Docs"}, {"text": "Creates an image component that can be used to upload images (as an input)\nor display images (as an output).\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "**As input component** : A `str` containing the path to the image.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` or `pathlib.Path` object\ncontaining the path to the image.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | None\n\ndefault `= None`\n\nA path or URL for the default value that SimpleImage component is going to\ntake. If a function is provided, the function will be called each time the app\nloads to set the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_download_button: bool\n\ndefault `= True`\n\nIf True, will display button to download image.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "ll_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload and edit an image; if False, can only be\nused to display images. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": " user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.SimpleImage` | \"simpleimage\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe SimpleImage component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`SimpleImage.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the SimpleImage using the clear button for the component.  \n`SimpleImage.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the SimpleImage changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`SimpleImage.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the SimpleImage.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "ponent | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "rue, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second subm", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "bmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "n @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/simpleimage", "source_page_title": "Gradio - Simpleimage Docs"}, {"text": "Creates an image component that can be used to upload images (as an input)\nor display images (as an output).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "**As input component** : Passes the uploaded image as a tuple of\n`numpy.array`, `PIL.Image` or `str` filepath depending on `type`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: tuple[\n        str | PIL.Image.Image | np.ndarray | None, str | PIL.Image.Image | np.ndarray | None\n    ]\n    )\n    \t...\n\n  \n\n**As output component** : Expects a tuple of `numpy.array`, `PIL.Image`, or\n`str` or `pathlib.Path` filepath to an image which is displayed.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> np.ndarray | PIL.Image.Image | str | Path | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: image_tuple | Callable | None\n\ndefault `= None`\n\nA tuple of PIL Image, numpy array, path or URL for the default value that\nImageSlider component is going to take, this pair of images should be of equal\nsize. If a function is provided, the function will be called each time the app\nloads to set the initial value of this component.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFile format (e.g. \"png\" or \"gif\"). Used to save image if it does not already\nhave a valid format (e.g. if the image is being returned to the frontend as a\nnumpy array or PIL Image). The format should be supported by the PIL library.\nApplies both when this component is used as an input or output. This parameter\nhas no effect on SVG files.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed tuple\nof image file or numpy array, but will affect the displayed image.\n\n\n    \n    \n    width: int | str | None\n\ndefault `= None`\n\nThe width of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. This has no effect on the preprocessed tuple\nof image file or numpy array, but will affect the displayed image.\n\n\n    \n    \n    image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None\n\ndefault `= \"RGB\"`\n\nThe pixel format and color depth that the image should be loaded and\npreprocessed as. \"RGB\" will load the image as a color image, or \"L\" as black-\nand-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html\nfor other supported image modes and their meaning. This parameter has no\neffect on SVG or GIF files. If set to None, the image_mode will be inferred\nfrom the image file types (e.g. \"RGBA\" for a .png image, \"RGB\" in most other\ncases).\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe for", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "image_mode will be inferred\nfrom the image file types (e.g. \"RGBA\" for a .png image, \"RGB\" in most other\ncases).\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe format the images are converted to before being passed into the prediction\nfunction. \"numpy\" converts the images to numpy arrays with shape (height,\nwidth, 3) and values from 0 to 255, \"pil\" converts the images to PIL image\nobjects, \"filepath\" passes str paths to temporary files containing the images.\nTo support animated GIFs in input, the `type` should be set to \"filepath\" or\n\"pil\". To support SVGs, the `type` should be set to \"filepath\".\n\n\n    \n    \n    label: str | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    show_download_button: bool\n\ndefault `= True`\n\nIf True, will display button to download image. Only applies if interactive is\nFalse (e.g. if the component is used as an output).\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Comp", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload and edit an image; if False, can only be\nused to display images. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parame", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "e component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a fullscreen icon in the corner of the component that\nallows user to view the image in fullscreen mode. If False, icon does not\nappear.\n\n\n    \n    \n    slider_position: float\n\ndefault `= 50`\n\nThe position of the slider as a percentage of the width of the image, between\n0 and 100.\n\n\n    \n    \n    max_height: int\n\ndefault `= 500`\n\nThe maximum height of the image.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.ImageSlider` | \"imageslider\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "imageslider\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr from PIL import ImageFilter def\nimg_to_slider(im): if not im: return im return (im,\nim.filter(filter=ImageFilter.GaussianBlur(radius=10))) def slider_to_self(im):\nif not im or not im[0]: return im return (im[0],\nim[0].filter(filter=ImageFilter.GaussianBlur(radius=10))) def\nslider_to_self_two(im): return im def position_to_slider(pos): return\ngr.ImageSlider(slider_position=pos) with gr.Blocks() as demo: gr.Markdown(\"\nimg to image slider\") with gr.Row(): img1 = gr.Image(label=\"Blur image\",\ntype=\"pil\") img2 = gr.ImageSlider(label=\"Blur image\", type=\"pil\") btn =\ngr.Button(\"Blur image\") btn.click(img_to_slider, inputs=img1, outputs=img2)\ngr.Markdown(\"unified image slider\") with gr.Row(): img3 =\ngr.ImageSlider(label=\"Blur image\", type=\"pil\") img3.upload(slider_to_self,\ninputs=img3, outputs=img3) pos = gr.Slider(label=\"Position\", value=50,\nminimum=0, maximum=100, step=0.01) pos.change(position_to_slider, inputs=pos,\noutputs=img3, show_progress=\"hidden\") if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    from PIL import ImageFilter\n    \n    \n    def img_to_slider(im):\n        if not im:\n            return im\n        return (im, im.filter(filter=ImageFilter.GaussianBlur(radius=10)))\n    \n    \n    def slider_to_self(im):\n        if not im or not im[0]:\n            return im\n        return (im[0], im[0].filter(filter=ImageFilter.GaussianBlur(radius=10)))\n    \n    \n    def slider_to_self_two(im):\n        return im\n    \n    \n    def position_to_slider(pos):\n        return gr.ImageSlider(slider_position=pos)\n    \n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"img to image slider\")\n        with gr.Row():\n            img1 = gr.Image(label=\"Blur image\", type=\"pil\")\n            img2 = gr.ImageSlider(label=\"Blur image\", type=\"pil\")\n        btn = gr.Button(\"Blur image\")\n        btn.click(img_to_slider, inputs=img1, outputs=img2)\n        gr.Markdown(\"unified image slider\")\n        with gr.Row()", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "r(label=\"Blur image\", type=\"pil\")\n        btn = gr.Button(\"Blur image\")\n        btn.click(img_to_slider, inputs=img1, outputs=img2)\n        gr.Markdown(\"unified image slider\")\n        with gr.Row():\n            img3 = gr.ImageSlider(label=\"Blur image\", type=\"pil\")\n            img3.upload(slider_to_self, inputs=img3, outputs=img3)\n        pos = gr.Slider(label=\"Position\", value=50, minimum=0, maximum=100, step=0.01)\n        pos.change(position_to_slider, inputs=pos, outputs=img3, show_progress=\"hidden\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe ImageSlider component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`ImageSlider.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the ImageSlider using the clear button for the component.  \n`ImageSlider.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the ImageSlider changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`ImageSlider.stream(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user streams the ImageSlider.  \n`ImageSlider.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the ImageSlider. Uses event data gradio.SelectData to carry `value` referring to the label of the ImageSlider, and `selected` to refer to state of the ImageSlider. See EventData documentation on how to use this event data  \n`ImageSlider.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the ImageSlider.  \n`ImageSlider.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the ImageSlider.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one out", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hi", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "ss animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functio", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "ts to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automati", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "thod of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/imageslider", "source_page_title": "Gradio - Imageslider Docs"}, {"text": "Creates a gallery or table to display data samples. This component is\nprimarily designed for internal use to display examples. However, it can also\nbe used directly to display a dataset and let users select examples.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "**As input component** : Passes the selected sample either as a `list` of\ndata corresponding to each input component (if `type` is \"value\") or as an\n`int` index (if `type` is \"index\"), or as a `tuple` of the index and the data\n(if `type` is \"tuple\").\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: int | list | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects an `int` index or `list` of sample data.\nReturns the index of the sample in the dataset or `None` if the sample is not\nfound.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> list[list]\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, appears above the component.\n\n\n    \n    \n    show_label: bool\n\ndefault `= True`\n\nIf True, the label will be shown above the component.\n\n\n    \n    \n    components: list[Component] | list[str] | None\n\ndefault `= None`\n\nWhich component types to show in this dataset widget, can be passed in as a\nlist of string names or Components instances. The following components are\nsupported in a Dataset: Audio, Checkbox, CheckboxGroup, ColorPicker,\nDataframe, Dropdown, File, HTML, Image, Markdown, Model3D, Number, Radio,\nSlider, Textbox, TimeSeries, Video\n\n\n    \n    \n    component_props: list[dict[str, Any]] | None\n\ndefault `= None`\n\n\n    \n    \n    samples: list[list[Any]] | None\n\ndefault `= None`\n\na nested list of samples. Each sublist within the outer list represents a data\nsample, and each element within the sublist represents an value for each\ncomponent\n\n\n    \n    \n    headers: list[str] | None\n\ndefault `= None`\n\nColumn headers in the Dataset widget, should be the same len as components. If\nnot provided, inferred from component labels\n\n\n    \n    \n    type: Literal['values', 'index', 'tuple']\n\ndefault `= \"values\"`\n\n\"values\" if clicking on a sample should pass the value of the sample, \"index\"\nif it should pass the index of the sample, or \"tuple\" if it should pass both\nthe index and the value of the sample.\n\n\n    \n    \n    layout: Literal['gallery', 'table'] | None\n\ndefault `= None`\n\n\"gallery\" if the dataset should be displayed as a gallery with each sample in\na clickable card, or \"table\" if it should be displayed as a table with each\nsample in a row. By default, \"gallery\" is used if there is a single component,\nand \"table\" is used if there are more than one component. If there are more\nthan one component, the layout can only be \"table\".\n\n\n    \n    \n    samples_per_page: int\n\ndefault `= 10`\n\nhow many examples to show per page.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "re more\nthan one component, the layout can only be \"table\".\n\n\n    \n    \n    samples_per_page: int\n\ndefault `= 10`\n\nhow many examples to show per page.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n  ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": " in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    proxy_url: str | None\n\ndefault `= None`\n\nThe URL of the external Space used to load this component. Set automatically\nwhen using `gr.load()`. This should not be set manually.\n\n\n    \n    \n    sample_labels: list[str] | None\n\ndefault `= None`\n\nA list of labels for each sample. If provided, the length of this list should\nbe the same as the number of samples, and these labels will be used in the UI\ninstead of rendering the sample values.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Dataset` | \"dataset\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "**Updating a Dataset**\n\nIn this example, we display a text dataset using `gr.Dataset` and then update\nit when the user clicks a button:\n\n    \n    \n    import gradio as gr\n    \n    philosophy_quotes = [\n        [\"I think therefore I am.\"],\n        [\"The unexamined life is not worth living.\"]\n    ]\n    \n    startup_quotes = [\n        [\"Ideas are easy. Implementation is hard\"],\n        [\"Make mistakes faster.\"]\n    ]\n    \n    def show_startup_quotes():\n        return gr.Dataset(samples=startup_quotes)\n    \n    with gr.Blocks() as demo:\n        textbox = gr.Textbox()\n        dataset = gr.Dataset(components=[textbox], samples=philosophy_quotes)\n        button = gr.Button()\n    \n        button.click(show_startup_quotes, None, dataset)\n    \n    demo.launch()\n\n", "heading1": "Examples", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Dataset component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Dataset.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the Dataset is clicked.  \n`Dataset.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Dataset. Uses event data gradio.SelectData to carry `value` referring to the label of the Dataset, and `selected` to refer to state of the Dataset. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API doc", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of '", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "r the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dataset", "source_page_title": "Gradio - Dataset Docs"}, {"text": "Creates a dropdown of choices from which a single entry or multiple entries\ncan be selected (as an input component) or displayed (as an output component).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "**As input component** : Passes the value of the selected dropdown choice as a `str | int | float` or its index as an `int` into the function, depending on `type`. Or, if `multiselect` is True, passes the values of the selected dropdown choices as a list of corresponding values/indices instead.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | int | float | list[str | int | float] | list[int | None] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str | int | float` corresponding to the value of the dropdown entry to be selected. Or, if `multiselect` is True, expects a `list` of values corresponding to the selected dropdown entries.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | int | float | list[str | int | float] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    choices: list[str | int | float | tuple[str, str | int | float]] | None\n\ndefault `= None`\n\na list of string or numeric options to choose from. An option can also be a\ntuple of the form (name, value), where name is the displayed name of the\ndropdown choice and value is the value to be passed to the function, or\nreturned by the function.\n\n\n    \n    \n    value: str | int | float | list[str | int | float] | Callable | DefaultValue | None\n\ndefault `= DefaultValue()`\n\nthe value selected in dropdown. If `multiselect` is true, this should be list,\notherwise a single string or number from among `choices`. By default, the\nfirst choice in `choices` is initally selected. If set explicitly to None, no\nvalue is initally selected. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    type: Literal['value', 'index']\n\ndefault `= \"value\"`\n\ntype of value to be returned by component. \"value\" returns the string of the\nchoice selected, \"index\" returns the index of the choice selected.\n\n\n    \n    \n    multiselect: bool | None\n\ndefault `= None`\n\nif True, multiple choices can be selected.\n\n\n    \n    \n    allow_custom_value: bool\n\ndefault `= False`\n\nif True, allows user to enter a custom value that is not in the list of\nchoices.\n\n\n    \n    \n    max_choices: int | None\n\ndefault `= None`\n\nmaximum number of choices that can be selected. If None, no limit is enforced.\n\n\n    \n    \n    filterable: bool\n\ndefault `= True`\n\nif True, user will be able to type into the dropdown and filter the choices by\ntyping. Can only be set to False if `allow_custom_value` is False.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component, displayed above the component if `show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this compon", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "`show_label` is\n`True` and is also used as the header if there are a table of examples for\nthis component. If None and used in a `gr.Interface`, the label will be the\nname of the parameter this component corresponds to.\n\n\n    \n    \n    info: str | I18nData | None\n\ndefault `= None`\n\nadditional component description, appears below the label in smaller font.\nSupports markdown / HTML syntax.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\ncontinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\ncomponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nif True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, choices in this dropdown will be selectable; if False, selection will\nbe disabled. If not provided, this is inferred based on whether the component\nis used as an input or output.\n\n\n    \n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "ne`\n\nif True, choices in this dropdown will be selectable; if False, selection will\nbe disabled. If not provided, this is inferred based on whether the component\nis used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nif False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nan optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nan optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not be rendered in the Blocks context. Should be used\nif the intention is to assign event listeners now but render the component\nlater.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Dropdown` | \"dropdown\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "sentence_builder\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def sentence_builder(quantity, animal,\ncountries, place, activity_list, morning): return f\"\"\"The {quantity} {animal}s\nfrom {\" and \".join(countries)} went to the {place} where they {\" and\n\".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\" demo =\ngr.Interface( sentence_builder, [ gr.Slider(2, 20, value=4, label=\"Count\",\ninfo=\"Choose between 2 and 20\"), gr.Dropdown( [\"cat\", \"dog\", \"bird\"],\nlabel=\"Animal\", info=\"Will add more animals later!\" ),\ngr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where\nare they from?\"), gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\",\ninfo=\"Where did they go?\"), gr.Dropdown( [\"ran\", \"swam\", \"ate\", \"slept\"],\nvalue=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum\ndolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies\naliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\" ),\ngr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"), ],\n\"text\", examples=[ [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"],\nTrue], [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False], [10, \"bird\",\n[\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False], [8, \"cat\", [\"Pakistan\"], \"zoo\",\n[\"ate\"], True], ] ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def sentence_builder(quantity, animal, countries, place, activity_list, morning):\n        return f\"\"\"The {quantity} {animal}s from {\" and \".join(countries)} went to the {place} where they {\" and \".join(activity_list)} until the {\"morning\" if morning else \"night\"}\"\"\"\n    \n    demo = gr.Interface(\n        sentence_builder,\n        [\n            gr.Slider(2, 20, value=4, label=\"Count\", info=\"Choose between 2 and 20\"),\n            gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "  gr.Dropdown(\n                [\"cat\", \"dog\", \"bird\"], label=\"Animal\", info=\"Will add more animals later!\"\n            ),\n            gr.CheckboxGroup([\"USA\", \"Japan\", \"Pakistan\"], label=\"Countries\", info=\"Where are they from?\"),\n            gr.Radio([\"park\", \"zoo\", \"road\"], label=\"Location\", info=\"Where did they go?\"),\n            gr.Dropdown(\n                [\"ran\", \"swam\", \"ate\", \"slept\"], value=[\"swam\", \"slept\"], multiselect=True, label=\"Activity\", info=\"Lorem ipsum dolor sit amet, consectetur adipiscing elit. Sed auctor, nisl eget ultricies aliquam, nunc nisl aliquet nunc, eget aliquam nisl nunc vel nisl.\"\n            ),\n            gr.Checkbox(label=\"Morning\", info=\"Did they do it in the morning?\"),\n        ],\n        \"text\",\n        examples=[\n            [2, \"cat\", [\"Japan\", \"Pakistan\"], \"park\", [\"ate\", \"swam\"], True],\n            [4, \"dog\", [\"Japan\"], \"zoo\", [\"ate\", \"swam\"], False],\n            [10, \"bird\", [\"USA\", \"Pakistan\"], \"road\", [\"ran\"], False],\n            [8, \"cat\", [\"Pakistan\"], \"zoo\", [\"ate\"], True],\n        ]\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Dropdown component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Dropdown.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Dropdown changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Dropdown.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Dropdown.  \n`Dropdown.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Dropdown. Uses event data gradio.SelectData to carry `value` referring to the label of the Dropdown, and `selected` to refer to state of the Dropdown. See EventData documentation on how to use this event data  \n`Dropdown.focus(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Dropdown is focused.  \n`Dropdown.blur(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the Dropdown is unfocused/blurred.  \n`Dropdown.key_up(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user presses a key while the Dropdown is focused.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Compone", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "nd the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Com", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "utput component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled,", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "ick_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": " to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/dropdown", "source_page_title": "Gradio - Dropdown Docs"}, {"text": "Tab (or its alias TabItem) is a layout element. Components defined within\nthe Tab will be visible when this tab is selected tab.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "with gr.Blocks() as demo:\n        with gr.Tab(\"Lion\"):\n            gr.Image(\"lion.jpg\")\n            gr.Button(\"New Lion\")\n        with gr.Tab(\"Tiger\"):\n            gr.Image(\"tiger.jpg\")\n            gr.Button(\"New Tiger\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe visual label for the tab\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, Tab will be hidden.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nIf False, Tab will not be clickable.\n\n\n    \n    \n    id: int | str | None\n\ndefault `= None`\n\nAn optional identifier for the tab, required if you wish to control the\nselected tab from a predict function.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of the <div> containing the\ncontents of the Tab layout. The same string followed by \"-button\" is attached\nto the Tab button. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent elements. 1 or greater indicates the Tab\nwill expand in size.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Tab.select(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nEvent listener for when the user selects or deselects the Tab. Uses event data\ngradio.SelectData to carry `value` referring to the label of the Tab, and\n`selected` to refer to state of the Tab. See EventData documentation on how to\nuse this event data\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ni", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "eral['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner w", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "put component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nse", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "g 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "\n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "select", "source_page_url": "https://gradio.app/docs/gradio/tab", "source_page_title": "Gradio - Tab Docs"}, {"text": "Row is a layout element within Blocks that renders all children\nhorizontally.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/row", "source_page_title": "Gradio - Row Docs"}, {"text": "with gr.Blocks() as demo:\n        with gr.Row():\n            gr.Image(\"lion.jpg\", scale=2)\n            gr.Image(\"tiger.jpg\", scale=1)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/row", "source_page_title": "Gradio - Row Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    variant: Literal['default', 'panel', 'compact']\n\ndefault `= \"default\"`\n\nrow type, 'default' (no background), 'panel' (gray background color and\nrounded corners), or 'compact' (rounded corners and no internal gap).\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, row will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative height compared to adjacent elements. 1 or greater indicates the Row\nwill expand in height, and any child columns will also expand to fill the\nheight.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe height of the row, specified in pixels if a number is passed, or in CSS\nunits if a string is passed. If content exceeds the height, the row will\nscroll vertically. If not set, the row will expand to fit the content.\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= None`\n\nThe maximum height of the row, specified in pixels if a number is passed, or\nin CSS units if a string is passed. If content exceeds the height, the row\nwill scroll vertically. If content is shorter than the height, the row will\nshrink to fit the content. Will not have any effect if `height` is set and is\nsmaller than `max_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the row, specified in pixels if a number is passed, or\nin CSS units if a string is passed. If content e", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/row", "source_page_title": "Gradio - Row Docs"}, {"text": "x_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the row, specified in pixels if a number is passed, or\nin CSS units if a string is passed. If content exceeds the height, the row\nwill expand to fit the content. Will not have any effect if `height` is set\nand is larger than `min_height`.\n\n\n    \n    \n    equal_height: bool\n\ndefault `= False`\n\nIf True, makes every child element have equal height\n\n\n    \n    \n    show_progress: bool\n\ndefault `= False`\n\nIf True, shows progress animation when being updated.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/row", "source_page_title": "Gradio - Row Docs"}, {"text": "Creates a bar plot component to display data from a pandas DataFrame.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "**As input component** : The data to display in a line plot.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: AltairPlotData\n    )\n    \t...\n\n  \n\n**As output component** : Expects a pandas DataFrame containing the data to\ndisplay in the line plot. The DataFrame should contain at least two columns,\none for the x-axis (corresponding to this component's `x` argument) and one\nfor the y-axis (corresponding to `y`).\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> pd.DataFrame | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: pd.DataFrame | Callable | None\n\ndefault `= None`\n\nThe pandas dataframe containing the data to display in the plot.\n\n\n    \n    \n    x: str | None\n\ndefault `= None`\n\nColumn corresponding to the x axis. Column can be numeric, datetime, or\nstring/category.\n\n\n    \n    \n    y: str | None\n\ndefault `= None`\n\nColumn corresponding to the y axis. Column must be numeric.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nColumn corresponding to series, visualized by color. Column must be\nstring/category.\n\n\n    \n    \n    title: str | None\n\ndefault `= None`\n\nThe title to display on top of the chart.\n\n\n    \n    \n    x_title: str | None\n\ndefault `= None`\n\nThe title given to the x axis. By default, uses the value of the x parameter.\n\n\n    \n    \n    y_title: str | None\n\ndefault `= None`\n\nThe title given to the y axis. By default, uses the value of the y parameter.\n\n\n    \n    \n    color_title: str | None\n\ndefault `= None`\n\nThe title given to the color legend. By default, uses the value of color\nparameter.\n\n\n    \n    \n    x_bin: str | float | None\n\ndefault `= None`\n\nGrouping used to cluster x values. If x column is numeric, should be number to\nbin the x values. If x column is datetime, should be string such as \"1h\",\n\"15m\", \"10s\", using \"s\", \"m\", \"h\", \"d\" suffixes.\n\n\n    \n    \n    y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None\n\ndefault `= None`\n\nAggregation function used to aggregate y values, used if x_bin is provided or\nx is a string/category. Must be one of \"sum\", \"mean\", \"median\", \"min\", \"max\".\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nMapping of series to color names or codes. For example, {\"success\": \"green\",\n\"fail\": \"FF8888\"}.\n\n\n    \n    \n    x_lim: list[float] | None\n\ndefault `= None`\n\nA tuple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "ple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA tuple of list containing the limits for the y-axis, specified as [y_min,\ny_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum to value.\n\n\n    \n    \n    x_label_angle: float\n\ndefault `= 0`\n\nThe angle of the x-axis labels in degrees offset clockwise.\n\n\n    \n    \n    y_label_angle: float\n\ndefault `= 0`\n\nThe angle of the y-axis labels in degrees offset clockwise.\n\n\n    \n    \n    x_axis_labels_visible: bool\n\ndefault `= True`\n\nWhether the x-axis labels should be visible. Can be hidden when many x-axis\nlabels are present.\n\n\n    \n    \n    caption: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) caption to display below the plot.\n\n\n    \n    \n    sort: Literal['x', 'y', '-x', '-y'] | list[str] | None\n\ndefault `= None`\n\nThe sorting order of the x values, if x column is type string/category. Can be\n\"x\", \"y\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n    \n    \n    tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hovering on a point. \"axis\" shows the values for\nthe axis columns, \"all\" shows all column values, and \"none\" shows no tooltips.\nCan also provide a list of strings representing columns to show in the\ntooltip, which will be displayed along with axis values.\n\n\n    \n    \n    height: int | None\n\ndefault `= None`\n\nThe height of the plot in pixels.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) label to display on the top left corner of the plot.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nWhether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Whether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the plot should be visible.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n  ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "default `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True, will show a button to make plot visible in fullscreen mode.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.BarPlot` | \"barplot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "bar_plot_demo\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd from random import randint, random import\ngradio as gr temp_sensor_data = pd.DataFrame( { \"time\":\npd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200), \"temperature\":\n[randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n\"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in\nrange(200)], \"location\": [\"indoor\", \"outdoor\"] * 100, } ) food_rating_data =\npd.DataFrame( { \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in\nrange(100)], \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n\"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)], \"wait\":\n[random() for i in range(100)], } ) with gr.Blocks() as bar_plots: with\ngr.Row(): start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\") end =\ngr.DateTime(\"2021-01-05 00:00:00\", label=\"End\") apply_btn = gr.Button(\"Apply\",\nscale=0) with gr.Row(): group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"],\nvalue=\"None\", label=\"Group by\") aggregate = gr.Radio([\"sum\", \"mean\", \"median\",\n\"min\", \"max\"], value=\"sum\", label=\"Aggregation\") temp_by_time = gr.BarPlot(\ntemp_sensor_data, x=\"time\", y=\"temperature\", ) temp_by_time_location =\ngr.BarPlot( temp_sensor_data, x=\"time\", y=\"temperature\", color=\"location\", )\ntime_graphs = [temp_by_time, temp_by_time_location] group_by.change( lambda\ngroup: [gr.BarPlot(x_bin=None if group == \"None\" else group)] *\nlen(time_graphs), group_by, time_graphs ) aggregate.change( lambda aggregate:\n[gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs), aggregate, time_graphs\n) def rescale(select: gr.SelectData): return select.index rescale_evt =\ngr.on([plot.select for plot in time_graphs], rescale, None, [start, end]) for\ntrigger in [apply_btn.click, rescale_evt.then]: trigger( lambda start, end:\n[gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n) with gr.Row(): price_by_cuisine = gr.BarPlot( food_rating_data, x=\"cuisine\",\ny=\"price\", ) with gr.Column(scale=0): gr.Button(\"Sort $", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n) with gr.Row(): price_by_cuisine = gr.BarPlot( food_rating_data, x=\"cuisine\",\ny=\"price\", ) with gr.Column(scale=0): gr.Button(\"Sort $ > $$$\").click(lambda:\ngr.BarPlot(sort=\"y\"), None, price_by_cuisine) gr.Button(\"Sort $$$ >\n$\").click(lambda: gr.BarPlot(sort=\"-y\"), None, price_by_cuisine)\ngr.Button(\"Sort A > Z\").click(lambda: gr.BarPlot(sort=[\"Chinese\", \"Italian\",\n\"Mexican\"]), None, price_by_cuisine) with gr.Row(): price_by_rating =\ngr.BarPlot( food_rating_data, x=\"rating\", y=\"price\", x_bin=1, )\nprice_by_rating_color = gr.BarPlot( food_rating_data, x=\"rating\", y=\"price\",\ncolor=\"cuisine\", x_bin=1, color_map={\"Italian\": \"red\", \"Mexican\": \"green\",\n\"Chinese\": \"blue\"}, ) if __name__ == \"__main__\": bar_plots.launch()\n\n    \n    \n    import pandas as pd\n    from random import randint, random\n    import gradio as gr\n    \n    \n    temp_sensor_data = pd.DataFrame(\n        {\n            \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n            \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"location\": [\"indoor\", \"outdoor\"] * 100,\n        }\n    )\n    \n    food_rating_data = pd.DataFrame(\n        {\n            \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n            \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n            \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n            \"wait\": [random() for i in range(100)],\n        }\n    )\n    \n    with gr.Blocks() as bar_plots:\n        with gr.Row():\n            start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n            end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n            apply_btn = gr.Button(\"Apply\", scale=0)\n        with gr.Row():\n            group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Gro", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "021-01-05 00:00:00\", label=\"End\")\n            apply_btn = gr.Button(\"Apply\", scale=0)\n        with gr.Row():\n            group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n            aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n    \n        temp_by_time = gr.BarPlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n        )\n        temp_by_time_location = gr.BarPlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n            color=\"location\",\n        )\n    \n        time_graphs = [temp_by_time, temp_by_time_location]\n        group_by.change(\n            lambda group: [gr.BarPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n            group_by,\n            time_graphs\n        )\n        aggregate.change(\n            lambda aggregate: [gr.BarPlot(y_aggregate=aggregate)] * len(time_graphs),\n            aggregate,\n            time_graphs\n        )\n    \n        def rescale(select: gr.SelectData):\n            return select.index\n        rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])\n    \n        for trigger in [apply_btn.click, rescale_evt.then]:\n            trigger(\n                lambda start, end: [gr.BarPlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n            )\n    \n        with gr.Row():\n            price_by_cuisine = gr.BarPlot(\n                food_rating_data,\n                x=\"cuisine\",\n                y=\"price\",\n            )\n            with gr.Column(scale=0):\n                gr.Button(\"Sort $ > $$$\").click(lambda: gr.BarPlot(sort=\"y\"), None, price_by_cuisine)\n                gr.Button(\"Sort $$$ > $\").click(lambda: gr.BarPlot(sort=\"-y\"), None, price_by_cuisine)\n                gr.Button(\"Sort A > Z\").click(lambda: gr.BarPlot(sort=[\"Chinese\", \"Italian\", \"Mexican\"]), None, price_by_cuisine)\n    \n        with gr.Row():\n      ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "sort=\"-y\"), None, price_by_cuisine)\n                gr.Button(\"Sort A > Z\").click(lambda: gr.BarPlot(sort=[\"Chinese\", \"Italian\", \"Mexican\"]), None, price_by_cuisine)\n    \n        with gr.Row():\n            price_by_rating = gr.BarPlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                x_bin=1,\n            )\n            price_by_rating_color = gr.BarPlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                color=\"cuisine\",\n                x_bin=1,\n                color_map={\"Italian\": \"red\", \"Mexican\": \"green\", \"Chinese\": \"blue\"},\n            )\n    \n    if __name__ == \"__main__\":\n        bar_plots.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe BarPlot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`BarPlot.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the NativePlot. Uses event data gradio.SelectData to carry `value` referring to the label of the NativePlot, and `selected` to refer to state of the NativePlot. See EventData documentation on how to use this event data  \n`BarPlot.double_click(fn, \u00b7\u00b7\u00b7)` | Triggered when the NativePlot is double clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": " list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each pa", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "w a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/barplot", "source_page_title": "Gradio - Barplot Docs"}, {"text": "The gr.CopyData class is a subclass of gr.EventData that specifically\ncarries information about the `.copy()` event. When gr.CopyData is added as a\ntype hint to an argument of an event listener method, a gr.CopyData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/copydata", "source_page_title": "Gradio - Copydata Docs"}, {"text": "import gradio as gr\n    def on_copy(copy_data: gr.CopyData):\n        return f\"Copied text: {copy_data.value}\"\n    with gr.Blocks() as demo:\n        textbox = gr.Textbox(\"Hello World!\")\n        copied = gr.Textbox()\n        textbox.copy(on_copy, None, copied)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/copydata", "source_page_title": "Gradio - Copydata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: Any\n\nThe value that was copied.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/copydata", "source_page_title": "Gradio - Copydata Docs"}, {"text": "The gr.DownloadData class is a subclass of gr.EventData that specifically\ncarries information about the `.download()` event. When gr.DownloadData is\nadded as a type hint to an argument of an event listener method, a\ngr.DownloadData object will automatically be passed as the value of that\nargument. The attributes of this object contains information about the event\nthat triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/downloaddata", "source_page_title": "Gradio - Downloaddata Docs"}, {"text": "import gradio as gr\n    def on_download(download_data: gr.DownloadData):\n        return f\"Downloaded file: {download_data.file.path}\"\n    with gr.Blocks() as demo:\n        files = gr.File()\n        textbox = gr.Textbox()\n        files.download(on_download, None, textbox)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/downloaddata", "source_page_title": "Gradio - Downloaddata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    file: FileData\n\nThe file that was downloaded, as a FileData object.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/downloaddata", "source_page_title": "Gradio - Downloaddata Docs"}, {"text": "Creates a line plot component to display data from a pandas DataFrame.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "**As input component** : The data to display in a line plot.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: AltairPlotData | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a pandas DataFrame containing the data to\ndisplay in the line plot. The DataFrame should contain at least two columns,\none for the x-axis (corresponding to this component's `x` argument) and one\nfor the y-axis (corresponding to `y`).\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> pd.DataFrame | dict | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: pd.DataFrame | Callable | None\n\ndefault `= None`\n\nThe pandas dataframe containing the data to display in the plot.\n\n\n    \n    \n    x: str | None\n\ndefault `= None`\n\nColumn corresponding to the x axis. Column can be numeric, datetime, or\nstring/category.\n\n\n    \n    \n    y: str | None\n\ndefault `= None`\n\nColumn corresponding to the y axis. Column must be numeric.\n\n\n    \n    \n    color: str | None\n\ndefault `= None`\n\nColumn corresponding to series, visualized by color. Column must be\nstring/category.\n\n\n    \n    \n    title: str | None\n\ndefault `= None`\n\nThe title to display on top of the chart.\n\n\n    \n    \n    x_title: str | None\n\ndefault `= None`\n\nThe title given to the x axis. By default, uses the value of the x parameter.\n\n\n    \n    \n    y_title: str | None\n\ndefault `= None`\n\nThe title given to the y axis. By default, uses the value of the y parameter.\n\n\n    \n    \n    color_title: str | None\n\ndefault `= None`\n\nThe title given to the color legend. By default, uses the value of color\nparameter.\n\n\n    \n    \n    x_bin: str | float | None\n\ndefault `= None`\n\nGrouping used to cluster x values. If x column is numeric, should be number to\nbin the x values. If x column is datetime, should be string such as \"1h\",\n\"15m\", \"10s\", using \"s\", \"m\", \"h\", \"d\" suffixes.\n\n\n    \n    \n    y_aggregate: Literal['sum', 'mean', 'median', 'min', 'max', 'count'] | None\n\ndefault `= None`\n\nAggregation function used to aggregate y values, used if x_bin is provided or\nx is a string/category. Must be one of \"sum\", \"mean\", \"median\", \"min\", \"max\".\n\n\n    \n    \n    color_map: dict[str, str] | None\n\ndefault `= None`\n\nMapping of series to color names or codes. For example, {\"success\": \"green\",\n\"fail\": \"FF8888\"}.\n\n\n    \n    \n    x_lim: list[float] | None\n\ndefault `= None`\n\nA tuple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ple or list containing the limits for the x-axis, specified as [x_min,\nx_max]. If x column is datetime type, x_lim should be timestamps.\n\n\n    \n    \n    y_lim: list[float | None]\n\ndefault `= None`\n\nA tuple of list containing the limits for the y-axis, specified as [y_min,\ny_max]. To fix only one of these values, set the other to None, e.g. [0, None]\nto scale from 0 to the maximum to value.\n\n\n    \n    \n    x_label_angle: float\n\ndefault `= 0`\n\nThe angle of the x-axis labels in degrees offset clockwise.\n\n\n    \n    \n    y_label_angle: float\n\ndefault `= 0`\n\nThe angle of the y-axis labels in degrees offset clockwise.\n\n\n    \n    \n    x_axis_labels_visible: bool\n\ndefault `= True`\n\nWhether the x-axis labels should be visible. Can be hidden when many x-axis\nlabels are present.\n\n\n    \n    \n    caption: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) caption to display below the plot.\n\n\n    \n    \n    sort: Literal['x', 'y', '-x', '-y'] | list[str] | None\n\ndefault `= None`\n\nThe sorting order of the x values, if x column is type string/category. Can be\n\"x\", \"y\", \"-x\", \"-y\", or list of strings that represent the order of the\ncategories.\n\n\n    \n    \n    tooltip: Literal['axis', 'none', 'all'] | list[str]\n\ndefault `= \"axis\"`\n\nThe tooltip to display when hovering on a point. \"axis\" shows the values for\nthe axis columns, \"all\" shows all column values, and \"none\" shows no tooltips.\nCan also provide a list of strings representing columns to show in the\ntooltip, which will be displayed along with axis values.\n\n\n    \n    \n    height: int | None\n\ndefault `= None`\n\nThe height of the plot in pixels.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe (optional) label to display on the top left corner of the plot.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nWhether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Whether the label should be displayed.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | Set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nWhether the plot should be visible.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n  ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "default `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= False`\n\nIf True, will show a button to make plot visible in fullscreen mode.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.LinePlot` | \"lineplot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "line_plot_demo\n\nOpen in \ud83c\udfa2 \u2197 import pandas as pd from random import randint, random import\ngradio as gr temp_sensor_data = pd.DataFrame( { \"time\":\npd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200), \"temperature\":\n[randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n\"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in\nrange(200)], \"location\": [\"indoor\", \"outdoor\"] * 100, } ) food_rating_data =\npd.DataFrame( { \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in\nrange(100)], \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n\"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)], \"wait\":\n[random() for i in range(100)], } ) with gr.Blocks() as line_plots: with\ngr.Row(): start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\") end =\ngr.DateTime(\"2021-01-05 00:00:00\", label=\"End\") apply_btn = gr.Button(\"Apply\",\nscale=0) with gr.Row(): group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"],\nvalue=\"None\", label=\"Group by\") aggregate = gr.Radio([\"sum\", \"mean\", \"median\",\n\"min\", \"max\"], value=\"sum\", label=\"Aggregation\") temp_by_time = gr.LinePlot(\ntemp_sensor_data, x=\"time\", y=\"temperature\", ) temp_by_time_location =\ngr.LinePlot( temp_sensor_data, x=\"time\", y=\"temperature\", color=\"location\", )\ntime_graphs = [temp_by_time, temp_by_time_location] group_by.change( lambda\ngroup: [gr.LinePlot(x_bin=None if group == \"None\" else group)] *\nlen(time_graphs), group_by, time_graphs ) aggregate.change( lambda aggregate:\n[gr.LinePlot(y_aggregate=aggregate)] * len(time_graphs), aggregate,\ntime_graphs ) def rescale(select: gr.SelectData): return select.index\nrescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None,\n[start, end]) for trigger in [apply_btn.click, rescale_evt.then]: trigger(\nlambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs),\n[start, end], time_graphs ) price_by_cuisine = gr.LinePlot( food_rating_data,\nx=\"cuisine\", y=\"price\", ) with gr.Row(): price_by_rating = gr.LinePlot(\nfoo", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ePlot(x_lim=[start, end])] * len(time_graphs),\n[start, end], time_graphs ) price_by_cuisine = gr.LinePlot( food_rating_data,\nx=\"cuisine\", y=\"price\", ) with gr.Row(): price_by_rating = gr.LinePlot(\nfood_rating_data, x=\"rating\", y=\"price\", ) price_by_rating_color =\ngr.LinePlot( food_rating_data, x=\"rating\", y=\"price\", color=\"cuisine\",\ncolor_map={\"Italian\": \"red\", \"Mexican\": \"green\", \"Chinese\": \"blue\"}, ) if\n__name__ == \"__main__\": line_plots.launch()\n\n    \n    \n    import pandas as pd\n    from random import randint, random\n    import gradio as gr\n    \n    \n    temp_sensor_data = pd.DataFrame(\n        {\n            \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n            \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n            \"location\": [\"indoor\", \"outdoor\"] * 100,\n        }\n    )\n    \n    food_rating_data = pd.DataFrame(\n        {\n            \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n            \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n            \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n            \"wait\": [random() for i in range(100)],\n        }\n    )\n    \n    with gr.Blocks() as line_plots:\n        with gr.Row():\n            start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n            end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n            apply_btn = gr.Button(\"Apply\", scale=0)\n        with gr.Row():\n            group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n            aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n    \n        temp_by_time = gr.LinePlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n        )\n        temp_by_time_location = gr.LinePlot(\n            temp_sensor_data,\n          ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "_by_time = gr.LinePlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n        )\n        temp_by_time_location = gr.LinePlot(\n            temp_sensor_data,\n            x=\"time\",\n            y=\"temperature\",\n            color=\"location\",\n        )\n    \n        time_graphs = [temp_by_time, temp_by_time_location]\n        group_by.change(\n            lambda group: [gr.LinePlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n            group_by,\n            time_graphs\n        )\n        aggregate.change(\n            lambda aggregate: [gr.LinePlot(y_aggregate=aggregate)] * len(time_graphs),\n            aggregate,\n            time_graphs\n        )\n    \n        def rescale(select: gr.SelectData):\n            return select.index\n        rescale_evt = gr.on([plot.select for plot in time_graphs], rescale, None, [start, end])\n    \n        for trigger in [apply_btn.click, rescale_evt.then]:\n            trigger(\n                lambda start, end: [gr.LinePlot(x_lim=[start, end])] * len(time_graphs), [start, end], time_graphs\n            )\n    \n        price_by_cuisine = gr.LinePlot(\n            food_rating_data,\n            x=\"cuisine\",\n            y=\"price\",\n        )\n        with gr.Row():\n            price_by_rating = gr.LinePlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n            )\n            price_by_rating_color = gr.LinePlot(\n                food_rating_data,\n                x=\"rating\",\n                y=\"price\",\n                color=\"cuisine\",\n                color_map={\"Italian\": \"red\", \"Mexican\": \"green\", \"Chinese\": \"blue\"},\n            )\n    \n    if __name__ == \"__main__\":\n        line_plots.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe LinePlot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`LinePlot.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the NativePlot. Uses event data gradio.SelectData to carry `value` referring to the label of the NativePlot, and `selected` to refer to state of the NativePlot. See EventData documentation on how to use this event data  \n`LinePlot.double_click(fn, \u00b7\u00b7\u00b7)` | Triggered when the NativePlot is double clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint w", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "pty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "ue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "llow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/lineplot", "source_page_title": "Gradio - Lineplot Docs"}, {"text": "A Gradio request object that can be used to access the request headers,\ncookies, query parameters and other information about the request from within\nthe prediction function. The class is a thin wrapper around the\nfastapi.Request class. Attributes of this class include: `headers`, `client`,\n`query_params`, `session_hash`, and `path_params`. If auth is enabled, the\n`username` attribute can be used to get the logged in user. In some\nenvironments, the dict-like attributes (e.g. `requests.headers`,\n`requests.query_params`) of this class are automatically converted to\ndictionaries, so we recommend converting them to dictionaries before accessing\nattributes for consistent behavior in different environments.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/request", "source_page_title": "Gradio - Request Docs"}, {"text": "import gradio as gr\n    def echo(text, request: gr.Request):\n        if request:\n            print(\"Request headers dictionary:\", request.headers)\n            print(\"IP address:\", request.client.host)\n            print(\"Query parameters:\", dict(request.query_params))\n            print(\"Session hash:\", request.session_hash)\n        return text\n    io = gr.Interface(echo, \"textbox\", \"textbox\").launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/request", "source_page_title": "Gradio - Request Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    request: fastapi.Request | None\n\ndefault `= None`\n\nA fastapi.Request\n\n\n    \n    \n    username: str | None\n\ndefault `= None`\n\nThe username of the logged in user (if auth is enabled)\n\n\n    \n    \n    session_hash: str | None\n\ndefault `= None`\n\nThe session hash of the current session. It is unique for each page load.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/request", "source_page_title": "Gradio - Request Docs"}, {"text": "request_ip_headers\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def predict(text, request: gr.Request):\nheaders = request.headers host = request.client.host user_agent =\nrequest.headers[\"user-agent\"] return { \"ip\": host, \"user_agent\": user_agent,\n\"headers\": headers, } gr.Interface(predict, \"text\", \"json\").launch()\n\n    \n    \n    import gradio as gr\n    \n    def predict(text, request: gr.Request):\n        headers = request.headers\n        host = request.client.host\n        user_agent = request.headers[\"user-agent\"]\n        return {\n            \"ip\": host,\n            \"user_agent\": user_agent,\n            \"headers\": headers,\n        }\n    \n    gr.Interface(predict, \"text\", \"json\").launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/request", "source_page_title": "Gradio - Request Docs"}, {"text": "Used to display arbitrary JSON output prettily. As this component does not\naccept user input, it is rarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "**As input component** : Passes the JSON value as a `dict` or `list`\ndepending on the value.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: dict | list | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a valid JSON `str` \\-- or a `list` or\n`dict` that can be serialized to a JSON string. The `list` or `dict` value can\ncontain numpy arrays.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> dict | list | str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | dict | list | Callable | None\n\ndefault `= None`\n\nDefault value as a valid JSON `str` -- or a `list` or `dict` that can be\nserialized to a JSON string. If a function is provided, the function will be\ncalled each time the app loads to set the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "rap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    open: bool\n\ndefault `= False`\n\nIf True, all JSON nodes will be expanded when rendered. By default, node\nlevels deeper than 3 are collapsed.\n\n\n    \n    \n    show_indices: bool\n\ndefault `= False`\n\nWhether to show numerical indices when displaying the elements of a list\nwithin the JSON object.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nHeight of the JSON component in pixels if a number is passed, or in CSS units\nif ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": " displaying the elements of a list\nwithin the JSON object.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nHeight of the JSON component in pixels if a number is passed, or in CSS units\nif a string is passed. Overflow will be scrollable. If None, the height will\nbe automatically adjusted to fit the content.\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= 500`\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.JSON` | \"json\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "zip_to_jsonblocks_xray\n\nOpen in \ud83c\udfa2 \u2197 from zipfile import ZipFile import gradio as gr def\nzip_to_json(file_obj): files = [] with ZipFile(file_obj.name) as zfile: for\nzinfo in zfile.infolist(): files.append( { \"name\": zinfo.filename,\n\"file_size\": zinfo.file_size, \"compressed_size\": zinfo.compress_size, } )\nreturn files demo = gr.Interface(zip_to_json, \"file\", \"json\") if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    from zipfile import ZipFile\n    \n    import gradio as gr\n    \n    def zip_to_json(file_obj):\n        files = []\n        with ZipFile(file_obj.name) as zfile:\n            for zinfo in zfile.infolist():\n                files.append(\n                    {\n                        \"name\": zinfo.filename,\n                        \"file_size\": zinfo.file_size,\n                        \"compressed_size\": zinfo.compress_size,\n                    }\n                )\n        return files\n    \n    demo = gr.Interface(zip_to_json, \"file\", \"json\")\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr disease_values = [0.25, 0.5, 0.75] def\nxray_model(diseases, img): return [{disease: disease_values[idx] for\nidx,disease in enumerate(diseases)}] def ct_model(diseases, img): return\n[{disease: 0.1 for disease in diseases}] with gr.Blocks(fill_width=True) as\ndemo: gr.Markdown( \"\"\" Detect Disease From Scan With this model you can\nlorem ipsum \\- ipsum 1 \\- ipsum 2 \"\"\" ) gr.DuplicateButton() disease =\ngr.CheckboxGroup( info=\"Select the diseases you want to scan for.\",\nchoices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\" )\nslider = gr.Slider(0, 100) with gr.Tab(\"X-ray\") as x_tab: with gr.Row():\nxray_scan = gr.Image() xray_results = gr.JSON(show_indices=True) xray_run =\ngr.Button(\"Run\") xray_run.click( xray_model, inputs=[disease, xray_scan],\noutputs=xray_results, api_name=\"xray_model\" ) with gr.Tab(\"CT Scan\"): with\ngr.Row(): ct_scan = gr.Image() ct_results = gr.JSON(show_indices=True) ct_run\n= gr.Button(\"Ru", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "l, inputs=[disease, xray_scan],\noutputs=xray_results, api_name=\"xray_model\" ) with gr.Tab(\"CT Scan\"): with\ngr.Row(): ct_scan = gr.Image() ct_results = gr.JSON(show_indices=True) ct_run\n= gr.Button(\"Run\") ct_run.click( ct_model, inputs=[disease, ct_scan],\noutputs=ct_results, api_name=\"ct_model\" ) upload_btn = gr.Button(\"Upload\nResults\", variant=\"primary\") upload_btn.click( lambda ct, xr: None,\ninputs=[ct_results, xray_results], outputs=[], ) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    \n    disease_values = [0.25, 0.5, 0.75]\n    \n    def xray_model(diseases, img):\n        return [{disease: disease_values[idx] for idx,disease in enumerate(diseases)}]\n    \n    def ct_model(diseases, img):\n        return [{disease: 0.1 for disease in diseases}]\n    \n    with gr.Blocks(fill_width=True) as demo:\n        gr.Markdown(\n            \"\"\"\n    Detect Disease From Scan\n    With this model you can lorem ipsum\n    - ipsum 1\n    - ipsum 2\n    \"\"\"\n        )\n        gr.DuplicateButton()\n        disease = gr.CheckboxGroup(\n            info=\"Select the diseases you want to scan for.\",\n            choices=[\"Covid\", \"Malaria\", \"Lung Cancer\"], label=\"Disease to Scan For\"\n        )\n        slider = gr.Slider(0, 100)\n    \n        with gr.Tab(\"X-ray\") as x_tab:\n            with gr.Row():\n                xray_scan = gr.Image()\n                xray_results = gr.JSON(show_indices=True)\n            xray_run = gr.Button(\"Run\")\n            xray_run.click(\n                xray_model,\n                inputs=[disease, xray_scan],\n                outputs=xray_results,\n                api_name=\"xray_model\"\n            )\n    \n        with gr.Tab(\"CT Scan\"):\n            with gr.Row():\n                ct_scan = gr.Image()\n                ct_results = gr.JSON(show_indices=True)\n            ct_run = gr.Button(\"Run\")\n            ct_run.click(\n                ct_model,\n                inputs=[disease, ct_scan],\n                outputs=ct_results,\n                api_name=\"ct", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "e)\n            ct_run = gr.Button(\"Run\")\n            ct_run.click(\n                ct_model,\n                inputs=[disease, ct_scan],\n                outputs=ct_results,\n                api_name=\"ct_model\"\n            )\n    \n        upload_btn = gr.Button(\"Upload Results\", variant=\"primary\")\n        upload_btn.click(\n            lambda ct, xr: None,\n            inputs=[ct_results, xray_results],\n            outputs=[],\n        )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe JSON component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`JSON.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the JSON changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the fun", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": " how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). Th", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "n the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "teral[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/json", "source_page_title": "Gradio - Json Docs"}, {"text": "Used to create an upload button, when clicked allows a user to upload files\nthat satisfy the specified file type or generic files (if file_type not set).  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "**As input component** : Passes the file as a `str` or `bytes` object, or a\nlist of `str` or list of `bytes` objects, depending on `type` and\n`file_count`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: bytes | str | list[bytes] | list[str] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` filepath or URL, or a `list[str]`\nof filepaths/URLs.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | list[str] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str\n\ndefault `= \"Upload a File\"`\n\nText to display on the button. Defaults to \"Upload a File\".\n\n\n    \n    \n    value: str | I18nData | list[str] | Callable | None\n\ndefault `= None`\n\nFile or list of files to upload by default.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    variant: Literal['primary', 'secondary', 'stop']\n\ndefault `= \"secondary\"`\n\n'primary' for main call-to-action, 'secondary' for a more subdued style,\n'stop' for a stop button.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    size: Literal['sm', 'md', 'lg']\n\ndefault `= \"lg\"`\n\nsize of the button. Can be \"sm\", \"md\", or \"lg\".\n\n\n    \n    \n    icon: str | None\n\ndefault `= None`\n\nURL or path to the icon file to display within the button. If None, no icon\nwill be displayed.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int | None\n\ndefault `= None`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nIf False, the UploadButto", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "ain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool\n\ndefault `= True`\n\nIf False, the UploadButton will be in a disabled state.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    type: Literal['filepath', 'binary']\n\ndefault `= \"filepath\"`\n\nType of value to be returned by component. \"file\" returns a temporary file\nobject with the same base name as the uploaded file, whose full path can be\nretrieved by file_obj.name, \"binary\" returns an bytes object.\n\n\n    \n    \n    file_count: Literal['single', 'multiple', 'directory']\n\ndefault `= \"single\"`\n\nif single, allows user to upload one file. If \"multiple\", user uploads\nmultiple files. If \"directory\", user ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "    \n    \n    file_count: Literal['single', 'multiple', 'directory']\n\ndefault `= \"single\"`\n\nif single, allows user to upload one file. If \"multiple\", user uploads\nmultiple files. If \"directory\", user uploads all files in selected directory.\nReturn type will be list for each file in case of \"multiple\" or \"directory\".\n\n\n    \n    \n    file_types: list[str] | None\n\ndefault `= None`\n\nList of type of files to be uploaded. \"file\" allows any file to be uploaded,\n\"image\" allows only image files to be uploaded, \"audio\" allows only audio\nfiles to be uploaded, \"video\" allows only video files to be uploaded, \"text\"\nallows only text files to be uploaded.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.UploadButton` | \"uploadbutton\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "upload_and_downloadupload_button\n\nOpen in \ud83c\udfa2 \u2197 from pathlib import Path import gradio as gr def\nupload_file(filepath): name = Path(filepath).name return\n[gr.UploadButton(visible=False), gr.DownloadButton(label=f\"Download {name}\",\nvalue=filepath, visible=True)] def download_file(): return\n[gr.UploadButton(visible=True), gr.DownloadButton(visible=False)] with\ngr.Blocks() as demo: gr.Markdown(\"First upload a file and and then you'll be\nable download it (but only once!)\") with gr.Row(): u = gr.UploadButton(\"Upload\na file\", file_count=\"single\") d = gr.DownloadButton(\"Download the file\",\nvisible=False) u.upload(upload_file, u, [u, d]) d.click(download_file, None,\n[u, d]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    from pathlib import Path\n    import gradio as gr\n    \n    def upload_file(filepath):\n        name = Path(filepath).name\n        return [gr.UploadButton(visible=False), gr.DownloadButton(label=f\"Download {name}\", value=filepath, visible=True)]\n    \n    def download_file():\n        return [gr.UploadButton(visible=True), gr.DownloadButton(visible=False)]\n    \n    with gr.Blocks() as demo:\n        gr.Markdown(\"First upload a file and and then you'll be able download it (but only once!)\")\n        with gr.Row():\n            u = gr.UploadButton(\"Upload a file\", file_count=\"single\")\n            d = gr.DownloadButton(\"Download the file\", visible=False)\n    \n        u.upload(upload_file, u, [u, d])\n        d.click(download_file, None, [u, d])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def upload_file(files): file_paths =\n[file.name for file in files] return file_paths with gr.Blocks() as demo:\nfile_output = gr.File() upload_button = gr.UploadButton(\"Click to Upload a\nFile\", file_types=[\"image\", \"video\"], file_count=\"multiple\")\nupload_button.upload(upload_file, upload_button, file_output) demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def upload_file(files):\n        file_paths = [file.name fo", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "ile_count=\"multiple\")\nupload_button.upload(upload_file, upload_button, file_output) demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def upload_file(files):\n        file_paths = [file.name for file in files]\n        return file_paths\n    \n    with gr.Blocks() as demo:\n        file_output = gr.File()\n        upload_button = gr.UploadButton(\"Click to Upload a File\", file_types=[\"image\", \"video\"], file_count=\"multiple\")\n        upload_button.upload(upload_file, upload_button, file_output)\n    \n    demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe UploadButton component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`UploadButton.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the UploadButton is clicked.  \n`UploadButton.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the UploadButton.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand d", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "oint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), wit", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "r each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"defau", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "ments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/uploadbutton", "source_page_title": "Gradio - Uploadbutton Docs"}, {"text": "Creates a file component that allows uploading one or more generic files\n(when used as an input) or displaying generic files or URLs for download (as\noutput).  \nDemo: zip_files, zip_to_json\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "**As input component** : Passes the file as a `str` or `bytes` object, or a\nlist of `str` or list of `bytes` objects, depending on `type` and\n`file_count`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: bytes | str | list[bytes] | list[str] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` filepath or URL, or a `list[str]`\nof filepaths/URLs.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | list[str] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | list[str] | Callable | None\n\ndefault `= None`\n\nDefault file(s) to display, given as a str file path or URL, or a list of str\nfile paths / URLs. If a function is provided, the function will be called each\ntime the app loads to set the initial value of this component.\n\n\n    \n    \n    file_count: Literal['single', 'multiple', 'directory']\n\ndefault `= \"single\"`\n\nif single, allows user to upload one file. If \"multiple\", user uploads\nmultiple files. If \"directory\", user uploads all files in selected directory.\nReturn type will be list for each file in case of \"multiple\" or \"directory\".\n\n\n    \n    \n    file_types: list[str] | None\n\ndefault `= None`\n\nList of file extensions or types of files to be uploaded (e.g. ['image',\n'.json', '.mp4']). \"file\" allows any file to be uploaded, \"image\" allows only\nimage files to be uploaded, \"audio\" allows only audio files to be uploaded,\n\"video\" allows only video files to be uploaded, \"text\" allows only text files\nto be uploaded.\n\n\n    \n    \n    type: Literal['filepath', 'binary']\n\ndefault `= \"filepath\"`\n\nType of value to be returned by component. \"file\" returns a temporary file\nobject with the same base name as the uploaded file, whose full path can be\nretrieved by file_obj.name, \"binary\" returns an bytes object.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponent", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "r whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    height: int | str | float | None\n\ndefault `= None`\n\nThe default height of the file component when no files have been uploaded, or\nthe maximum height of the file component when files are present. Specified in\npixels if a number is passed, or in CSS units if a string is passed. If more\nfiles are uploaded than can fit in the height, a scrollbar will appear.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to upload a file; if False, can only be used to\ndisplay files. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": " \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    allow_reordering: bool\n\ndefault `= False`\n\nif True, will allow users to reorder uploaded files by dragging and dropping.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.File` | \"file\" | Uses default values  \n`gradio.Files` | \"files\" | Uses file_count=\"multiple\"  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe File component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`File.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the File changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`File.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the File. Uses event data gradio.SelectData to carry `value` referring to the label of the File, and `selected` to refer to state of the File. See EventData documentation on how to use this event data  \n`File.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the File using the clear button for the component.  \n`File.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the File.  \n`File.delete(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user deletes and item from the File. Uses event data gradio.DeletedFileData to carry `value` referring to the file that was deleted as an instance of FileData. See EventData documentation on how to use this event data  \n`File.download(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user downloads a file from the File. Uses event data gradio.DownloadData to carry information about the downloaded file as a FileData object. See EventData documentation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event i", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "ation on how to use this event data  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minima", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "l be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "\n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"vie", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/file", "source_page_title": "Gradio - File Docs"}, {"text": "Creates a file explorer component that allows users to browse files on the\nmachine hosting the Gradio app. As an input component, it also allows users to\nselect files to be used as input to a function, while as an output component,\nit displays selected files.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "**As input component** : Passes the selected file or directory as a `str`\npath (relative to `root`) or `list[str}` depending on `file_count`\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: list[str] | str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects function to return a `str` path to a\nfile, or `list[str]` consisting of paths to files.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | list[str] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    glob: str\n\ndefault `= \"**/*\"`\n\nThe glob-style pattern used to select which files to display, e.g. \"*\" to\nmatch all files, \"*.png\" to match all .png files, \"**/*.txt\" to match any .txt\nfile in any subdirectory, etc. The default value matches all files and folders\nrecursively. See the Python glob documentation at\nhttps://docs.python.org/3/library/glob.html for more information.\n\n\n    \n    \n    value: str | list[str] | Callable | None\n\ndefault `= None`\n\nThe file (or list of files, depending on the `file_count` parameter) to show\nas \"selected\" when the component is first loaded. If a callable is provided,\nit will be called when the app loads to set the initial value of the\ncomponent. If not provided, no files are shown as selected.\n\n\n    \n    \n    file_count: Literal['single', 'multiple']\n\ndefault `= \"multiple\"`\n\nWhether to allow single or multiple files to be selected. If \"single\", the\ncomponent will return a single absolute file path as a string. If \"multiple\",\nthe component will return a list of absolute file paths as a list of strings.\n\n\n    \n    \n    root_dir: str | Path\n\ndefault `= \".\"`\n\nPath to root directory to select files from. If not provided, defaults to\ncurrent working directory.\n\n\n    \n    \n    ignore_glob: str | None\n\ndefault `= None`\n\nThe glob-style, case-sensitive pattern that will be used to exclude files from\nthe list. For example, \"*.py\" will exclude all .py files from the list. See\nthe Python glob documentation at https://docs.python.org/3/library/glob.html\nfor more information.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "bel will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= None`\n\nThe maximum height of the file component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If more files are uploaded than\ncan fit in the height, a scrollbar will appear.\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= 500`\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to select file(s); if False, will only display\nfiles. If not provided, this is inferred based on wheth", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "lt `= None`\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nif True, will allow users to select file(s); if False, will only display\nfiles. If not provided, this is inferred based on whether the component is\nused as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.FileExplorer` | \"fileexplorer\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe FileExplorer component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`FileExplorer.change(fn, \u00b7\u00b7\u00b7)` |   \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\nd", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "nt will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch togeth", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "en if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nth", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/fileexplorer", "source_page_title": "Gradio - Fileexplorer Docs"}, {"text": "The gr.RetryData class is a subclass of gr.Event data that specifically\ncarries information about the `.retry()` event. When gr.RetryData is added as\na type hint to an argument of an event listener method, a gr.RetryData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/retrydata", "source_page_title": "Gradio - Retrydata Docs"}, {"text": "import gradio as gr\n    \n    def retry(retry_data: gr.RetryData, history: list[gr.MessageDict]):\n        history_up_to_retry = history[:retry_data.index]\n        new_response = \"\"\n        for token in api.chat_completion(history):\n            new_response += token\n            yield history + [new_response]\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot()\n        chatbot.retry(retry, chatbot, chatbot)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/retrydata", "source_page_title": "Gradio - Retrydata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    index: int | tuple[int, int]\n\nThe index of the user message that should be retried.\n\n\n    \n    \n    value: Any\n\nThe value of the user message that should be retried.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/retrydata", "source_page_title": "Gradio - Retrydata Docs"}, {"text": "Creates a gallery component that allows displaying a grid of images or\nvideos, and optionally captions. If used as an input, the user can upload\nimages or videos to the gallery. If used as an output, the user can click on\nindividual images or videos to view them at a higher resolution.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "**As input component** : Passes the list of images or videos as a list of\n(media, caption) tuples, or a list of (media, None) tuples if no captions are\nprovided (which is usually the case). Images can be a `str` file path, a\n`numpy` array, or a `PIL.Image` object depending on `type`. Videos are always\n`str` file path.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: List[tuple[str, str | None]] | List[tuple[PIL.Image.Image, str | None]] | List[tuple[np.ndarray, str | None]] | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects the function to return a `list` of images\nor videos, or `list` of (media, `str` caption) tuples. Each image can be a\n`str` file path, a `numpy` array, or a `PIL.Image` object. Each video can be a\n`str` file path.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> list[GalleryImageType | CaptionedGalleryImageType] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: list[np.ndarray | PIL.Image.Image | str | Path | tuple] | Callable | None\n\ndefault `= None`\n\nList of images or videos to display in the gallery by default. If a function\nis provided, the function will be called each time the app loads to set the\ninitial value of this component.\n\n\n    \n    \n    format: str\n\ndefault `= \"webp\"`\n\nFormat to save images before they are returned to the frontend, such as 'jpeg'\nor 'png'. This parameter only applies to images that are returned from the\nprediction function as numpy arrays or PIL Images. The format should be\nsupported by the PIL library.\n\n\n    \n    \n    file_types: list[str] | None\n\ndefault `= None`\n\nList of file extensions or types of files to be uploaded (e.g. ['image',\n'.mp4']), when this is used as an input component. \"image\" allows only image\nfiles to be uploaded, \"video\" allows only video files to be uploaded, \".mp4\"\nallows only mp4 files to be uploaded, etc. If None, any image and video files\ntypes are allowed.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf Tru", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, i", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "re not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    columns: int | None\n\ndefault `= 2`\n\nRepresents the number of images that should be shown in one row.\n\n\n    \n    \n    rows: int | None\n\ndefault `= None`\n\nRepresents the number of rows in the image grid.\n\n\n    \n    \n    height: int | float | str | None\n\ndefault `= None`\n\nThe height of the gallery component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If more images are displayed\nthan can fit in the height, a scrollbar will appear.\n\n\n    \n    \n    allow_preview: bool\n\ndefault `= True`\n\nIf True, images in the gallery will be enlarged when they are clicked. Default\nis True.\n\n\n    \n    \n    preview: bool | None\n\ndefault `= None`\n\nIf True, Gallery will start in preview mode, which shows all of the images as\nthumbnails and allows the user to click on them to view them in full size.\nOnly works if allow_preview is True.\n\n\n    \n    \n    selected_index: int | None\n\ndefault `= None`\n\nThe index of the image that should be initially selected. If None, no image\nwill be selected at start. If provided, will set Gallery to preview mode\nunless allow_preview is set to False.\n\n\n    \n    \n    object_fit: Literal['contain', 'cover', 'fill', 'none', 'scale-down'] | None\n\ndefault `= None`\n\nCSS object-fit property for the thumbnail images in the gallery. Can be\n\"contain\", \"cover\", \"fill\", \"none\", or \"scale-down\".\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussio", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "wn\".\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    show_download_button: bool | None\n\ndefault `= True`\n\nIf True, will show a download button in the corner of the selected image. If\nFalse, the icon does not appear. Default is True.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, the gallery will be interactive, allowing the user to upload images.\nIf False, the gallery will be static. Default is True.\n\n\n    \n    \n    type: Literal['numpy', 'pil', 'filepath']\n\ndefault `= \"filepath\"`\n\nThe format the image is converted to before being passed into the prediction\nfunction. \"numpy\" converts the image to a numpy array with shape (height,\nwidth, 3) and values from 0 to 255, \"pil\" converts the image to a PIL image\nobject, \"filepath\" passes a str path to a temporary file containing the image.\nIf the image is SVG, the `type` is ignored and the filepath of the SVG is\nreturned.\n\n\n    \n    \n    show_fullscreen_button: bool\n\ndefault `= True`\n\nIf True, will show a fullscreen icon in the corner of the component that\nallows user to view the gallery in fullscreen mode. If False, icon does not\nappear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Gallery` | \"gallery\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "fake_gan\n\nOpen in \ud83c\udfa2 \u2197 This demo needs to be run from the repo folder. python\ndemo/fake_gan/run.py import random import gradio as gr def fake_gan(): images\n= [ (random.choice( [ \"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg\",\n\"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg\",\n\"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg\",\n\"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg\",\n\"http://www.marketingtool.online/en/face-\ngenerator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\", ] ), f\"label\n{i}\") for i in range(3) ] return images with gr.Blocks() as demo: gallery =\ngr.Gallery( label=\"Generated images\", show_label=False, elem_id=\"gallery\" ,\ncolumns=1, object_fit=\"contain\", height=\"auto\") btn = gr.Button(\"Generate\nimages\", scale=0) btn.click(fake_gan, None, gallery) if __name__ ==\n\"__main__\": demo.launch()\n\n    \n    \n    This demo needs to be run from the repo folder.\n    python demo/fake_gan/run.py\n    import random\n    \n    import gradio as gr\n    \n    def fake_gan():\n        images = [\n            (random.choice(\n                [\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1151ce9f4b2043de0d2e3b7826127998.jpg\",\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-116b5e92936b766b7fdfc242649337f7.jpg\",\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1163530ca19b5cebe1b002b8ec67b6fc.jpg\",\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-1116395d6e6a6581eef8b8038f4c8e55.jpg\",\n                    \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\",\n                ]\n            ), f\"label {i}\")\n            for i in range(3", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "                  \"http://www.marketingtool.online/en/face-generator/img/faces/avatar-11319be65db395d0e8e6855d18ddcef0.jpg\",\n                ]\n            ), f\"label {i}\")\n            for i in range(3)\n        ]\n        return images\n    \n    with gr.Blocks() as demo:\n        gallery = gr.Gallery(\n            label=\"Generated images\", show_label=False, elem_id=\"gallery\"\n        , columns=1, object_fit=\"contain\", height=\"auto\")\n        btn = gr.Button(\"Generate images\", scale=0)\n    \n        btn.click(fake_gan, None, gallery)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Gallery component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Gallery.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Gallery. Uses event data gradio.SelectData to carry `value` referring to the label of the Gallery, and `selected` to refer to state of the Gallery. See EventData documentation on how to use this event data  \n`Gallery.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Gallery.  \n`Gallery.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Gallery changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Gallery.preview_close(fn, \u00b7\u00b7\u00b7)` | This event is triggered when the Gallery preview is closed by the user  \n`Gallery.preview_open(fn, \u00b7\u00b7\u00b7)` | This event is triggered when the Gallery preview is opened by the user  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.co", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "ple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "ows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_m", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "nents .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/gallery", "source_page_title": "Gradio - Gallery Docs"}, {"text": "Sidebar is a collapsible panel that renders child components on the left\nside of the screen within a Blocks layout.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "with gr.Blocks() as demo:\n        with gr.Sidebar():\n            gr.Textbox()\n            gr.Button()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nname of the sidebar. Not displayed to the user.\n\n\n    \n    \n    open: bool\n\ndefault `= True`\n\nif True, sidebar is open by default.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    width: int | str\n\ndefault `= 320`\n\nThe width of the sidebar, specified in pixels if a number is passed, or in CSS\nunits if a string is passed.\n\n\n    \n    \n    position: Literal['left', 'right']\n\ndefault `= \"left\"`\n\nThe position of the sidebar in the layout, either \"left\" or \"right\". Defaults\nto \"left\".\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Sidebar.expand(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%2", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Sidebar is expanded.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | B", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": " to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    ", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "spinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are ite", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "ample,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: i", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Sidebar.collapse(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Sidebar is collapsed.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "nds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": " a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": " example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "me to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/sidebar", "source_page_title": "Gradio - Sidebar Docs"}, {"text": "Creates an audio component that can be used to upload/record audio (as an\ninput) or display audio (as an output).\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "**As input component** : passes audio as one of these formats (depending on\n`type`): a `str` filepath, or `tuple` of (sample rate in Hz, audio data as\nnumpy array). If the latter, the audio data is a 16-bit `int` array whose\nvalues range from -32768 to 32767 and shape of the audio data array is\n(samples,) for mono audio or (samples, channels) for multi-channel audio.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | tuple[int, np.ndarray] | None\n    )\n    \t...\n\n  \n\n**As output component** : expects audio data in any of these formats: a\n`str` or `pathlib.Path` filepath or URL to an audio file, or a `bytes` object\n(recommended for streaming), or a `tuple` of (sample rate in Hz, audio data as\nnumpy array). Note: if audio is supplied as a numpy array, the audio will be\nnormalized by its peak value to avoid distortion or clipping in the resulting\naudio.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | Path | bytes | tuple[int, np.ndarray] | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Path | tuple[int, np.ndarray] | Callable | None\n\ndefault `= None`\n\nA path, URL, or [sample_rate, numpy array] tuple (sample rate in Hz, audio\ndata as a float or int numpy array) for the default value that Audio component\nis going to take. If a function is provided, the function will be called each\ntime the app loads to set the initial value of this component.\n\n\n    \n    \n    sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None\n\ndefault `= None`\n\nA list of sources permitted for audio. \"upload\" creates a box where user can\ndrop an audio file, \"microphone\" creates a microphone input. The first element\nin the list will be used as the default source. If None, defaults to\n[\"upload\", \"microphone\"], or [\"microphone\"] if `streaming` is True.\n\n\n    \n    \n    type: Literal['numpy', 'filepath']\n\ndefault `= \"numpy\"`\n\nThe format the audio file is converted to before being passed into the\nprediction function. \"numpy\" converts the audio to a tuple consisting of: (int\nsample rate, numpy.array for the data), \"filepath\" passes a str path to a\ntemporary file containing the audio.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nRelative width compared to adjacent Components in a Row. For example, if\nComponent A has scale=2, and Component B has scale=1, A will be twice as wide\nas B. Should be an integer.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nMinimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    interactive: bool | None\n\ndefault `= None`\n\nIf True, will allow users to upload and edit an audio file. If False, can only\nbe used to play audio. If not provided, this is inferred based on whether the\ncomponent is used as an input or output.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    streaming: bool\n\ndefault `= False`\n\nIf set to True when used in a `live` interface as an input, will automatically\nstream webcam feed. When used set as an output, takes audio chunks yield from\nthe backend and combines them into one streaming audio output.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component w", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nif False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    format: Literal['wav', 'mp3'] | None\n\ndefault `= None`\n\nthe file extension with which to save audio files. Either 'wav' or 'mp3'. wav\nfiles are lossless but will tend to be larger files. mp3 files tend to be\nsmaller. This parameter applies both when this component is used as an input\n(and `type` is \"filepath\") to determine which file format to convert user-\nprovided audio to, and when this component is used as an output to determine\nthe format of audio returned to the user. If None, no file format conversion\nis done and the audio is kept as is. In the case where output audio is\nreturned from the prediction function as numpy array and no `format` is\nprovided, it will be returned as a \"wav\" file.\n\n\n    \n    \n    autoplay: bool\n\ndefault `= False`\n\nWhether to automatically play the audio when the component is used as an\noutput. Note: browsers will not autoplay audio files if the user has not\ninteracted with the page yet.\n\n\n    \n    \n    show_down", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "hether to automatically play the audio when the component is used as an\noutput. Note: browsers will not autoplay audio files if the user has not\ninteracted with the page yet.\n\n\n    \n    \n    show_download_button: bool | None\n\ndefault `= None`\n\nIf True, will show a download button in the corner of the component for saving\naudio. If False, icon does not appear. By default, it will be True for output\ncomponents and False for input components.\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    editable: bool\n\ndefault `= True`\n\nIf True, allows users to manipulate the audio file if the component is\ninteractive. Defaults to True.\n\n\n    \n    \n    min_length: int | None\n\ndefault `= None`\n\nThe minimum length of audio (in seconds) that the user can pass into the\nprediction function. If None, there is no minimum length.\n\n\n    \n    \n    max_length: int | None\n\ndefault `= None`\n\nThe maximum length of audio (in seconds) that the user can pass into the\nprediction function. If None, there is no maximum length.\n\n\n    \n    \n    waveform_options: WaveformOptions | dict | None\n\ndefault `= None`\n\nA dictionary of options for the waveform display. Options include:\nwaveform_color (str), waveform_progress_color (str), show_controls (bool),\nskip_length (int), trim_region_color (str). Default is None, which uses the\ndefault values for these options. See `gr.WaveformOptions` docs.\n\n\n    \n    \n    loop: bool\n\ndefault `= False`\n\nIf True, the audio will loop when it reaches the end and continue playing from\nthe beginning.\n\n\n    \n    \n    recording: bool\n\ndefault `= False`\n\nIf True, the audio component will be set to record audio from the microphone\nif the source is set to \"microphone\". Def", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "e playing from\nthe beginning.\n\n\n    \n    \n    recording: bool\n\ndefault `= False`\n\nIf True, the audio component will be set to record audio from the microphone\nif the source is set to \"microphone\". Defaults to False.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Audio` | \"audio\" | Uses default values  \n`gradio.Microphone` | \"microphone\" | Uses sources=[\"microphone\"]  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "generate_tonereverse_audio\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr notes = [\"C\", \"C\", \"D\",\n\"D\", \"E\", \"F\", \"F\", \"G\", \"G\", \"A\", \"A\", \"B\"] def generate_tone(note,\noctave, duration): sr = 48000 a4_freq, tones_from_a4 = 440, 12 * (octave - 4)\n+ (note - 9) frequency = a4_freq * 2 ** (tones_from_a4 / 12) duration =\nint(duration) audio = np.linspace(0, duration, duration * sr) audio = (20000 *\nnp.sin(audio * (2 * np.pi * frequency))).astype(np.int16) return sr, audio\ndemo = gr.Interface( generate_tone, [ gr.Dropdown(notes, type=\"index\"),\ngr.Slider(4, 6, step=1), gr.Textbox(value=\"1\", label=\"Duration in seconds\"),\n], \"audio\", ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    import gradio as gr\n    \n    notes = [\"C\", \"C\", \"D\", \"D\", \"E\", \"F\", \"F\", \"G\", \"G\", \"A\", \"A\", \"B\"]\n    \n    def generate_tone(note, octave, duration):\n        sr = 48000\n        a4_freq, tones_from_a4 = 440, 12 * (octave - 4) + (note - 9)\n        frequency = a4_freq * 2 ** (tones_from_a4 / 12)\n        duration = int(duration)\n        audio = np.linspace(0, duration, duration * sr)\n        audio = (20000 * np.sin(audio * (2 * np.pi * frequency))).astype(np.int16)\n        return sr, audio\n    \n    demo = gr.Interface(\n        generate_tone,\n        [\n            gr.Dropdown(notes, type=\"index\"),\n            gr.Slider(4, 6, step=1),\n            gr.Textbox(value=\"1\", label=\"Duration in seconds\"),\n        ],\n        \"audio\",\n    )\n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import numpy as np import gradio as gr def reverse_audio(audio):\nsr, data = audio return (sr, np.flipud(data)) input_audio = gr.Audio(\nsources=[\"microphone\"], waveform_options=gr.WaveformOptions(\nwaveform_color=\"01C6FF\", waveform_progress_color=\"0066B4\", skip_length=2,\nshow_controls=False, ), ) demo = gr.Interface( fn=reverse_audio,\ninputs=input_audio, outputs=\"audio\" ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    \n    ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "ip_length=2,\nshow_controls=False, ), ) demo = gr.Interface( fn=reverse_audio,\ninputs=input_audio, outputs=\"audio\" ) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import numpy as np\n    \n    import gradio as gr\n    \n    def reverse_audio(audio):\n        sr, data = audio\n        return (sr, np.flipud(data))\n    \n    input_audio = gr.Audio(\n        sources=[\"microphone\"],\n        waveform_options=gr.WaveformOptions(\n            waveform_color=\"01C6FF\",\n            waveform_progress_color=\"0066B4\",\n            skip_length=2,\n            show_controls=False,\n        ),\n    )\n    demo = gr.Interface(\n        fn=reverse_audio,\n        inputs=input_audio,\n        outputs=\"audio\"\n    )\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Audio component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Audio.stream(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user streams the Audio.  \n`Audio.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Audio changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Audio.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Audio using the clear button for the component.  \n`Audio.play(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user plays the media in the Audio.  \n`Audio.pause(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the media in the Audio stops for any reason.  \n`Audio.stop(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user reaches the end of the media playing in the Audio.  \n`Audio.pause(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the media in the Audio stops for any reason.  \n`Audio.start_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user starts recording with the Audio.  \n`Audio.pause_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user pauses recording with the Audio.  \n`Audio.stop_recording(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user stops recording with the Audio.  \n`Audio.upload(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user uploads a file into the Audio.  \n`Audio.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Audio.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "s triggered when the user uploads a file into the Audio.  \n`Audio.input(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user changes the value of the Audio.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "iption. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"minimal\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postpr", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "nt data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited b", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "gradio.WaveformOptions(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass for specifying options for the waveform display in the Audio\ncomponent. An instance of this class can be passed into the `waveform_options`\nparameter of `gr.Audio`.\n\nInitialization\n\nParameters \u25bc\n\n\n    \n    \n    waveform_color: str | None\n\ndefault `= None`\n\nThe color (as a hex string or valid CSS color) of the full waveform\nrepresenting the amplitude of the audio. Defaults to a light gray color.\n\n\n    \n    \n    waveform_progress_color: str | None\n\ndefault `= None`\n\nThe color (as a hex string or valid CSS color) that the waveform fills with to\nas the audio plays. Defaults to the accent color.\n\n\n    \n    \n    trim_region_color: str | None\n\ndefault `= None`\n\nThe color (as a hex string or valid CSS color) of the trim region. Defaults to\nthe accent color.\n\n\n    \n    \n    show_recording_waveform: bool\n\ndefault `= True`\n\nIf True, shows a waveform when recording audio or playing audio. If False,\nuses the default browser audio players. For streamed audio, the default\nbrowser audio player is always used.\n\n\n    \n    \n    show_controls: bool\n\ndefault `= False`\n\nDeprecated and has no effect. Use `show_recording_waveform` instead.\n\n\n    \n    \n    skip_length: int | float\n\ndefault `= 5`\n\nThe percentage (between 0 and 100) of the audio to skip when clicking on the\nskip forward / skip backward buttons.\n\n\n    \n    \n    sample_rate: int\n\ndefault `= 44100`\n\nThe output sample rate (in Hz) of the audio after editing.\n\n", "heading1": "WaveformOptions", "source_page_url": "https://gradio.app/docs/gradio/audio", "source_page_title": "Gradio - Audio Docs"}, {"text": "Accordion is a layout element which can be toggled to show/hide the\ncontained content.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "with gr.Accordion(\"See Details\"):\n        gr.Markdown(\"lorem ipsum\")\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nname of accordion section.\n\n\n    \n    \n    open: bool\n\ndefault `= True`\n\nif True, accordion is open by default.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional string or list of strings that are assigned as the class of this\ncomponent in the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, this layout will not be rendered in the Blocks context. Should be\nused if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= None`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "", "heading1": "Methods", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Accordion.expand(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Accordion is expanded.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "onds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "s a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "r example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limi", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "ame to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Accordion.collapse(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20In", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Accordion is collapsed.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Compon", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "esponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n  ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "hows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": " For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_l", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "i_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Accordion.expand(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Accordion is expanded.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "onds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "s a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "r example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limi", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "ame to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "expand", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\n    \n    \n    gradio.Accordion.collapse(\u00b7\u00b7\u00b7)\n\nDescription\n![](data:image/svg+xml,%3csvg%20xmlns='http://www.w3.org/2000/svg'%20fill='%23808080'%20viewBox='0%200%20640%20512'%3e%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20In", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "%3c!--!%20Font%20Awesome%20Pro%206.0.0%20by%20@fontawesome%20-%20https://fontawesome.com%20License%20-%20https://fontawesome.com/license%20\\(Commercial%20License\\)%20Copyright%202022%20Fonticons,%20Inc.%20--%3e%3cpath%20d='M172.5%20131.1C228.1%2075.51%20320.5%2075.51%20376.1%20131.1C426.1%20181.1%20433.5%20260.8%20392.4%20318.3L391.3%20319.9C381%20334.2%20361%20337.6%20346.7%20327.3C332.3%20317%20328.9%20297%20339.2%20282.7L340.3%20281.1C363.2%20249%20359.6%20205.1%20331.7%20177.2C300.3%20145.8%20249.2%20145.8%20217.7%20177.2L105.5%20289.5C73.99%20320.1%2073.99%20372%20105.5%20403.5C133.3%20431.4%20177.3%20435%20209.3%20412.1L210.9%20410.1C225.3%20400.7%20245.3%20404%20255.5%20418.4C265.8%20432.8%20262.5%20452.8%20248.1%20463.1L246.5%20464.2C188.1%20505.3%20110.2%20498.7%2060.21%20448.8C3.741%20392.3%203.741%20300.7%2060.21%20244.3L172.5%20131.1zM467.5%20380C411%20436.5%20319.5%20436.5%20263%20380C213%20330%20206.5%20251.2%20247.6%20193.7L248.7%20192.1C258.1%20177.8%20278.1%20174.4%20293.3%20184.7C307.7%20194.1%20311.1%20214.1%20300.8%20229.3L299.7%20230.9C276.8%20262.1%20280.4%20306.9%20308.3%20334.8C339.7%20366.2%20390.8%20366.2%20422.3%20334.8L534.5%20222.5C566%20191%20566%20139.1%20534.5%20108.5C506.7%2080.63%20462.7%2076.99%20430.7%2099.9L429.1%20101C414.7%20111.3%20394.7%20107.1%20384.5%2093.58C374.2%2079.2%20377.5%2059.21%20391.9%2048.94L393.5%2047.82C451%206.731%20529.8%2013.25%20579.8%2063.24C636.3%20119.7%20636.3%20211.3%20579.8%20267.7L467.5%20380z'/%3e%3c/svg%3e)\n\nThis listener is triggered when the Accordion is collapsed.\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Compon", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "esponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n  ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "hows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that ", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": " For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_l", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "i_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "collapse", "source_page_url": "https://gradio.app/docs/gradio/accordion", "source_page_title": "Gradio - Accordion Docs"}, {"text": "A TabbedInterface is created by providing a list of Interfaces or Blocks,\neach of which gets rendered in a separate tab. Only the components from the\nInterface/Blocks will be rendered in the tab. Certain high-level attributes of\nthe Blocks (e.g. custom `css`, `js`, and `head` attributes) will not be\nloaded.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/tabbedinterface", "source_page_title": "Gradio - Tabbedinterface Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    interface_list: list[Blocks]\n\nA list of Interfaces (or Blocks) to be rendered in the tabs.\n\n\n    \n    \n    tab_names: list[str] | None\n\ndefault `= None`\n\nA list of tab names. If None, the tab names will be \"Tab 1\", \"Tab 2\", etc.\n\n\n    \n    \n    title: str | None\n\ndefault `= None`\n\nThe tab title to display when this demo is opened in a browser window.\n\n\n    \n    \n    theme: Theme | str | None\n\ndefault `= None`\n\nA Theme object or a string representing a theme. If a string, will look for a\nbuilt-in theme with that name (e.g. \"soft\" or \"default\"), or will attempt to\nload a theme from the Hugging Face Hub (e.g. \"gradio/monochrome\"). If None,\nwill use the Default theme.\n\n\n    \n    \n    analytics_enabled: bool | None\n\ndefault `= None`\n\nWhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable or default to True.\n\n\n    \n    \n    css: str | None\n\ndefault `= None`\n\nCustom css as a string or path to a css file. This css will be included in the\ndemo webpage.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nCustom js as a string or path to a js file. The custom js should in the form\nof a single js function. This function will automatically be executed when the\npage loads. For more flexibility, use the head parameter to insert js inside\n<script> tags.\n\n\n    \n    \n    head: str | None\n\ndefault `= None`\n\nCustom html to insert into the head of the demo webpage. This can be used to\nadd custom meta tags, multiple scripts, stylesheets, etc. to the page.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/tabbedinterface", "source_page_title": "Gradio - Tabbedinterface Docs"}, {"text": "tabbed_interface_lite\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr hello_world = gr.Interface(lambda name: \"Hello\n\" + name, \"text\", \"text\") bye_world = gr.Interface(lambda name: \"Bye \" + name,\n\"text\", \"text\") chat = gr.ChatInterface(lambda *args: \"Hello \" + args[0]) demo\n= gr.TabbedInterface([hello_world, bye_world, chat], [\"Hello World\", \"Bye\nWorld\", \"Chat\"]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    hello_world = gr.Interface(lambda name: \"Hello \" + name, \"text\", \"text\")\n    bye_world = gr.Interface(lambda name: \"Bye \" + name, \"text\", \"text\")\n    chat = gr.ChatInterface(lambda *args: \"Hello \" + args[0])\n    \n    demo = gr.TabbedInterface([hello_world, bye_world, chat], [\"Hello World\", \"Bye World\", \"Chat\"])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/tabbedinterface", "source_page_title": "Gradio - Tabbedinterface Docs"}, {"text": "Creates a component to display arbitrary HTML output. As this component\ndoes not accept user input, it is rarely used as an input component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "**As input component** : (Rarely used) passes the HTML as a `str`.\n\nYour function should accept one of these types:\n\n    \n    \n    def predict(\n    \tvalue: str | None\n    )\n    \t...\n\n  \n\n**As output component** : Expects a `str` consisting of valid HTML.\n\nYour function should return one of these types:\n\n    \n    \n    def predict(\u00b7\u00b7\u00b7) -> str | None\n    \t...\t\n    \treturn value\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: str | Callable | None\n\ndefault `= None`\n\nThe HTML content to display. Only static HTML is rendered (e.g. no JavaScript.\nTo render JavaScript, use the `js` or `head` parameters in the `Blocks`\nconstructor). If a function is provided, the function will be called each time\nthe app loads to set the initial value of this component.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nThe label for this component. Is used as the header if there are a table of\nexamples for this component. If None and used in a `gr.Interface`, the label\nwill be the name of the parameter this component is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool\n\ndefault `= False`\n\nIf True, the label will be displayed. If False, the label will be hidden.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    k", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "ault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    min_height: int | None\n\ndefault `= None`\n\nThe minimum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If HTML content exceeds the\nheight, the component will expand to fit the content.\n\n\n    \n    \n    max_height: int | None\n\ndefault `= None`\n\nThe maximum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If content exceeds the height,\nthe component will scroll.\n\n\n    \n    \n    container: bool\n\ndefault `= False`\n\nIf True, the HTML component will be displayed in a container. Default is\nFalse.\n\n\n    \n    \n    padding: bool\n\ndefault `= True`\n\nIf True, the HTML component will have a certain padding (set by the `--block-\npadding` CSS variable) in all directions. Default is True.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.HTML` | \"html\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "blocks_scroll\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr demo = gr.Blocks() with demo: inp =\ngr.Textbox(placeholder=\"Enter text.\") scroll_btn = gr.Button(\"Scroll\")\nno_scroll_btn = gr.Button(\"No Scroll\") big_block = gr.HTML(\"\"\" <div\nstyle='height: 800px; width: 100px; background-color: pink;'></div> \"\"\") out =\ngr.Textbox() scroll_btn.click(lambda x: x, inputs=inp, outputs=out,\nscroll_to_output=True) no_scroll_btn.click(lambda x: x, inputs=inp,\noutputs=out) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    demo = gr.Blocks()\n    \n    with demo:\n        inp = gr.Textbox(placeholder=\"Enter text.\")\n        scroll_btn = gr.Button(\"Scroll\")\n        no_scroll_btn = gr.Button(\"No Scroll\")\n        big_block = gr.HTML(\"\"\"\n        \n    \n    \n        \"\"\")\n        out = gr.Textbox()\n    \n        scroll_btn.click(lambda x: x,\n                   inputs=inp,\n                   outputs=out,\n                    scroll_to_output=True)\n        no_scroll_btn.click(lambda x: x,\n                   inputs=inp,\n                   outputs=out)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe HTML component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`HTML.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the HTML changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`HTML.click(fn, \u00b7\u00b7\u00b7)` | Triggered when the HTML is clicked.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API do", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "i_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": ".\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "er the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/html", "source_page_title": "Gradio - Html Docs"}, {"text": "Creates a chatbot that displays user-submitted messages and responses.\nSupports a subset of Markdown including bold, italics, code, tables. Also\nsupports audio/video/image files, which are displayed in the Chatbot, and\nother kinds of files which are displayed as links. This component is usually\nused as an output component.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "The data format accepted by the Chatbot is dictated by the `type` parameter.\nThis parameter can take two values, `'tuples'` and `'messages'`. The\n`'tuples'` type is deprecated and will be removed in a future version of\nGradio.\n\n", "heading1": "Behavior", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "If the `type` is `'messages'`, then the data sent to/from the chatbot will be\na list of dictionaries with `role` and `content` keys. This format is\ncompliant with the format expected by most LLM APIs (HuggingChat, OpenAI,\nClaude). The `role` key is either `'user'` or `'assistant'` and the `content`\nkey can be one of the following should be a string (rendered as markdown/html)\nor a Gradio component (useful for displaying files).\n\nAs an example:\n\n    \n    \n    import gradio as gr\n    \n    history = [\n        {\"role\": \"assistant\", \"content\": \"I am happy to provide you that report and plot.\"},\n        {\"role\": \"assistant\", \"content\": gr.Plot(value=make_plot_from_file('quaterly_sales.txt'))}\n    ]\n    \n    with gr.Blocks() as demo:\n        gr.Chatbot(history, type=\"messages\")\n    \n    demo.launch()\n\nFor convenience, you can use the `ChatMessage` dataclass so that your text\neditor can give you autocomplete hints and typechecks.\n\n    \n    \n    import gradio as gr\n    \n    history = [\n        gr.ChatMessage(role=\"assistant\", content=\"How can I help you?\"),\n        gr.ChatMessage(role=\"user\", content=\"Can you make me a plot of quarterly sales?\"),\n        gr.ChatMessage(role=\"assistant\", content=\"I am happy to provide you that report and plot.\")\n    ]\n    \n    with gr.Blocks() as demo:\n        gr.Chatbot(history, type=\"messages\")\n    \n    demo.launch()\n\n", "heading1": "Message format", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    value: list[MessageDict | Message] | TupleFormat | Callable | None\n\ndefault `= None`\n\nDefault list of messages to show in chatbot, where each message is of the\nformat {\"role\": \"user\", \"content\": \"Help me.\"}. Role can be one of \"user\",\n\"assistant\", or \"system\". Content should be either text, or media passed as a\nGradio component, e.g. {\"content\": gr.Image(\"lion.jpg\")}. If a function is\nprovided, the function will be called each time the app loads to set the\ninitial value of this component.\n\n\n    \n    \n    type: Literal['messages', 'tuples'] | None\n\ndefault `= None`\n\nThe format of the messages passed into the chat history parameter of `fn`. If \"messages\", passes the value as a list of dictionaries with openai-style \"role\" and \"content\" keys. The \"content\" key's value should be one of the following - (1) strings in valid Markdown (2) a dictionary with a \"path\" key and value corresponding to the file to display or (3) an instance of a Gradio component. At the moment Image, Plot, Video, Gallery, Audio, HTML, and Model3D are supported. The \"role\" key should be one of 'user' or 'assistant'. Any other roles will not be displayed in the output. If this parameter is 'tuples', expects a `list[list[str | None | tuple]]`, i.e. a list of lists. The inner list should have 2 elements: the user message and the response message, but this format is deprecated.\n\n\n    \n    \n    label: str | I18nData | None\n\ndefault `= None`\n\nthe label for this component. Appears above the component and is also used as\nthe header if there are a table of examples for this component. If None and\nused in a `gr.Interface`, the label will be the name of the parameter this\ncomponent is assigned to.\n\n\n    \n    \n    every: Timer | float | None\n\ndefault `= None`\n\nContinously calls `value` to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n   ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": " to recalculate it if `value` is a function (has no\neffect otherwise). Can provide a Timer whose tick resets `value`, or a float\nthat provides the regular interval for the reset Timer.\n\n\n    \n    \n    inputs: Component | list[Component] | set[Component] | None\n\ndefault `= None`\n\nComponents that are used as inputs to calculate `value` if `value` is a\nfunction (has no effect otherwise). `value` is recalculated any time the\ninputs change.\n\n\n    \n    \n    show_label: bool | None\n\ndefault `= None`\n\nif True, will display label.\n\n\n    \n    \n    container: bool\n\ndefault `= True`\n\nIf True, will place the component in a container - providing some extra\npadding around the border.\n\n\n    \n    \n    scale: int | None\n\ndefault `= None`\n\nrelative size compared to adjacent Components. For example if Components A and\nB are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide\nas B. Should be an integer. scale applies in Rows, and to top-level Components\nin Blocks where fill_height=True.\n\n\n    \n    \n    min_width: int\n\ndefault `= 160`\n\nminimum pixel width, will wrap if not sufficient screen space to satisfy this\nvalue. If a certain scale value results in this Component being narrower than\nmin_width, the min_width parameter will be respected first.\n\n\n    \n    \n    visible: bool\n\ndefault `= True`\n\nIf False, component will be hidden.\n\n\n    \n    \n    elem_id: str | None\n\ndefault `= None`\n\nAn optional string that is assigned as the id of this component in the HTML\nDOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    elem_classes: list[str] | str | None\n\ndefault `= None`\n\nAn optional list of strings that are assigned as the classes of this component\nin the HTML DOM. Can be used for targeting CSS styles.\n\n\n    \n    \n    autoscroll: bool\n\ndefault `= True`\n\nIf True, will automatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    render", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "omatically scroll to the bottom of the textbox when the value\nchanges, unless the user scrolls up. If False, will not scroll to the bottom\nof the textbox when the value changes.\n\n\n    \n    \n    render: bool\n\ndefault `= True`\n\nIf False, component will not render be rendered in the Blocks context. Should\nbe used if the intention is to assign event listeners now but render the\ncomponent later.\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nin a gr.render, Components with the same key across re-renders are treated as\nthe same component, not a new component. Properties set in 'preserved_by_key'\nare not reset across a re-render.\n\n\n    \n    \n    preserved_by_key: list[str] | str | None\n\ndefault `= \"value\"`\n\nA list of parameters from this component's constructor. Inside a gr.render()\nfunction, if a component is re-rendered with the same key, these (and only\nthese) parameters will be preserved in the UI (if they have been changed by\nthe user or an event listener) instead of re-rendered based on the values\nprovided during constructor.\n\n\n    \n    \n    height: int | str | None\n\ndefault `= 400`\n\nThe height of the component, specified in pixels if a number is passed, or in\nCSS units if a string is passed. If messages exceed the height, the component\nwill scroll.\n\n\n    \n    \n    resizable: bool\n\ndefault `= False`\n\nIf True, the user of the Gradio app can resize the chatbot by dragging the\nbottom right corner.\n\n\n    \n    \n    resizeable: bool\n\ndefault `= False`\n\n\n    \n    \n    max_height: int | str | None\n\ndefault `= None`\n\nThe maximum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If messages exceed the height,\nthe component will scroll. If messages are shorter than the height, the\ncomponent will shrink to fit the content. Will not have any effect if `height`\nis set and is smaller than `max_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the c", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "ill shrink to fit the content. Will not have any effect if `height`\nis set and is smaller than `max_height`.\n\n\n    \n    \n    min_height: int | str | None\n\ndefault `= None`\n\nThe minimum height of the component, specified in pixels if a number is\npassed, or in CSS units if a string is passed. If messages exceed the height,\nthe component will expand to fit the content. Will not have any effect if\n`height` is set and is larger than `min_height`.\n\n\n    \n    \n    editable: Literal['user', 'all'] | None\n\ndefault `= None`\n\nAllows user to edit messages in the chatbot. If set to \"user\", allows editing\nof user messages. If set to \"all\", allows editing of assistant messages as\nwell.\n\n\n    \n    \n    latex_delimiters: list[dict[str, str | bool]] | None\n\ndefault `= None`\n\nA list of dicts of the form {\"left\": open delimiter (str), \"right\": close\ndelimiter (str), \"display\": whether to display in newline (bool)} that will be\nused to render LaTeX expressions. If not provided, `latex_delimiters` is set\nto `[{ \"left\": \"$$\", \"right\": \"$$\", \"display\": True }]`, so only expressions\nenclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass\nin an empty list to disable LaTeX rendering. For more information, see the\n[KaTeX documentation](https://katex.org/docs/autorender.html).\n\n\n    \n    \n    rtl: bool\n\ndefault `= False`\n\nIf True, sets the direction of the rendered text to right-to-left. Default is\nFalse, which renders text left-to-right.\n\n\n    \n    \n    show_share_button: bool | None\n\ndefault `= None`\n\nIf True, will show a share icon in the corner of the component that allows\nuser to share outputs to Hugging Face Spaces Discussions. If False, icon does\nnot appear. If set to None (default behavior), then the icon appears if this\nGradio app is launched on Spaces, but not otherwise.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, will show a copy button for each chatbot message.\n\n\n    \n    \n    watermark: str | None\n\ndefault `= None`\n\nIf provided, this tex", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "se.\n\n\n    \n    \n    show_copy_button: bool\n\ndefault `= False`\n\nIf True, will show a copy button for each chatbot message.\n\n\n    \n    \n    watermark: str | None\n\ndefault `= None`\n\nIf provided, this text will be appended to the end of messages copied from the\nchatbot, after a blank line. Useful for indicating that the message is\ngenerated by an AI model.\n\n\n    \n    \n    avatar_images: tuple[str | Path | None, str | Path | None] | None\n\ndefault `= None`\n\nTuple of two avatar image paths or URLs for user and bot (in that order). Pass\nNone for either the user or bot image to skip. Must be within the working\ndirectory of the Gradio app or an external URL.\n\n\n    \n    \n    sanitize_html: bool\n\ndefault `= True`\n\nIf False, will disable HTML sanitization for chatbot messages. This is not\nrecommended, as it can lead to security vulnerabilities.\n\n\n    \n    \n    render_markdown: bool\n\ndefault `= True`\n\nIf False, will disable Markdown rendering for chatbot messages.\n\n\n    \n    \n    feedback_options: list[str] | tuple[str, ...] | None\n\ndefault `= ('Like', 'Dislike')`\n\nA list of strings representing the feedback options that will be displayed to\nthe user. The exact case-sensitive strings \"Like\" and \"Dislike\" will render as\nthumb icons, but any other choices will appear under a separate flag icon.\n\n\n    \n    \n    feedback_value: list[str | None] | None\n\ndefault `= None`\n\nA list of strings representing the feedback state for entire chat. Only works\nwhen type=\"messages\". Each entry in the list corresponds to that assistant\nmessage, in order, and the value is the feedback given (e.g. \"Like\",\n\"Dislike\", or any custom feedback option) or None if no feedback was given for\nthat message.\n\n\n    \n    \n    bubble_full_width: <class 'inspect._empty'>\n\ndefault `= None`\n\nDeprecated.\n\n\n    \n    \n    line_breaks: bool\n\ndefault `= True`\n\nIf True (default), will enable Github-flavored Markdown line breaks in chatbot\nmessages. If False, single new lines will be ignored. Only applies if\n`render_markdown`", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "e_breaks: bool\n\ndefault `= True`\n\nIf True (default), will enable Github-flavored Markdown line breaks in chatbot\nmessages. If False, single new lines will be ignored. Only applies if\n`render_markdown` is True.\n\n\n    \n    \n    layout: Literal['panel', 'bubble'] | None\n\ndefault `= None`\n\nIf \"panel\", will display the chatbot in a llm style layout. If \"bubble\", will\ndisplay the chatbot with message bubbles, with the user and bot messages on\nalterating sides. Will default to \"bubble\".\n\n\n    \n    \n    placeholder: str | None\n\ndefault `= None`\n\na placeholder message to display in the chatbot when it is empty. Centered\nvertically and horizontally in the Chatbot. Supports Markdown and HTML. If\nNone, no placeholder is displayed.\n\n\n    \n    \n    examples: list[ExampleMessage] | None\n\ndefault `= None`\n\nA list of example messages to display in the chatbot before any user/assistant\nmessages are shown. Each example should be a dictionary with an optional\n\"text\" key representing the message that should be populated in the Chatbot\nwhen clicked, an optional \"files\" key, whose value should be a list of files\nto populate in the Chatbot, an optional \"icon\" key, whose value should be a\nfilepath or URL to an image to display in the example box, and an optional\n\"display_text\" key, whose value should be the text to display in the example\nbox. If \"display_text\" is not provided, the value of \"text\" will be displayed.\n\n\n    \n    \n    show_copy_all_button: <class 'inspect._empty'>\n\ndefault `= False`\n\nIf True, will show a copy all button that copies all chatbot messages to the\nclipboard.\n\n\n    \n    \n    allow_file_downloads: <class 'inspect._empty'>\n\ndefault `= True`\n\nIf True, will show a download button for chatbot messages that contain media.\nDefaults to True.\n\n\n    \n    \n    group_consecutive_messages: bool\n\ndefault `= True`\n\nIf True, will display consecutive messages from the same role in the same\nbubble. If False, will display each message in a separate bubble. Defaults to\nTrue.\n\n\n    \n    ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "sages: bool\n\ndefault `= True`\n\nIf True, will display consecutive messages from the same role in the same\nbubble. If False, will display each message in a separate bubble. Defaults to\nTrue.\n\n\n    \n    \n    allow_tags: list[str] | bool\n\ndefault `= False`\n\nIf a list of tags is provided, these tags will be preserved in the output\nchatbot messages, even if `sanitize_html` is `True`. For example, if this list\nis [\"thinking\"], the tags `<thinking>` and `</thinking>` will not be removed.\nIf True, all custom tags (non-standard HTML tags) will be preserved. If False,\nno tags will be preserved (default behavior).\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "Class | Interface String Shortcut | Initialization  \n---|---|---  \n`gradio.Chatbot` | \"chatbot\" | Uses default values  \n  \n", "heading1": "Shortcuts", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "**Displaying Thoughts/Tool Usage**\n\nWhen `type` is `messages`, you can provide additional metadata regarding any\ntools used to generate the response. This is useful for displaying the thought\nprocess of LLM agents. For example,\n\n    \n    \n    def generate_response(history):\n        history.append(\n            ChatMessage(role=\"assistant\",\n                        content=\"The weather API says it is 20 degrees Celcius in New York.\",\n                        metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool Weather API\"})\n            )\n        return history\n\nWould be displayed as following:\n\n![Gradio chatbot tool display](https://github.com/user-\nattachments/assets/c1514bc9-bc29-4af1-8c3f-cd4a7c2b217f)\n\nYou can also specify metadata with a plain python dictionary,\n\n    \n    \n    def generate_response(history):\n        history.append(\n            dict(role=\"assistant\",\n                 content=\"The weather API says it is 20 degrees Celcius in New York.\",\n                 metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool Weather API\"})\n            )\n        return history\n\n**Using Gradio Components Inside`gr.Chatbot`**\n\nThe `Chatbot` component supports using many of the core Gradio components\n(such as `gr.Image`, `gr.Plot`, `gr.Audio`, and `gr.HTML`) inside of the\nchatbot. Simply include one of these components in your list of tuples. Here\u2019s\nan example:\n\n    \n    \n    import gradio as gr\n    \n    def load():\n        return [\n            (\"Here's an audio\", gr.Audio(\"https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav\")),\n            (\"Here's an video\", gr.Video(\"https://github.com/gradio-app/gradio/raw/main/demo/video_component/files/world.mp4\"))\n        ]\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot()\n        button = gr.Button(\"Load audio and video\")\n        button.click(load, None, chatbot)\n    \n    demo.launch()\n\n", "heading1": "Examples", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "chatbot_simplechatbot_streamingchatbot_with_toolschatbot_core_components\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import random import time with gr.Blocks() as\ndemo: chatbot = gr.Chatbot(type=\"messages\") msg = gr.Textbox() clear =\ngr.ClearButton([msg, chatbot]) def respond(message, chat_history): bot_message\n= random.choice([\"How are you?\", \"Today is a great day\", \"I'm very hungry\"])\nchat_history.append({\"role\": \"user\", \"content\": message})\nchat_history.append({\"role\": \"assistant\", \"content\": bot_message})\ntime.sleep(2) return \"\", chat_history msg.submit(respond, [msg, chatbot],\n[msg, chatbot]) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import random\n    import time\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(type=\"messages\")\n        msg = gr.Textbox()\n        clear = gr.ClearButton([msg, chatbot])\n    \n        def respond(message, chat_history):\n            bot_message = random.choice([\"How are you?\", \"Today is a great day\", \"I'm very hungry\"])\n            chat_history.append({\"role\": \"user\", \"content\": message})\n            chat_history.append({\"role\": \"assistant\", \"content\": bot_message})\n            time.sleep(2)\n            return \"\", chat_history\n    \n        msg.submit(respond, [msg, chatbot], [msg, chatbot])\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import random import time with gr.Blocks() as\ndemo: chatbot = gr.Chatbot(type=\"messages\") msg = gr.Textbox() clear =\ngr.Button(\"Clear\") def user(user_message, history: list): return \"\", history +\n[{\"role\": \"user\", \"content\": user_message}] def bot(history: list):\nbot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\nhistory.append({\"role\": \"assistant\", \"content\": \"\"}) for character in\nbot_message: history[-1]['content'] += character time.sleep(0.05) yield\nhistory msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\nbot, chatbot, chatbot ) clear.click(lambda: None, N", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "\nbot_message: history[-1]['content'] += character time.sleep(0.05) yield\nhistory msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\nbot, chatbot, chatbot ) clear.click(lambda: None, None, chatbot, queue=False)\nif __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    import random\n    import time\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(type=\"messages\")\n        msg = gr.Textbox()\n        clear = gr.Button(\"Clear\")\n    \n        def user(user_message, history: list):\n            return \"\", history + [{\"role\": \"user\", \"content\": user_message}]\n    \n        def bot(history: list):\n            bot_message = random.choice([\"How are you?\", \"I love you\", \"I'm very hungry\"])\n            history.append({\"role\": \"assistant\", \"content\": \"\"})\n            for character in bot_message:\n                history[-1]['content'] += character\n                time.sleep(0.05)\n                yield history\n    \n        msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False).then(\n            bot, chatbot, chatbot\n        )\n        clear.click(lambda: None, None, chatbot, queue=False)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr from gradio import ChatMessage import time def\ngenerate_response(history): history.append( ChatMessage( role=\"user\",\ncontent=\"What is the weather in San Francisco right now?\" ) ) yield history\ntime.sleep(0.25) history.append( ChatMessage( role=\"assistant\", content=\"In\norder to find the current weather in San Francisco, I will need to use my\nweather tool.\", ) ) yield history time.sleep(0.25) history.append(\nChatMessage( role=\"assistant\", content=\"API Error when connecting to weather\nservice.\", metadata={\"title\": \"\ud83d\udca5 Error using tool 'Weather'\"}, ) ) yield\nhistory time.sleep(0.25) history.append( ChatMessage( role=\"assistant\",\ncontent=\"I will try again\", ) ) yield history time.sleep(0.25) history.append(\nChatMessage( role=\"assistant\", content=\"", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": " ) yield\nhistory time.sleep(0.25) history.append( ChatMessage( role=\"assistant\",\ncontent=\"I will try again\", ) ) yield history time.sleep(0.25) history.append(\nChatMessage( role=\"assistant\", content=\"Weather 72 degrees Fahrenheit with 20%\nchance of rain.\", metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool 'Weather'\"}, ) ) yield\nhistory time.sleep(0.25) history.append( ChatMessage( role=\"assistant\",\ncontent=\"Now that the API succeeded I can complete my task.\", ) ) yield\nhistory time.sleep(0.25) history.append( ChatMessage( role=\"assistant\",\ncontent=\"It's a sunny day in San Francisco with a current temperature of 72\ndegrees Fahrenheit and a 20% chance of rain. Enjoy the weather!\", ) ) yield\nhistory def like(evt: gr.LikeData): print(\"User liked the response\")\nprint(evt.index, evt.liked, evt.value) with gr.Blocks() as demo: chatbot =\ngr.Chatbot(type=\"messages\", height=500, show_copy_button=True) button =\ngr.Button(\"Get San Francisco Weather\") button.click(generate_response,\nchatbot, chatbot) chatbot.like(like) if __name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    from gradio import ChatMessage\n    import time\n    \n    def generate_response(history):\n        history.append(\n            ChatMessage(\n                role=\"user\", content=\"What is the weather in San Francisco right now?\"\n            )\n        )\n        yield history\n        time.sleep(0.25)\n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"In order to find the current weather in San Francisco, I will need to use my weather tool.\",\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"API Error when connecting to weather service.\",\n                metadata={\"title\": \"\ud83d\udca5 Error using tool 'Weather'\"},\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMes", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "r service.\",\n                metadata={\"title\": \"\ud83d\udca5 Error using tool 'Weather'\"},\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"I will try again\",\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"Weather 72 degrees Fahrenheit with 20% chance of rain.\",\n                metadata={\"title\": \"\ud83d\udee0\ufe0f Used tool 'Weather'\"},\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"Now that the API succeeded I can complete my task.\",\n            )\n        )\n        yield history\n        time.sleep(0.25)\n    \n        history.append(\n            ChatMessage(\n                role=\"assistant\",\n                content=\"It's a sunny day in San Francisco with a current temperature of 72 degrees Fahrenheit and a 20% chance of rain. Enjoy the weather!\",\n            )\n        )\n        yield history\n    \n    def like(evt: gr.LikeData):\n        print(\"User liked the response\")\n        print(evt.index, evt.liked, evt.value)\n    \n    with gr.Blocks() as demo:\n        chatbot = gr.Chatbot(type=\"messages\", height=500, show_copy_button=True)\n        button = gr.Button(\"Get San Francisco Weather\")\n        button.click(generate_response, chatbot, chatbot)\n        chatbot.like(like)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr import os import plotly.express as px import\nrandom Chatbot demo with multimodal input (text, markdown, LaTeX, code\nblocks, image, audio, video, & model3d). Plus shows support for streaming\ntext. txt = \"\"\" Absolutely! The mycorrhizal network, often referred to as the\n\"Wood Wide Web,\" is a symbiotic association between fungi ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "age, audio, video, & model3d). Plus shows support for streaming\ntext. txt = \"\"\" Absolutely! The mycorrhizal network, often referred to as the\n\"Wood Wide Web,\" is a symbiotic association between fungi and the roots of\nmost plant species. Here\u2019s a deeper dive into how it works and its\nimplications: How It Works 1\\. **Symbiosis**: Mycorrhizal fungi attach to\nplant roots, extending far into the soil. The plant provides the fungi with\ncarbohydrates produced via photosynthesis. In return, the fungi help the plant\nabsorb water and essential nutrients like phosphorus and nitrogen from the\nsoil. 2\\. **Network Formation**: The fungal hyphae (thread-like structures)\nconnect individual plants, creating an extensive underground network. This\nnetwork can link many plants together, sometimes spanning entire forests. 3\\.\n**Communication**: Trees and plants use this network to communicate and share\nresources. For example, a tree under attack by pests can send chemical signals\nthrough the mycorrhizal network to warn neighboring trees. These trees can\nthen produce defensive chemicals to prepare for the impending threat. \nBenefits and Functions 1\\. **Resource Sharing**: The network allows for the\nredistribution of resources among plants. For instance, a large, established\ntree might share excess nutrients and water with smaller, younger trees,\npromoting overall forest health. 2\\. **Defense Mechanism**: The ability to\nshare information about pests and diseases enhances the resilience of plant\ncommunities. This early warning system helps plants activate their defenses\nbefore they are directly affected. 3\\. **Support for Seedlings**: Young\nseedlings, which have limited root systems, benefit immensely from the\nmycorrhizal network. They receive nutrients and water from larger plants,\nincreasing their chances of survival and growth. Ecological Impact 1\\.\n**Biodiversity**: The mycorrhizal network supports biodiversity by fostering a\ncooperative environment. Plants of different spec", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "reasing their chances of survival and growth. Ecological Impact 1\\.\n**Biodiversity**: The mycorrhizal network supports biodiversity by fostering a\ncooperative environment. Plants of different species can coexist and thrive\nbecause of the shared resources and information. 2\\. **Forest Health**: The\nnetwork enhances the overall health of forests. By enabling efficient nutrient\ncycling and supporting plant defenses, it contributes to the stability and\nlongevity of forest ecosystems. 3\\. **Climate Change Mitigation**: Healthy\nforests act as significant carbon sinks, absorbing carbon dioxide from the\natmosphere. The mycorrhizal network plays a critical role in maintaining\nforest health and, consequently, in mitigating climate change. Research\nand Discoveries 1\\. **Suzanne Simard's Work**: Ecologist Suzanne Simard\u2019s\nresearch has been pivotal in uncovering the complexities of the mycorrhizal\nnetwork. She demonstrated that trees of different species can share resources\nand that \"mother trees\" (large, older trees) play a crucial role in nurturing\nyounger plants. 2\\. **Implications for Conservation**: Understanding the\nmycorrhizal network has significant implications for conservation efforts. It\nhighlights the importance of preserving not just individual trees but entire\necosystems, including the fungal networks that sustain them. Practical\nApplications 1\\. **Agriculture**: Farmers and horticulturists are exploring\nthe use of mycorrhizal fungi to improve crop yields and soil health. By\nincorporating these fungi into agricultural practices, they can reduce the\nneed for chemical fertilizers and enhance plant resilience. 2\\.\n**Reforestation**: In reforestation projects, introducing mycorrhizal fungi\ncan accelerate the recovery of degraded lands. The fungi help establish\nhealthy plant communities, ensuring the success of newly planted trees. The\n\"Wood Wide Web\" exemplifies the intricate and often hidden connections that\nsustain life on Earth. It\u2019s a reminder of the pr", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "healthy plant communities, ensuring the success of newly planted trees. The\n\"Wood Wide Web\" exemplifies the intricate and often hidden connections that\nsustain life on Earth. It\u2019s a reminder of the profound interdependence within\nnatural systems and the importance of preserving these delicate relationships.\n\"\"\" def random_plot(): df = px.data.iris() fig = px.scatter( df,\nx=\"sepal_width\", y=\"sepal_length\", color=\"species\", size=\"petal_length\",\nhover_data=[\"petal_width\"], ) return fig color_map = { \"harmful\": \"crimson\",\n\"neutral\": \"gray\", \"beneficial\": \"green\", } def html_src(harm_level): return\nf\"\"\" <div style=\"display: flex; gap: 5px;\"> <div style=\"background-color:\n{color_map[harm_level]}; padding: 2px; border-radius: 5px;\"> {harm_level}\n</div> </div> \"\"\" def print_like_dislike(x: gr.LikeData): print(x.index,\nx.value, x.liked) def random_bokeh_plot(): from bokeh.models import\nColumnDataSource, Whisker from bokeh.plotting import figure from\nbokeh.sampledata.autompg2 import autompg2 as df from bokeh.transform import\nfactor_cmap, jitter classes = sorted(df[\"class\"].unique()) p = figure(\nheight=400, x_range=classes, background_fill_color=\"efefef\", title=\"Car class\nvs HWY mpg with quintile ranges\", ) p.xgrid.grid_line_color = None g =\ndf.groupby(\"class\") upper = g.hwy.quantile(0.80) lower = g.hwy.quantile(0.20)\nsource = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\nerror = Whisker( base=\"base\", upper=\"upper\", lower=\"lower\", source=source,\nlevel=\"annotation\", line_width=2, ) error.upper_head.size = 20\nerror.lower_head.size = 20 p.add_layout(error) p.circle( jitter(\"class\", 0.3,\nrange=p.x_range), \"hwy\", source=df, alpha=0.5, size=13, line_color=\"white\",\ncolor=factor_cmap(\"class\", \"Light6\", classes), ) return p def\nrandom_matplotlib_plot(): import numpy as np import pandas as pd import\nmatplotlib.pyplot as plt countries = [\"USA\", \"Canada\", \"Mexico\", \"UK\"] months\n= [\"January\", \"February\", \"March\", \"April\", \"May\"] m = months.index(\"January\")\nr = 3.2 st", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "as np import pandas as pd import\nmatplotlib.pyplot as plt countries = [\"USA\", \"Canada\", \"Mexico\", \"UK\"] months\n= [\"January\", \"February\", \"March\", \"April\", \"May\"] m = months.index(\"January\")\nr = 3.2 start_day = 30 * m final_day = 30 * (m + 1) x = np.arange(start_day,\nfinal_day + 1) pop_count = {\"USA\": 350, \"Canada\": 40, \"Mexico\": 300, \"UK\":\n120} df = pd.DataFrame({\"day\": x}) for country in countries: df[country] = x\n** (r) * (pop_count[country] + 1) fig = plt.figure() plt.plot(df[\"day\"],\ndf[countries].to_numpy()) plt.title(\"Outbreak in \" + \"January\")\nplt.ylabel(\"Cases\") plt.xlabel(\"Days since Day 0\") plt.legend(countries)\nreturn fig def add_message(history, message): for x in message[\"files\"]:\nhistory.append({\"role\": \"user\", \"content\": {\"path\": x}}) if message[\"text\"] is\nnot None: history.append({\"role\": \"user\", \"content\": message[\"text\"]}) return\nhistory, gr.MultimodalTextbox(value=None, interactive=False) def bot(history,\nresponse_type): msg = {\"role\": \"assistant\", \"content\": \"\"} if response_type ==\n\"plot\": content = gr.Plot(random_plot()) elif response_type == \"bokeh_plot\":\ncontent = gr.Plot(random_bokeh_plot()) elif response_type ==\n\"matplotlib_plot\": content = gr.Plot(random_matplotlib_plot()) elif\nresponse_type == \"gallery\": content = gr.Gallery( [os.path.join(\"files\",\n\"avatar.png\"), os.path.join(\"files\", \"avatar.png\")] ) elif response_type ==\n\"dataframe\": content = gr.Dataframe( interactive=True, headers=[\"One\", \"Two\",\n\"Three\"], col_count=(3, \"fixed\"), row_count=(3, \"fixed\"), value=[[1, 2, 3],\n[4, 5, 6], [7, 8, 9]], label=\"Dataframe\", ) elif response_type == \"image\":\ncontent = gr.Image(os.path.join(\"files\", \"avatar.png\")) elif response_type ==\n\"video\": content = gr.Video(os.path.join(\"files\", \"world.mp4\")) elif\nresponse_type == \"audio\": content = gr.Audio(os.path.join(\"files\",\n\"audio.wav\")) elif response_type == \"audio_file\": content = {\"path\":\nos.path.join(\"files\", \"audio.wav\"), \"alt_text\": \"description\"} elif\nresponse_type == \"image_file\": content = {\"path\":", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "join(\"files\",\n\"audio.wav\")) elif response_type == \"audio_file\": content = {\"path\":\nos.path.join(\"files\", \"audio.wav\"), \"alt_text\": \"description\"} elif\nresponse_type == \"image_file\": content = {\"path\": os.path.join(\"files\",\n\"avatar.png\"), \"alt_text\": \"description\"} elif response_type == \"video_file\":\ncontent = {\"path\": os.path.join(\"files\", \"world.mp4\"), \"alt_text\":\n\"description\"} elif response_type == \"txt_file\": content = {\"path\":\nos.path.join(\"files\", \"sample.txt\"), \"alt_text\": \"description\"} elif\nresponse_type == \"model3d_file\": content = {\"path\": os.path.join(\"files\",\n\"Duck.glb\"), \"alt_text\": \"description\"} elif response_type == \"html\": content\n= gr.HTML( html_src(random.choice([\"harmful\", \"neutral\", \"beneficial\"])) )\nelif response_type == \"model3d\": content = gr.Model3D(os.path.join(\"files\",\n\"Duck.glb\")) else: content = txt msg[\"content\"] = content history.append(msg)\nreturn history fig = random_plot() with gr.Blocks(fill_height=True) as demo:\nchatbot = gr.Chatbot( elem_id=\"chatbot\", type=\"messages\",\nbubble_full_width=False, scale=1, show_copy_button=True, avatar_images=( None,\nos.path.join(\"files\", \"avatar.png\"), os.path.join(\"files\", \"avatar.png\"), ),\n) response_type = gr.Radio( [ \"audio_file\", \"image_file\", \"video_file\",\n\"txt_file\", \"model3d_file\", \"plot\", \"matplotlib_plot\", \"bokeh_plot\", \"image\",\n\"text\", \"gallery\", \"dataframe\", \"video\", \"audio\", \"html\", \"model3d\", ],\nvalue=\"text\", label=\"Response Type\", ) chat_input = gr.MultimodalTextbox(\ninteractive=True, placeholder=\"Enter message or upload file...\",\nshow_label=False, ) chat_msg = chat_input.submit( add_message, [chatbot,\nchat_input], [chatbot, chat_input] ) bot_msg = chat_msg.then( bot, [chatbot,\nresponse_type], chatbot, api_name=\"bot_response\" ) bot_msg.then(lambda:\ngr.MultimodalTextbox(interactive=True), None, [chat_input])\nchatbot.like(print_like_dislike, None, None) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    import os\n    import plotly.express as px\n    import ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": ", None, [chat_input])\nchatbot.like(print_like_dislike, None, None) if __name__ == \"__main__\":\ndemo.launch()\n\n    \n    \n    import gradio as gr\n    import os\n    import plotly.express as px\n    import random\n    \n    Chatbot demo with multimodal input (text, markdown, LaTeX, code blocks, image, audio, video, & model3d). Plus shows support for streaming text.\n    \n    txt = \"\"\"\n    Absolutely! The mycorrhizal network, often referred to as the \"Wood Wide Web,\" is a symbiotic association between fungi and the roots of most plant species. Here\u2019s a deeper dive into how it works and its implications:\n    \n    How It Works\n    \n    1. **Symbiosis**: Mycorrhizal fungi attach to plant roots, extending far into the soil. The plant provides the fungi with carbohydrates produced via photosynthesis. In return, the fungi help the plant absorb water and essential nutrients like phosphorus and nitrogen from the soil.\n    \n    2. **Network Formation**: The fungal hyphae (thread-like structures) connect individual plants, creating an extensive underground network. This network can link many plants together, sometimes spanning entire forests.\n    \n    3. **Communication**: Trees and plants use this network to communicate and share resources. For example, a tree under attack by pests can send chemical signals through the mycorrhizal network to warn neighboring trees. These trees can then produce defensive chemicals to prepare for the impending threat.\n    \n    Benefits and Functions\n    \n    1. **Resource Sharing**: The network allows for the redistribution of resources among plants. For instance, a large, established tree might share excess nutrients and water with smaller, younger trees, promoting overall forest health.\n    \n    2. **Defense Mechanism**: The ability to share information about pests and diseases enhances the resilience of plant communities. This early warning system helps plants activate their defenses before they are directly affected.\n    \n    3. **Support ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "ormation about pests and diseases enhances the resilience of plant communities. This early warning system helps plants activate their defenses before they are directly affected.\n    \n    3. **Support for Seedlings**: Young seedlings, which have limited root systems, benefit immensely from the mycorrhizal network. They receive nutrients and water from larger plants, increasing their chances of survival and growth.\n    \n    Ecological Impact\n    \n    1. **Biodiversity**: The mycorrhizal network supports biodiversity by fostering a cooperative environment. Plants of different species can coexist and thrive because of the shared resources and information.\n    \n    2. **Forest Health**: The network enhances the overall health of forests. By enabling efficient nutrient cycling and supporting plant defenses, it contributes to the stability and longevity of forest ecosystems.\n    \n    3. **Climate Change Mitigation**: Healthy forests act as significant carbon sinks, absorbing carbon dioxide from the atmosphere. The mycorrhizal network plays a critical role in maintaining forest health and, consequently, in mitigating climate change.\n    \n    Research and Discoveries\n    \n    1. **Suzanne Simard's Work**: Ecologist Suzanne Simard\u2019s research has been pivotal in uncovering the complexities of the mycorrhizal network. She demonstrated that trees of different species can share resources and that \"mother trees\" (large, older trees) play a crucial role in nurturing younger plants.\n    \n    2. **Implications for Conservation**: Understanding the mycorrhizal network has significant implications for conservation efforts. It highlights the importance of preserving not just individual trees but entire ecosystems, including the fungal networks that sustain them.\n    \n    Practical Applications\n    \n    1. **Agriculture**: Farmers and horticulturists are exploring the use of mycorrhizal fungi to improve crop yields and soil health. By incorporating these fungi into agricultur", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "al Applications\n    \n    1. **Agriculture**: Farmers and horticulturists are exploring the use of mycorrhizal fungi to improve crop yields and soil health. By incorporating these fungi into agricultural practices, they can reduce the need for chemical fertilizers and enhance plant resilience.\n    \n    2. **Reforestation**: In reforestation projects, introducing mycorrhizal fungi can accelerate the recovery of degraded lands. The fungi help establish healthy plant communities, ensuring the success of newly planted trees.\n    \n    The \"Wood Wide Web\" exemplifies the intricate and often hidden connections that sustain life on Earth. It\u2019s a reminder of the profound interdependence within natural systems and the importance of preserving these delicate relationships.\n    \"\"\"\n    \n    def random_plot():\n        df = px.data.iris()\n        fig = px.scatter(\n            df,\n            x=\"sepal_width\",\n            y=\"sepal_length\",\n            color=\"species\",\n            size=\"petal_length\",\n            hover_data=[\"petal_width\"],\n        )\n        return fig\n    \n    color_map = {\n        \"harmful\": \"crimson\",\n        \"neutral\": \"gray\",\n        \"beneficial\": \"green\",\n    }\n    \n    def html_src(harm_level):\n        return f\"\"\"\n    \n    \n    \n      \n    \n    \n      {harm_level}\n      \n    \n    \n    \n    \n    \n    \"\"\"\n    \n    def print_like_dislike(x: gr.LikeData):\n        print(x.index, x.value, x.liked)\n    \n    def random_bokeh_plot():\n        from bokeh.models import ColumnDataSource, Whisker\n        from bokeh.plotting import figure\n        from bokeh.sampledata.autompg2 import autompg2 as df\n        from bokeh.transform import factor_cmap, jitter\n    \n        classes = sorted(df[\"class\"].unique())\n    \n        p = figure(\n            height=400,\n            x_range=classes,\n            background_fill_color=\"efefef\",\n            title=\"Car class vs HWY mpg with quintile ranges\",\n        )\n        p.xgrid.grid_line_color = None\n    \n        g = df.groupby(\"class\")\n   ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "s,\n            background_fill_color=\"efefef\",\n            title=\"Car class vs HWY mpg with quintile ranges\",\n        )\n        p.xgrid.grid_line_color = None\n    \n        g = df.groupby(\"class\")\n        upper = g.hwy.quantile(0.80)\n        lower = g.hwy.quantile(0.20)\n        source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n    \n        error = Whisker(\n            base=\"base\",\n            upper=\"upper\",\n            lower=\"lower\",\n            source=source,\n            level=\"annotation\",\n            line_width=2,\n        )\n        error.upper_head.size = 20\n        error.lower_head.size = 20\n        p.add_layout(error)\n    \n        p.circle(\n            jitter(\"class\", 0.3, range=p.x_range),\n            \"hwy\",\n            source=df,\n            alpha=0.5,\n            size=13,\n            line_color=\"white\",\n            color=factor_cmap(\"class\", \"Light6\", classes),\n        )\n        return p\n    \n    def random_matplotlib_plot():\n        import numpy as np\n        import pandas as pd\n        import matplotlib.pyplot as plt\n    \n        countries = [\"USA\", \"Canada\", \"Mexico\", \"UK\"]\n        months = [\"January\", \"February\", \"March\", \"April\", \"May\"]\n        m = months.index(\"January\")\n        r = 3.2\n        start_day = 30 * m\n        final_day = 30 * (m + 1)\n        x = np.arange(start_day, final_day + 1)\n        pop_count = {\"USA\": 350, \"Canada\": 40, \"Mexico\": 300, \"UK\": 120}\n        df = pd.DataFrame({\"day\": x})\n        for country in countries:\n            df[country] = x ** (r) * (pop_count[country] + 1)\n    \n        fig = plt.figure()\n        plt.plot(df[\"day\"], df[countries].to_numpy())\n        plt.title(\"Outbreak in \" + \"January\")\n        plt.ylabel(\"Cases\")\n        plt.xlabel(\"Days since Day 0\")\n        plt.legend(countries)\n        return fig\n    \n    def add_message(history, message):\n        for x in message[\"files\"]:\n            history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n        if message[\"text\"] is not None:\n  ", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "eturn fig\n    \n    def add_message(history, message):\n        for x in message[\"files\"]:\n            history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n        if message[\"text\"] is not None:\n            history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n        return history, gr.MultimodalTextbox(value=None, interactive=False)\n    \n    def bot(history, response_type):\n        msg = {\"role\": \"assistant\", \"content\": \"\"}\n        if response_type == \"plot\":\n            content = gr.Plot(random_plot())\n        elif response_type == \"bokeh_plot\":\n            content = gr.Plot(random_bokeh_plot())\n        elif response_type == \"matplotlib_plot\":\n            content =  gr.Plot(random_matplotlib_plot())\n        elif response_type == \"gallery\":\n            content = gr.Gallery(\n                [os.path.join(\"files\", \"avatar.png\"), os.path.join(\"files\", \"avatar.png\")]\n            )\n        elif response_type == \"dataframe\":\n            content = gr.Dataframe(\n                interactive=True,\n                headers=[\"One\", \"Two\", \"Three\"],\n                col_count=(3, \"fixed\"),\n                row_count=(3, \"fixed\"),\n                value=[[1, 2, 3], [4, 5, 6], [7, 8, 9]],\n                label=\"Dataframe\",\n            )\n        elif response_type == \"image\":\n           content = gr.Image(os.path.join(\"files\", \"avatar.png\"))\n        elif response_type == \"video\":\n           content = gr.Video(os.path.join(\"files\", \"world.mp4\"))\n        elif response_type == \"audio\":\n            content = gr.Audio(os.path.join(\"files\", \"audio.wav\"))\n        elif response_type == \"audio_file\":\n            content = {\"path\": os.path.join(\"files\", \"audio.wav\"), \"alt_text\": \"description\"}\n        elif response_type == \"image_file\":\n            content = {\"path\": os.path.join(\"files\", \"avatar.png\"), \"alt_text\": \"description\"}\n        elif response_type == \"video_file\":\n            content = {\"path\": os.path.join(\"files\", \"world.mp4\"), \"alt_text\": \"description\"}\n        elif respo", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": ", \"avatar.png\"), \"alt_text\": \"description\"}\n        elif response_type == \"video_file\":\n            content = {\"path\": os.path.join(\"files\", \"world.mp4\"), \"alt_text\": \"description\"}\n        elif response_type == \"txt_file\":\n            content = {\"path\": os.path.join(\"files\", \"sample.txt\"), \"alt_text\": \"description\"}\n        elif response_type == \"model3d_file\":\n            content = {\"path\": os.path.join(\"files\", \"Duck.glb\"), \"alt_text\": \"description\"}\n        elif response_type == \"html\":\n            content = gr.HTML(\n                html_src(random.choice([\"harmful\", \"neutral\", \"beneficial\"]))\n            )\n        elif response_type == \"model3d\":\n            content = gr.Model3D(os.path.join(\"files\", \"Duck.glb\"))\n        else:\n            content = txt\n        msg[\"content\"] = content \n        history.append(msg)\n        return history\n    \n    fig = random_plot()\n    \n    with gr.Blocks(fill_height=True) as demo:\n        chatbot = gr.Chatbot(\n            elem_id=\"chatbot\",\n            type=\"messages\",\n            bubble_full_width=False,\n            scale=1,\n            show_copy_button=True,\n            avatar_images=(\n                None,  os.path.join(\"files\", \"avatar.png\"),\n                os.path.join(\"files\", \"avatar.png\"),\n            ),\n        )\n        response_type = gr.Radio(\n            [\n                \"audio_file\",\n                \"image_file\",\n                \"video_file\",\n                \"txt_file\",\n                \"model3d_file\",\n                \"plot\",\n                \"matplotlib_plot\",\n                \"bokeh_plot\",\n                \"image\",\n                \"text\",\n                \"gallery\",\n                \"dataframe\",\n                \"video\",\n                \"audio\",\n                \"html\",\n                \"model3d\",\n            ],\n            value=\"text\",\n            label=\"Response Type\",\n        )\n    \n        chat_input = gr.MultimodalTextbox(\n            interactive=True,\n            placeholder=\"Enter message or upload file...\",\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "    value=\"text\",\n            label=\"Response Type\",\n        )\n    \n        chat_input = gr.MultimodalTextbox(\n            interactive=True,\n            placeholder=\"Enter message or upload file...\",\n            show_label=False,\n        )\n    \n        chat_msg = chat_input.submit(\n            add_message, [chatbot, chat_input], [chatbot, chat_input]\n        )\n        bot_msg = chat_msg.then(\n            bot, [chatbot, response_type], chatbot, api_name=\"bot_response\"\n        )\n        bot_msg.then(lambda: gr.MultimodalTextbox(interactive=True), None, [chat_input])\n    \n        chatbot.like(print_like_dislike, None, None)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Chatbot component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Chatbot.change(fn, \u00b7\u00b7\u00b7)` | Triggered when the value of the Chatbot changes either because of user input (e.g. a user types in a textbox) OR because of a function update (e.g. an image receives a value from the output of an event trigger). See `.input()` for a listener that is only triggered by user input.  \n`Chatbot.select(fn, \u00b7\u00b7\u00b7)` | Event listener for when the user selects or deselects the Chatbot. Uses event data gradio.SelectData to carry `value` referring to the label of the Chatbot, and `selected` to refer to state of the Chatbot. See EventData documentation on how to use this event data  \n`Chatbot.like(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user likes/dislikes from within the Chatbot. This event has EventData of type gradio.LikeData that carries information, accessible through LikeData.index and LikeData.value. See EventData documentation on how to use this event data.  \n`Chatbot.retry(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clicks the retry button in the chatbot message.  \n`Chatbot.undo(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clicks the undo button in the chatbot message.  \n`Chatbot.example_select(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clicks on an example from within the Chatbot. This event has SelectData of type gradio.SelectData that carries information, accessible through SelectData.index and SelectData.value. See SelectData documentation on how to use this event data.  \n`Chatbot.option_select(fn, \u00b7\u00b7\u00b7)` | This listener is trigge", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": " carries information, accessible through SelectData.index and SelectData.value. See SelectData documentation on how to use this event data.  \n`Chatbot.option_select(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clicks on an option from within the Chatbot. This event has SelectData of type gradio.SelectData that carries information, accessible through SelectData.index and SelectData.value. See SelectData documentation on how to use this event data.  \n`Chatbot.clear(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user clears the Chatbot using the clear button for the component.  \n`Chatbot.copy(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user copies content from the Chatbot. Uses event data gradio.CopyData to carry information about the copied content. See EventData documentation on how to use this event data  \n`Chatbot.edit(fn, \u00b7\u00b7\u00b7)` | This listener is triggered when the user edits the Chatbot (e.g. image) using the built-in editor.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    fn: Callable | None | Literal['decorator']\n\ndefault `= \"decorator\"`\n\nthe function to call when this event is triggered. Often a machine learning\nmodel's prediction function. Each parameter of the function corresponds to one\ninput component, and the function should return a single value or a tuple of\nvalues, with each element in the tuple corresponding to one output component.\n\n\n    \n    \n    inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as inputs. If the function takes no inputs,\nthis should be an empty list.\n\n\n    \n    \n    outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None\n\ndefault `= None`\n\nList of gradio.components to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in ", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "nents to use as outputs. If the function returns no\noutputs, this should be an empty list.\n\n\n    \n    \n    api_name: str | None | Literal[False]\n\ndefault `= None`\n\ndefines how the endpoint appears in the API docs. Can be a string, None, or\nFalse. If set to a string, the endpoint will be exposed in the API docs with\nthe given name. If None (default), the name of the function will be used as\nthe API endpoint. If False, the endpoint will not be exposed in the API docs\nand downstream apps (including those that `gr.load` this app) will not be able\nto use this event.\n\n\n    \n    \n    api_description: str | None | Literal[False]\n\ndefault `= None`\n\nDescription of the API endpoint. Can be a string, None, or False. If set to a\nstring, the endpoint will be exposed in the API docs with the given\ndescription. If None, the function's docstring will be used as the API\nendpoint description. If False, then no description will be displayed in the\nAPI docs.\n\n\n    \n    \n    scroll_to_output: bool\n\ndefault `= False`\n\nIf True, will scroll to output component on completion\n\n\n    \n    \n    show_progress: Literal['full', 'minimal', 'hidden']\n\ndefault `= \"full\"`\n\nhow to show the progress animation while event is running: \"full\" shows a\nspinner which covers the output component area as well as a runtime display in\nthe upper right corner, \"minimal\" only shows the runtime display, \"hidden\"\nshows no progress animation at all\n\n\n    \n    \n    show_progress_on: Component | list[Component] | None\n\ndefault `= None`\n\nComponent or list of components to show the progress animation on. If None,\nwill show the progress animation on all of the output components.\n\n\n    \n    \n    queue: bool\n\ndefault `= True`\n\nIf True, will place the request on the queue, if the queue has been enabled.\nIf False, will not put this event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "event on the queue, even if the queue has been\nenabled. If None, will use the queue setting of the gradio app.\n\n\n    \n    \n    batch: bool\n\ndefault `= False`\n\nIf True, then the function should process a batch of inputs, meaning that it\nshould accept a list of input values for each parameter. The lists should be\nof equal length (and be up to length `max_batch_size`). The function is then\n*required* to return a tuple of lists (even if there is only 1 output\ncomponent), with each list in the tuple corresponding to one output component.\n\n\n    \n    \n    max_batch_size: int\n\ndefault `= 4`\n\nMaximum number of inputs to batch together if this is called from the queue\n(only relevant if batch=True)\n\n\n    \n    \n    preprocess: bool\n\ndefault `= True`\n\nIf False, will not run preprocessing of component data before running 'fn'\n(e.g. leaving it as a base64 string if this method is called with the `Image`\ncomponent).\n\n\n    \n    \n    postprocess: bool\n\ndefault `= True`\n\nIf False, will not run postprocessing of component data before returning 'fn'\noutput to the browser.\n\n\n    \n    \n    cancels: dict[str, Any] | list[dict[str, Any]] | None\n\ndefault `= None`\n\nA list of other events to cancel when this listener is triggered. For example,\nsetting cancels=[click_event] will cancel the click_event, where click_event\nis the return value of another components .click method. Functions that have\nnot yet run (or generators that are iterating) will be cancelled, but\nfunctions that are currently running will be allowed to finish.\n\n\n    \n    \n    trigger_mode: Literal['once', 'multiple', 'always_last'] | None\n\ndefault `= None`\n\nIf \"once\" (default for all events except `.change()`) would not allow any\nsubmissions while an event is pending. If set to \"multiple\", unlimited\nsubmissions are allowed while pending, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "nding, and \"always_last\" (default for\n`.change()` and `.key_up()` events) would allow a second submission after the\npending event is complete.\n\n\n    \n    \n    js: str | Literal[True] | None\n\ndefault `= None`\n\nOptional frontend js method to run before running 'fn'. Input arguments for js\nmethod are values of 'inputs' and 'outputs', return should be a list of values\nfor output components.\n\n\n    \n    \n    concurrency_limit: int | None | Literal['default']\n\ndefault `= \"default\"`\n\nIf set, this is the maximum number of this event that can be running\nsimultaneously. Can be set to None to mean no concurrency_limit (any number of\nthis event can be running simultaneously). Set to \"default\" to use the default\nconcurrency limit (defined by the `default_concurrency_limit` parameter in\n`Blocks.queue()`, which itself is 1 by default).\n\n\n    \n    \n    concurrency_id: str | None\n\ndefault `= None`\n\nIf set, this is the id of the concurrency group. Events with the same\nconcurrency_id will be limited by the lowest set concurrency_limit.\n\n\n    \n    \n    show_api: bool\n\ndefault `= True`\n\nwhether to show this event in the \"view API\" page of the Gradio app, or in the\n\".view_api()\" method of the Gradio clients. Unlike setting api_name to False,\nsetting show_api to False will still allow downstream apps as well as the\nClients to use this event. If fn is None, show_api will automatically be set\nto False.\n\n\n    \n    \n    time_limit: int | None\n\ndefault `= None`\n\n\n    \n    \n    stream_every: float\n\ndefault `= 0.5`\n\n\n    \n    \n    like_user_message: bool\n\ndefault `= False`\n\n\n    \n    \n    key: int | str | tuple[int | str, ...] | None\n\ndefault `= None`\n\nA unique key for this event listener to be used in @gr.render(). If set, this\nvalue identifies an event as identical across re-renders when the key is\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "\nidentical.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "", "heading1": "Helper Classes", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "gradio.ChatMessage(\u00b7\u00b7\u00b7)\n\nDescription\n\nA dataclass that represents a message in the Chatbot component (with\ntype=\"messages\"). The only required field is `content`. The value of\n`gr.Chatbot` is a list of these dataclasses.\n\nParameters \u25bc\n\n\n    \n    \n    content: str | FileData | Component | FileDataDict | tuple | list\n\nThe content of the message. Can be a string or a Gradio component.\n\n\n    \n    \n    role: Literal['user', 'assistant', 'system']\n\ndefault `= \"assistant\"`\n\nThe role of the message, which determines the alignment of the message in the\nchatbot. Can be \"user\", \"assistant\", or \"system\". Defaults to \"assistant\".\n\n\n    \n    \n    metadata: MetadataDict\n\ndefault `= _HAS_DEFAULT_FACTORY_CLASS()`\n\nThe metadata of the message, which is used to display intermediate thoughts /\ntool usage. Should be a dictionary with the following keys: \"title\" (required\nto display the thought), and optionally: \"id\" and \"parent_id\" (to nest\nthoughts), \"duration\" (to display the duration of the thought), \"status\" (to\ndisplay the status of the thought).\n\n\n    \n    \n    options: list[OptionDict]\n\ndefault `= _HAS_DEFAULT_FACTORY_CLASS()`\n\nThe options of the message. A list of Option objects, which are dictionaries\nwith the following keys: \"label\" (the text to display in the option), and\noptionally \"value\" (the value to return when the option is selected if\ndifferent from the label).\n\n", "heading1": "ChatMessage", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "A typed dictionary to represent metadata for a message in the Chatbot\ncomponent. An instance of this dictionary is used for the `metadata` field in\na ChatMessage when the chat message should be displayed as a thought.\n\nKeys \u25bc\n\n\n    \n    \n    title: str\n\nThe title of the 'thought' message. Only required field.\n\n\n    \n    \n    id: int | str\n\nThe ID of the message. Only used for nested thoughts. Nested thoughts can be\nnested by setting the parent_id to the id of the parent thought.\n\n\n    \n    \n    parent_id: int | str\n\nThe ID of the parent message. Only used for nested thoughts.\n\n\n    \n    \n    log: str\n\nA string message to display next to the thought title in a subdued font.\n\n\n    \n    \n    duration: float\n\nThe duration of the message in seconds. Appears next to the thought title in a\nsubdued font inside a parentheses.\n\n\n    \n    \n    status: Literal['pending', 'done']\n\nif set to `'pending'`, a spinner appears next to the thought title and the\naccordion is initialized open. If `status` is `'done'`, the thought accordion\nis initialized closed. If `status` is not provided, the thought accordion is\ninitialized open and no spinner is displayed.\n\n", "heading1": "MetadataDict", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "A typed dictionary to represent an option in a ChatMessage. A list of these\ndictionaries is used for the `options` field in a ChatMessage.\n\nKeys \u25bc\n\n\n    \n    \n    value: str\n\nThe value to return when the option is selected.\n\n\n    \n    \n    label: str\n\nThe text to display in the option, if different from the value.\n\n", "heading1": "OptionDict", "source_page_url": "https://gradio.app/docs/gradio/chatbot", "source_page_title": "Gradio - Chatbot Docs"}, {"text": "Gradio includes pre-built components that can be used as inputs or outputs\nin your Interface or Blocks with a single line of code. Components include\npreprocessing steps that convert user data submitted through browser to\nsomething that be can used by a Python function, and postprocessing steps to\nconvert values returned by a Python function into something that can be\ndisplayed in a browser.\n\nConsider an example with three inputs (Textbox, Number, and Image) and two\noutputs (Number and Gallery), below is a diagram of what our preprocessing\nwill send to the function and what our postprocessing will require from it.\n\n![](/_app/immutable/assets/dataflow.FNuRzshD.svg)\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": "Components also come with certain events that they support. These are\nmethods that are triggered with user actions. Below is a table showing which\nevents are supported for each component. All events are also listed (with\nparameters) in the component\u2019s docs.\n\n| retry| stop| undo| preview_open| change| submit| release| double_click| end|\nedit| example_select| stream| input| pause_recording| like| select|\noption_select| delete| download| preview_close| blur| click| upload|\nstop_recording| copy| pause| clear| start_recording| key_up| focus| play|\ntick| apply  \n---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---  \n[AnnotatedImage](annotatedimage) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Audio](audio) | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2713 | \u2713 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715  \n[BrowserState](browserstate) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Button](button) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Chatbot](chatbot) | \u2713 | \u2715 | \u2713 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Checkbox](checkbox) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[CheckboxGroup](checkboxgroup) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[ClearButton](clearbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Code](code) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": " | \u2715  \n[ClearButton](clearbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Code](code) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[ColorPicker](colorpicker) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[Dataframe](dataframe) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Dataset](dataset) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[DateTime](datetime) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[DeepLinkButton](deeplinkbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Dialogue](dialogue) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[DownloadButton](downloadbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Dropdown](dropdown) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715  \n[DuplicateButton](duplicatebutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[File](file) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[FileExplorer](fileexplorer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | ", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": " \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[FileExplorer](fileexplorer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Gallery](gallery) | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[HighlightedText](highlightedtext) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[HTML](html) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[ImageEditor](imageeditor) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713  \n[Image](image) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[ImageSlider](imageslider) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[JSON](json) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Label](label) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[LoginButton](loginbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Markdown](markdown) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Model3D](model3d) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Textbox](textbox) | \u2715 | \u2713 | \u2715 ", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": "\u2715 | \u2715 | \u2715 | \u2715  \n[Model3D](model3d) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Textbox](textbox) | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[MultimodalTextbox](multimodaltextbox) | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[BarPlot](barplot) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[LinePlot](lineplot) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[ScatterPlot](scatterplot) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Number](number) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715  \n[ParamViewer](paramviewer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Plot](plot) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Radio](radio) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Slider](slider) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[State](state) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Timer](timer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 |", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": "\u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Timer](timer) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715  \n[UploadButton](uploadbutton) | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715  \n[Video](video) | \u2715 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2713 | \u2715 | \u2713 | \u2713 | \u2713 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715  \n[SimpleImage](simpleimage) | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2713 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715 | \u2715\n\n", "heading1": "Events", "source_page_url": "https://gradio.app/docs/gradio/introduction", "source_page_title": "Gradio - Introduction Docs"}, {"text": "The gr.KeyUpData class is a subclass of gr.EventData that specifically\ncarries information about the `.key_up()` event. When gr.KeyUpData is added as\na type hint to an argument of an event listener method, a gr.KeyUpData object\nwill automatically be passed as the value of that argument. The attributes of\nthis object contains information about the event that triggered the listener.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/gradio/keyupdata", "source_page_title": "Gradio - Keyupdata Docs"}, {"text": "import gradio as gr\n    \n    def test(value, key_up_data: gr.KeyUpData):\n        return {\n            \"component value\": value,\n            \"input value\": key_up_data.input_value,\n            \"key\": key_up_data.key\n        }\n    \n    with gr.Blocks() as demo:\n        d = gr.Dropdown([\"abc\", \"def\"], allow_custom_value=True)\n        t = gr.JSON()\n        d.key_up(test, d, t)\n    demo.launch()\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/docs/gradio/keyupdata", "source_page_title": "Gradio - Keyupdata Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    key: str\n\nThe key that was pressed.\n\n\n    \n    \n    input_value: str\n\nThe displayed value in the input textbox after the key was pressed. This may\nbe different than the `value` attribute of the component itself, as the\n`value` attribute of some components (e.g. Dropdown) are not updated until the\nuser presses Enter.\n\n", "heading1": "Attributes", "source_page_url": "https://gradio.app/docs/gradio/keyupdata", "source_page_title": "Gradio - Keyupdata Docs"}, {"text": "dropdown_key_up\n\nOpen in \ud83c\udfa2 \u2197 import gradio as gr def test(value, key_up_data: gr.KeyUpData):\nreturn { \"component value\": value, \"input value\": key_up_data.input_value,\n\"key\": key_up_data.key } with gr.Blocks() as demo: d = gr.Dropdown([\"abc\",\n\"def\"], allow_custom_value=True) t = gr.JSON() d.key_up(test, d, t) if\n__name__ == \"__main__\": demo.launch()\n\n    \n    \n    import gradio as gr\n    \n    def test(value, key_up_data: gr.KeyUpData):\n        return {\n            \"component value\": value,\n            \"input value\": key_up_data.input_value,\n            \"key\": key_up_data.key\n        }\n    \n    with gr.Blocks() as demo:\n        d = gr.Dropdown([\"abc\", \"def\"], allow_custom_value=True)\n        t = gr.JSON()\n        d.key_up(test, d, t)\n    \n    if __name__ == \"__main__\":\n        demo.launch()\n    \n    \t\t\n\n", "heading1": "Demos", "source_page_url": "https://gradio.app/docs/gradio/keyupdata", "source_page_title": "Gradio - Keyupdata Docs"}, {"text": "**Stream From a Gradio app in 5 lines**\n\n  \n\nUse the `submit` method to get a job you can iterate over.\n\n  \n\nIn python:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"gradio/llm_stream\")\n    \n    for result in client.submit(\"What's the best UI framework in Python?\"):\n        print(result)\n\n  \n\nIn typescript:\n\n    \n    \n    import { Client } from \"@gradio/client\";\n    \n    const client = await Client.connect(\"gradio/llm_stream\")\n    const job = client.submit(\"/predict\", {\"text\": \"What's the best UI framework in Python?\"})\n    \n    for await (const msg of job) console.log(msg.data)\n\n  \n\n**Use the same keyword arguments as the app**\n\n  \nIn the examples below, the upstream app has a function with parameters called\n`message`, `system_prompt`, and `tokens`. We can see that the client `predict`\ncall uses the same arguments.\n\nIn python:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"http://127.0.0.1:7860/\")\n    result = client.predict(\n    \t\tmessage=\"Hello!!\",\n    \t\tsystem_prompt=\"You are helpful AI.\",\n    \t\ttokens=10,\n    \t\tapi_name=\"/chat\"\n    )\n    print(result)\n\nIn typescript:\n\n    \n    \n    import { Client } from \"@gradio/client\";\n    \n    const client = await Client.connect(\"http://127.0.0.1:7860/\");\n    const result = await client.predict(\"/chat\", { \t\t\n    \t\tmessage: \"Hello!!\", \t\t\n    \t\tsystem_prompt: \"Hello!!\", \t\t\n    \t\ttokens: 10, \n    });\n    \n    console.log(result.data);\n\n  \n\n**Better Error Messages**\n\n  \nIf something goes wrong in the upstream app, the client will raise the same\nexception as the app provided that `show_error=True` in the original app's\n`launch()` function, or it's a `gr.Error` exception.\n\n", "heading1": "Ergonomic API \ud83d\udc86", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "Anything you can do in the UI, you can do with the client:\n\n  * \ud83d\udd10Authentication\n  * \ud83d\uded1 Job Cancelling\n  * \u2139\ufe0f Access Queue Position and API\n  * \ud83d\udcd5 View the API information\n\n  \nHere's an example showing how to display the queue position of a pending job:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"gradio/diffusion_model\")\n    \n    job = client.submit(\"A cute cat\")\n    while not job.done():\n        status = job.status()\n        print(f\"Current in position {status.rank} out of {status.queue_size}\")\n\n", "heading1": "Transparent Design \ud83e\ude9f", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "The client can run from pretty much any python and javascript environment\n(node, deno, the browser, Service Workers).  \nHere's an example using the client from a Flask server using gevent:\n\n    \n    \n    from gevent import monkey\n    monkey.patch_all()\n    \n    from gradio_client import Client\n    from flask import Flask, send_file\n    import time\n    \n    app = Flask(__name__)\n    \n    imageclient = Client(\"gradio/diffusion_model\")\n    \n    @app.route(\"/gen\")\n    def gen():\n          result = imageclient.predict(\n                    \"A cute cat\",\n                    api_name=\"/predict\"\n                  )\n          return send_file(result)\n    \n    if __name__ == \"__main__\":\n          app.run(host=\"0.0.0.0\", port=5000)\n\n", "heading1": "Portable Design \u26fa\ufe0f", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "Changes\n\n  \n\n**Python**\n\n  * The `serialize` argument of the `Client` class was removed and has no effect.\n  * The `upload_files` argument of the `Client` was removed.\n  * All filepaths must be wrapped in the `handle_file` method. For example, `caption = client.predict(handle_file('./dog.jpg'))`.\n  * The `output_dir` argument was removed. It is not specified in the `download_files` argument.\n\n  \n\n**Javascript**\n\n  \nThe client has been redesigned entirely. It was refactored from a function\ninto a class. An instance can now be constructed by awaiting the `connect`\nmethod.\n\n    \n    \n    const app = await Client.connect(\"gradio/whisper\")\n\nThe app variable has the same methods as the python class (`submit`,\n`predict`, `view_api`, `duplicate`).\n\n", "heading1": "v1.0 Migration Guide and Breaking", "source_page_url": "https://gradio.app/docs/python-client/version-1-release", "source_page_title": "Python Client - Version 1 Release Docs"}, {"text": "ZeroGPU\n\nZeroGPU spaces are rate-limited to ensure that a single user does not hog all\nof the available GPUs. The limit is controlled by a special token that the\nHugging Face Hub infrastructure adds to all incoming requests to Spaces. This\ntoken is a request header called `X-IP-Token` and its value changes depending\non the user who makes a request to the ZeroGPU space.\n\n  \n\nLet\u2019s say you want to create a space (Space A) that uses a ZeroGPU space\n(Space B) programmatically. Simply calling Space B from Space A with the\npython client will quickly exhaust your rate limit, as all the requests to the\nZeroGPU space will have the same token. So in order to avoid this, we need to\nextract the token of the user using Space A before we call Space B\nprogrammatically.\n\n  \n\nHow to do this will be explained in the following section.\n\n", "heading1": "Explaining Rate Limits for", "source_page_url": "https://gradio.app/docs/python-client/using-zero-gpu-spaces", "source_page_title": "Python Client - Using Zero Gpu Spaces Docs"}, {"text": "When a user presses enter in the textbox, we will extract their token from the\n`X-IP-Token` header of the incoming request. We will use this header when\nconstructing the gradio client. The following hypothetical text-to-image\napplication shows how this is done.\n\n  \n\n    \n    \n    import gradio as gr\n    from gradio_client import Client\n    \n    def text_to_image(prompt, request: gr.Request):\n        x_ip_token = request.headers['x-ip-token']\n        client = Client(\"hysts/SDXL\", headers={\"x-ip-token\": x_ip_token})\n        img = client.predict(prompt, api_name=\"/predict\")\n        return img\n    \n    \n    with gr.Blocks() as demo:\n        image = gr.Image()\n        prompt = gr.Textbox(max_lines=1)\n        prompt.submit(text_to_image, [prompt], [image])\n    \n    demo.launch()\n\n", "heading1": "Avoiding Rate Limits", "source_page_url": "https://gradio.app/docs/python-client/using-zero-gpu-spaces", "source_page_title": "Python Client - Using Zero Gpu Spaces Docs"}, {"text": "A Job is a wrapper over the Future class that represents a prediction call\nthat has been submitted by the Gradio client. This class is not meant to be\ninstantiated directly, but rather is created by the Client.submit() method.  \nA Job object includes methods to get the status of the prediction call, as\nwell to get the outputs of the prediction call. Job objects are also iterable,\nand can be used in a loop to get the outputs of prediction calls as they\nbecome available for generator endpoints.\n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    future: Future\n\nThe future object that represents the prediction call, created by the\nClient.submit() method\n\n\n    \n    \n    communicator: Communicator | None\n\ndefault `= None`\n\nThe communicator object that is used to communicate between the client and the\nbackground thread running the job\n\n\n    \n    \n    verbose: bool\n\ndefault `= True`\n\nWhether to print any status-related messages to the console\n\n\n    \n    \n    space_id: str | None\n\ndefault `= None`\n\nThe space ID corresponding to the Client object that created this Job object\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Job component supports the following event listeners. Each event listener\ntakes the same parameters, which are listed in the Event Parameters table\nbelow.\n\nListener | Description  \n---|---  \n`Job.result(fn, \u00b7\u00b7\u00b7)` | Return the result of the call that the future represents. Raises CancelledError: If the future was cancelled, TimeoutError: If the future didn't finish executing before the given timeout, and Exception: If the call raised then that exception will be raised. <br>  \n`Job.outputs(fn, \u00b7\u00b7\u00b7)` | Returns a list containing the latest outputs from the Job. <br> If the endpoint has multiple output components, the list will contain a tuple of results. Otherwise, it will contain the results without storing them in tuples. <br> For endpoints that are queued, this list will contain the final job output even if that endpoint does not use a generator function. <br>  \n`Job.status(fn, \u00b7\u00b7\u00b7)` | Returns the latest status update from the Job in the form of a StatusUpdate object, which contains the following fields: code, rank, queue_size, success, time, eta, and progress_data. <br> progress_data is a list of updates emitted by the gr.Progress() tracker of the event handler. Each element of the list has the following fields: index, length, unit, progress, desc. If the event handler does not have a gr.Progress() tracker, the progress_data field will be None. <br>  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    timeout: float | None\n\ndefault `= None`\n\nThe number of seconds to wait for the result if the future isn't done. If\nNone, then there is no limit on the wait time.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/job", "source_page_title": "Python Client - Job Docs"}, {"text": "If you already have a recent version of `gradio`, then the `gradio_client` is\nincluded as a dependency. But note that this documentation reflects the latest\nversion of the `gradio_client`, so upgrade if you\u2019re not sure!\n\nThe lightweight `gradio_client` package can be installed from pip (or pip3)\nand is tested to work with **Python versions 3.9 or higher** :\n\n    \n    \n    $ pip install --upgrade gradio_client\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Spaces\n\nStart by connecting instantiating a `Client` object and connecting it to a\nGradio app that is running on Hugging Face Spaces.\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/en2fr\")  a Space that translates from English to French\n\nYou can also connect to private Spaces by passing in your HF token with the\n`hf_token` parameter. You can get your HF token here:\n<https://huggingface.co/settings/tokens>\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/my-private-space\", hf_token=\"...\")\n\n", "heading1": "Connecting to a Gradio App on Hugging Face", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "use\n\nWhile you can use any public Space as an API, you may get rate limited by\nHugging Face if you make too many requests. For unlimited usage of a Space,\nsimply duplicate the Space to create a private Space, and then use it to make\nas many requests as you\u2019d like!\n\nThe `gradio_client` includes a class method: `Client.duplicate()` to make this\nprocess simple (you\u2019ll need to pass in your [Hugging Face\ntoken](https://huggingface.co/settings/tokens) or be logged in using the\nHugging Face CLI):\n\n    \n    \n    import os\n    from gradio_client import Client, file\n    \n    HF_TOKEN = os.environ.get(\"HF_TOKEN\")\n    \n    client = Client.duplicate(\"abidlabs/whisper\", hf_token=HF_TOKEN)\n    client.predict(file(\"audio_sample.wav\"))\n    \n    >> \"This is a test of the whisper speech recognition model.\"\n\nIf you have previously duplicated a Space, re-running `duplicate()` will _not_\ncreate a new Space. Instead, the Client will attach to the previously-created\nSpace. So it is safe to re-run the `Client.duplicate()` method multiple times.\n\n**Note:** if the original Space uses GPUs, your private Space will as well,\nand your Hugging Face account will get billed based on the price of the GPU.\nTo minimize charges, your Space will automatically go to sleep after 1 hour of\ninactivity. You can also set the hardware using the `hardware` parameter of\n`duplicate()`.\n\n", "heading1": "Duplicating a Space for private", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "app\n\nIf your app is running somewhere else, just provide the full URL instead,\nincluding the \u201chttp://\u201d or \u201chttps://\u201c. Here\u2019s an example of making predictions\nto a Gradio app that is running on a share URL:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"https://bec81a83-5b5c-471e.gradio.live\")\n\n", "heading1": "Connecting a general Gradio", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are\navailable to you by calling the `Client.view_api()` method. For the Whisper\nSpace, we see the following:\n\n    \n    \n    Client.predict() Usage Info\n    ---------------------------\n    Named API endpoints: 1\n    \n     - predict(audio, api_name=\"/predict\") -> output\n        Parameters:\n         - [Audio] audio: filepath (required)  \n        Returns:\n         - [Textbox] output: str \n\nWe see that we have 1 API endpoint in this space, and shows us how to use the\nAPI endpoint to make a prediction: we should call the `.predict()` method\n(which we will explore below), providing a parameter `input_audio` of type\n`str`, which is a `filepath or URL`.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()`\nmethod. Although this isn\u2019t necessary if a Gradio app has only 1 named\nendpoint, it does allow us to call different endpoints in a single app if they\nare available.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the\n\u201cUse via API\u201d link in the footer of the Gradio app, which shows us the same\ninformation, along with example usage.\n\n![](https://huggingface.co/datasets/huggingface/documentation-\nimages/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \u201cAPI Recorder\u201d that lets you interact with\nthe Gradio UI normally and converts your interactions into the corresponding\ncode to run with the Python Client.\n\n", "heading1": "The \u201cView API\u201d Page", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()`\nfunction with the appropriate arguments:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/en2fr\", api_name='/predict')\n    client.predict(\"Hello\")\n    \n    >> Bonjour\n\nIf there are multiple parameters, then you should pass them as separate\narguments to `.predict()`, like this:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"gradio/calculator\")\n    client.predict(4, \"add\", 5)\n    \n    >> 9.0\n\nIt is recommended to provide key-word arguments instead of positional\narguments:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"gradio/calculator\")\n    client.predict(num1=4, operation=\"add\", num2=5)\n    \n    >> 9.0\n\nThis allows you to take advantage of default arguments. For example, this\nSpace includes the default value for the Slider component so you do not need\nto provide it when accessing it with the client.\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/image_generator\")\n    client.predict(text=\"an astronaut riding a camel\")\n\nThe default value is the initial value of the corresponding Gradio component.\nIf the component does not have an initial value, but if the corresponding\nargument in the predict function has a default value of `None`, then that\nparameter is also optional in the client. Of course, if you\u2019d like to override\nit, you can include it as well:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(\"abidlabs/image_generator\")\n    client.predict(text=\"an astronaut riding a camel\", steps=25)\n\nFor providing files or URLs as inputs, you should pass in the filepath or URL\nto the file enclosed within `gradio_client.file()`. This takes care of\nuploading the file to the Gradio server and ensures that the file is\npreprocessed correctly:\n\n    \n    \n    from gradio_client import Client, file\n    \n    client = Client(\"abidlabs/whisper\")\n    client.predict(\n      ", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": " to the Gradio server and ensures that the file is\npreprocessed correctly:\n\n    \n    \n    from gradio_client import Client, file\n    \n    client = Client(\"abidlabs/whisper\")\n    client.predict(\n        audio=file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\")\n    )\n    \n    >> \"My thought I have nobody by a beauty and will as you poured. Mr. Rochester is serve in that so don't find simpus, and devoted abode, to at might in a r\u2014\"\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Oe should note that `.predict()` is a _blocking_ operation as it waits for the\noperation to complete before returning the prediction.\n\nIn many cases, you may be better off letting the job run in the background\nuntil you need the results of the prediction. You can do this by creating a\n`Job` instance using the `.submit()` method, and then later calling\n`.result()` on the job to get the result. For example:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(space=\"abidlabs/en2fr\")\n    job = client.submit(\"Hello\", api_name=\"/predict\")  This is not blocking\n    \n    Do something else\n    \n    job.result()  This is blocking\n    \n    >> Bonjour\n\n", "heading1": "Running jobs asynchronously", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Alternatively, one can add one or more callbacks to perform actions after the\njob has completed running, like this:\n\n    \n    \n    from gradio_client import Client\n    \n    def print_result(x):\n        print(\"The translated result is: {x}\")\n    \n    client = Client(space=\"abidlabs/en2fr\")\n    \n    job = client.submit(\"Hello\", api_name=\"/predict\", result_callbacks=[print_result])\n    \n    Do something else\n    \n    >> The translated result is: Bonjour\n    \n\n", "heading1": "Adding callbacks", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The `Job` object also allows you to get the status of the running job by\ncalling the `.status()` method. This returns a `StatusUpdate` object with the\nfollowing attributes: `code` (the status code, one of a set of defined strings\nrepresenting the status. See the `utils.Status` class), `rank` (the current\nposition of this job in the queue), `queue_size` (the total queue size), `eta`\n(estimated time this job will complete), `success` (a boolean representing\nwhether the job completed successfully), and `time` (the time that the status\nwas generated).\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(src=\"gradio/calculator\")\n    job = client.submit(5, \"add\", 4, api_name=\"/predict\")\n    job.status()\n    \n    >> <Status.STARTING: 'STARTING'>\n\n_Note_ : The `Job` class also has a `.done()` instance method which returns a\nboolean indicating whether the job has completed.\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The `Job` class also has a `.cancel()` instance method that cancels jobs that\nhave been queued but not started. For example, if you run:\n\n    \n    \n    client = Client(\"abidlabs/whisper\")\n    job1 = client.submit(file(\"audio_sample1.wav\"))\n    job2 = client.submit(file(\"audio_sample2.wav\"))\n    job1.cancel()  will return False, assuming the job has started\n    job2.cancel()  will return True, indicating that the job has been canceled\n\nIf the first job has started processing, then it will not be canceled. If the\nsecond job has not yet started, it will be successfully canceled and removed\nfrom the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a\nseries of values. You can get the series of values that have been returned at\nany time from such a generator endpoint by running `job.outputs()`:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(src=\"gradio/count_generator\")\n    job = client.submit(3, api_name=\"/count\")\n    while not job.done():\n        time.sleep(0.1)\n    job.outputs()\n    \n    >> ['0', '1', '2']\n\nNote that running `job.result()` on a generator endpoint only gives you the\n_first_ value returned by the endpoint.\n\nThe `Job` object is also iterable, which means you can use it to display the\nresults of a generator function as they are returned from the endpoint. Here\u2019s\nthe equivalent example using the `Job` as a generator:\n\n    \n    \n    from gradio_client import Client\n    \n    client = Client(src=\"gradio/count_generator\")\n    job = client.submit(3, api_name=\"/count\")\n    \n    for o in job:\n        print(o)\n    \n    >> 0\n    >> 1\n    >> 2\n\nYou can also cancel jobs that that have iterative outputs, in which case the\njob will finish as soon as the current iteration finishes running.\n\n    \n    \n    from gradio_client import Client\n    import time\n    \n    client = Client(\"abidlabs/test-yield\")\n    job = client.submit(\"abcdef\")\n    time.sleep(3)\n    job.cancel()  job cancels after 2 iterations\n\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "Gradio demos can include [session state](https://www.gradio.app/guides/state-\nin-blocks), which provides a way for demos to persist information from user\ninteractions within a page session.\n\nFor example, consider the following demo, which maintains a list of words that\na user has submitted in a `gr.State` component. When a user submits a new\nword, it is added to the state, and the number of previous occurrences of that\nword is displayed:\n\n    \n    \n    import gradio as gr\n    \n    def count(word, list_of_words):\n        return list_of_words.count(word), list_of_words + [word]\n    \n    with gr.Blocks() as demo:\n        words = gr.State([])\n        textbox = gr.Textbox()\n        number = gr.Number()\n        textbox.submit(count, inputs=[textbox, words], outputs=[number, words])\n        \n    demo.launch()\n\nIf you were to connect this this Gradio app using the Python Client, you would\nnotice that the API information only shows a single input and output:\n\n    \n    \n    Client.predict() Usage Info\n    ---------------------------\n    Named API endpoints: 1\n    \n     - predict(word, api_name=\"/count\") -> value_31\n        Parameters:\n         - [Textbox] word: str (required)  \n        Returns:\n         - [Number] value_31: float \n\nThat is because the Python client handles state automatically for you \u2014 as you\nmake a series of requests, the returned state from one request is stored\ninternally and automatically supplied for the subsequent request. If you\u2019d\nlike to reset the state, you can do that by calling `Client.reset_session()`.\n\n", "heading1": "Demos with Session State", "source_page_url": "https://gradio.app/docs/python-client/introduction", "source_page_title": "Python Client - Introduction Docs"}, {"text": "The main Client class for the Python client. This class is used to connect\nto a remote Gradio app and call its API endpoints.  \n\n", "heading1": "Description", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "from gradio_client import Client\n    \n    client = Client(\"abidlabs/whisper-large-v2\")  connecting to a Hugging Face Space\n    client.predict(\"test.mp4\", api_name=\"/predict\")\n    >> What a nice recording! returns the result of the remote API call\n    \n    client = Client(\"https://bec81a83-5b5c-471e.gradio.live\")  connecting to a temporary Gradio share URL\n    job = client.submit(\"hello\", api_name=\"/predict\")  runs the prediction in a background thread\n    job.result()\n    >> 49 returns the result of the remote API call (blocking call)\n\n", "heading1": "Example usage", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "Parameters \u25bc\n\n\n    \n    \n    src: str\n\neither the name of the Hugging Face Space to load, (e.g. \"abidlabs/whisper-\nlarge-v2\") or the full URL (including \"http\" or \"https\") of the hosted Gradio\napp to load (e.g. \"http://mydomain.com/app\" or\n\"https://bec81a83-5b5c-471e.gradio.live/\").\n\n\n    \n    \n    hf_token: str | Literal[False] | None\n\ndefault `= False`\n\noptional Hugging Face token to use to access private Spaces. By default, no\ntoken is sent to the server. Set `hf_token=None` to use the locally saved\ntoken if there is one (warning: only provide a token if you are loading a\ntrusted private Space as the token can be read by the Space you are loading).\nFind your tokens here: https://huggingface.co/settings/tokens.\n\n\n    \n    \n    max_workers: int\n\ndefault `= 40`\n\nmaximum number of thread workers that can be used to make requests to the\nremote Gradio app simultaneously.\n\n\n    \n    \n    verbose: bool\n\ndefault `= True`\n\nwhether the client should print statements to the console.\n\n\n    \n    \n    auth: tuple[str, str] | None\n\ndefault `= None`\n\n\n    \n    \n    httpx_kwargs: dict[str, Any] | None\n\ndefault `= None`\n\nadditional keyword arguments to pass to `httpx.Client`, `httpx.stream`,\n`httpx.get` and `httpx.post`. This can be used to set timeouts, proxies, http\nauth, etc.\n\n\n    \n    \n    headers: dict[str, str] | None\n\ndefault `= None`\n\nadditional headers to send to the remote Gradio app on every request. By\ndefault only the HF authorization and user-agent headers are sent. This\nparameter will override the default headers if they have the same keys.\n\n\n    \n    \n    download_files: str | Path | Literal[False]\n\ndefault `= \"/tmp/gradio\"`\n\ndirectory where the client should download output files on the local machine\nfrom the remote API. By default, uses the value of the GRADIO_TEMP_DIR\nenvironment variable which, if not set by the user, is a temporary directory\non your machine. If False, the client does not download files and returns a\nFileData dataclass object with the filepath ", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "IR\nenvironment variable which, if not set by the user, is a temporary directory\non your machine. If False, the client does not download files and returns a\nFileData dataclass object with the filepath on the remote machine instead.\n\n\n    \n    \n    ssl_verify: bool\n\ndefault `= True`\n\nif False, skips certificate validation which allows the client to connect to\nGradio apps that are using self-signed certificates.\n\n\n    \n    \n    analytics_enabled: bool\n\ndefault `= True`\n\nWhether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED\nenvironment variable or default to True.\n\n", "heading1": "Initialization", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "Description\n\nEvent listeners allow you to respond to user interactions with the UI\ncomponents you've defined in a Gradio Blocks app. When a user interacts with\nan element, such as changing a slider value or uploading an image, a function\nis called.\n\nSupported Event Listeners\n\nThe Client component supports the following event listeners. Each event\nlistener takes the same parameters, which are listed in the Event Parameters\ntable below.\n\nListener | Description  \n---|---  \n`Client.predict(fn, \u00b7\u00b7\u00b7)` | Calls the Gradio API and returns the result (this is a blocking call). Arguments can be provided as positional arguments or as keyword arguments (latter is recommended). <br>  \n`Client.submit(fn, \u00b7\u00b7\u00b7)` | Creates and returns a Job object which calls the Gradio API in a background thread. The job can be used to retrieve the status and result of the remote API call. Arguments can be provided as positional arguments or as keyword arguments (latter is recommended). <br>  \n`Client.view_api(fn, \u00b7\u00b7\u00b7)` | Prints the usage info for the API. If the Gradio app has multiple API endpoints, the usage info for each endpoint will be printed separately. If return_format=\"dict\" the info is returned in dictionary format, as shown in the example below. <br>  \n`Client.duplicate(fn, \u00b7\u00b7\u00b7)` | Duplicates a Hugging Face Space under your account and returns a Client object for the new Space. No duplication is created if the Space already exists in your account (to override this, provide a new name for the new Space using `to_id`). To use this method, you must provide an `hf_token` or be logged in via the Hugging Face Hub CLI. <br> The new Space will be private by default and use the same hardware as the original Space. This can be changed by using the `private` and `hardware` parameters. For hardware upgrades (beyond the basic CPU tier), you may be required to provide billing information on Hugging Face: https://huggingface.co/settings/billing <br>  \n`Client.deploy_discord(fn, \u00b7\u00b7\u00b7)` | Deploy", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "dware upgrades (beyond the basic CPU tier), you may be required to provide billing information on Hugging Face: https://huggingface.co/settings/billing <br>  \n`Client.deploy_discord(fn, \u00b7\u00b7\u00b7)` | Deploy the upstream app as a discord bot. Currently only supports gr.ChatInterface.  \n  \nEvent Parameters\n\nParameters \u25bc\n\n\n    \n    \n    args: <class 'inspect._empty'>\n\nThe positional arguments to pass to the remote API endpoint. The order of the\narguments must match the order of the inputs in the Gradio app.\n\n\n    \n    \n    api_name: str | None\n\ndefault `= None`\n\nThe name of the API endpoint to call starting with a leading slash, e.g.\n\"/predict\". Does not need to be provided if the Gradio app has only one named\nAPI endpoint.\n\n\n    \n    \n    fn_index: int | None\n\ndefault `= None`\n\nAs an alternative to api_name, this parameter takes the index of the API\nendpoint to call, e.g. 0. Both api_name and fn_index can be provided, but if\nthey conflict, api_name will take precedence.\n\n\n    \n    \n    headers: dict[str, str] | None\n\ndefault `= None`\n\nAdditional headers to send to the remote Gradio app on this request. This\nparameter will overrides the headers provided in the Client constructor if\nthey have the same keys.\n\n\n    \n    \n    kwargs: <class 'inspect._empty'>\n\nThe keyword arguments to pass to the remote API endpoint.\n\n", "heading1": "Event Listeners", "source_page_url": "https://gradio.app/docs/python-client/client", "source_page_title": "Python Client - Client Docs"}, {"text": "`gradio-rs` is a Gradio Client in Rust built by\n[@JacobLinCool](https://github.com/JacobLinCool). You can find the repo\n[here](https://github.com/JacobLinCool/gradio-rs), and more in depth API\ndocumentation [here](https://docs.rs/gradio/latest/gradio/).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/docs/third-party-clients/rust-client", "source_page_title": "Third Party Clients - Rust Client Docs"}, {"text": "Here is an example of using BS-RoFormer model to separate vocals and\nbackground music from an audio file.\n\n    \n    \n    use gradio::{PredictionInput, Client, ClientOptions};\n    \n    [tokio::main]\n    async fn main() {\n        if std::env::args().len() < 2 {\n            println!(\"Please provide an audio file path as an argument\");\n            std::process::exit(1);\n        }\n        let args: Vec<String> = std::env::args().collect();\n        let file_path = &args[1];\n        println!(\"File: {}\", file_path);\n    \n        let client = Client::new(\"JacobLinCool/vocal-separation\", ClientOptions::default())\n            .await\n            .unwrap();\n    \n        let output = client\n            .predict(\n                \"/separate\",\n                vec![\n                    PredictionInput::from_file(file_path),\n                    PredictionInput::from_value(\"BS-RoFormer\"),\n                ],\n            )\n            .await\n            .unwrap();\n        println!(\n            \"Vocals: {}\",\n            output[0].clone().as_file().unwrap().url.unwrap()\n        );\n        println!(\n            \"Background: {}\",\n            output[1].clone().as_file().unwrap().url.unwrap()\n        );\n    }\n\nYou can find more examples [here](https://github.com/JacobLinCool/gradio-\nrs/tree/main/examples).\n\n", "heading1": "Usage", "source_page_url": "https://gradio.app/docs/third-party-clients/rust-client", "source_page_title": "Third Party Clients - Rust Client Docs"}, {"text": "cargo install gradio\n    gr --help\n\nTake [stabilityai/stable-\ndiffusion-3-medium](https://huggingface.co/spaces/stabilityai/stable-\ndiffusion-3-medium) HF Space as an example:\n\n    \n    \n    > gr list stabilityai/stable-diffusion-3-medium\n    API Spec for stabilityai/stable-diffusion-3-medium:\n            /infer\n                    Parameters:\n                            prompt               ( str      ) \n                            negative_prompt      ( str      ) \n                            seed                 ( float    ) numeric value between 0 and 2147483647\n                            randomize_seed       ( bool     ) \n                            width                ( float    ) numeric value between 256 and 1344\n                            height               ( float    ) numeric value between 256 and 1344\n                            guidance_scale       ( float    ) numeric value between 0.0 and 10.0\n                            num_inference_steps  ( float    ) numeric value between 1 and 50\n                    Returns:\n                            Result               ( filepath ) \n                            Seed                 ( float    ) numeric value between 0 and 2147483647\n    \n    > gr run stabilityai/stable-diffusion-3-medium infer 'Rusty text \"AI & CLI\" on the snow.' '' 0 true 1024 1024 5 28\n    Result: https://stabilityai-stable-diffusion-3-medium.hf.space/file=/tmp/gradio/5735ca7775e05f8d56d929d8f57b099a675c0a01/image.webp\n    Seed: 486085626\n\nFor file input, simply use the file path as the argument:\n\n    \n    \n    gr run hf-audio/whisper-large-v3 predict 'test-audio.wav' 'transcribe'\n    output: \" Did you know you can try the coolest model on your command line?\"\n\n", "heading1": "Command Line Interface", "source_page_url": "https://gradio.app/docs/third-party-clients/rust-client", "source_page_title": "Third Party Clients - Rust Client Docs"}, {"text": "Gradio applications support programmatic requests from many environments:\n\n  * The [Python Client](/docs/python-client): `gradio-client` allows you to make requests from Python environments.\n  * The [JavaScript Client](/docs/js-client): `@gradio/client` allows you to make requests in TypeScript from the browser or server-side.\n  * You can also query gradio apps [directly from cURL](/guides/querying-gradio-apps-with-curl).\n\n", "heading1": "Gradio Clients", "source_page_url": "https://gradio.app/docs/third-party-clients/introduction", "source_page_title": "Third Party Clients - Introduction Docs"}, {"text": "We also encourage the development and use of third party clients built by\nthe community:\n\n  * [Rust Client](/docs/third-party-clients/rust-client): `gradio-rs` built by [@JacobLinCool](https://github.com/JacobLinCool) allows you to make requests in Rust.\n  * [Powershell Client](https://github.com/rrg92/powershai): `powershai` built by [@rrg92](https://github.com/rrg92) allows you to make requests to Gradio apps directly from Powershell. See [here for documentation](https://github.com/rrg92/powershai/blob/main/docs/en-US/providers/HUGGING-FACE.md)\n\n", "heading1": "Community Clients", "source_page_url": "https://gradio.app/docs/third-party-clients/introduction", "source_page_title": "Third Party Clients - Introduction Docs"}, {"text": "The frontend code should have, at minimum, three files:\n\n* `Index.svelte`: This is the main export and where your component's layout and logic should live.\n* `Example.svelte`: This is where the example view of the component is defined.\n\nFeel free to add additional files and subdirectories. \nIf you want to export any additional modules, remember to modify the `package.json` file\n\n```json\n\"exports\": {\n    \".\": \"./Index.svelte\",\n    \"./example\": \"./Example.svelte\",\n    \"./package.json\": \"./package.json\"\n},\n```\n\n", "heading1": "The directory structure", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "Your component should expose the following props that will be passed down from the parent Gradio application.\n\n```typescript\nimport type { LoadingStatus } from \"@gradio/statustracker\";\nimport type { Gradio } from \"@gradio/utils\";\n\nexport let gradio: Gradio<{\n    event_1: never;\n    event_2: never;\n}>;\n\nexport let elem_id = \"\";\nexport let elem_classes: string[] = [];\nexport let scale: number | null = null;\nexport let min_width: number | undefined = undefined;\nexport let loading_status: LoadingStatus | undefined = undefined;\nexport let mode: \"static\" | \"interactive\";\n```\n\n* `elem_id` and `elem_classes` allow Gradio app developers to target your component with custom CSS and JavaScript from the Python `Blocks` class.\n\n* `scale` and `min_width` allow Gradio app developers to control how much space your component takes up in the UI.\n\n* `loading_status` is used to display a loading status over the component when it is the output of an event.\n\n* `mode` is how the parent Gradio app tells your component whether the `interactive` or `static` version should be displayed.\n\n* `gradio`: The `gradio` object is created by the parent Gradio app. It stores some application-level configuration that will be useful in your component, like internationalization. You must use it to dispatch events from your component.\n\nA minimal `Index.svelte` file would look like:\n\n```svelte\n<script lang=\"ts\">\n\timport type { LoadingStatus } from \"@gradio/statustracker\";\n    import { Block } from \"@gradio/atoms\";\n\timport { StatusTracker } from \"@gradio/statustracker\";\n\timport type { Gradio } from \"@gradio/utils\";\n\n\texport let gradio: Gradio<{\n\t\tevent_1: never;\n\t\tevent_2: never;\n\t}>;\n\n    export let value = \"\";\n\texport let elem_id = \"\";\n\texport let elem_classes: string[] = [];\n\texport let scale: number | null = null;\n\texport let min_width: number | undefined = undefined;\n\texport let loading_status: LoadingStatus | undefined = undefined;\n    export let mode: \"static\" | \"interactive\";\n</script>\n\n<Block\n\tvisib", "heading1": "The Index.svelte file", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": " null;\n\texport let min_width: number | undefined = undefined;\n\texport let loading_status: LoadingStatus | undefined = undefined;\n    export let mode: \"static\" | \"interactive\";\n</script>\n\n<Block\n\tvisible={true}\n\t{elem_id}\n\t{elem_classes}\n\t{scale}\n\t{min_width}\n\tallow_overflow={false}\n\tpadding={true}\n>\n\t{if loading_status}\n\t\t<StatusTracker\n\t\t\tautoscroll={gradio.autoscroll}\n\t\t\ti18n={gradio.i18n}\n\t\t\t{...loading_status}\n\t\t/>\n\t{/if}\n    <p>{value}</p>\n</Block>\n```\n\n", "heading1": "The Index.svelte file", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "The `Example.svelte` file should expose the following props:\n\n```typescript\n    export let value: string;\n    export let type: \"gallery\" | \"table\";\n    export let selected = false;\n    export let index: number;\n```\n\n* `value`: The example value that should be displayed.\n\n* `type`: This is a variable that can be either `\"gallery\"` or `\"table\"` depending on how the examples are displayed. The `\"gallery\"` form is used when the examples correspond to a single input component, while the `\"table\"` form is used when a user has multiple input components, and the examples need to populate all of them. \n\n* `selected`: You can also adjust how the examples are displayed if a user \"selects\" a particular example by using the selected variable.\n\n* `index`: The current index of the selected value.\n\n* Any additional props your \"non-example\" component takes!\n\nThis is the `Example.svelte` file for the code `Radio` component:\n\n```svelte\n<script lang=\"ts\">\n\texport let value: string;\n\texport let type: \"gallery\" | \"table\";\n\texport let selected = false;\n</script>\n\n<div\n\tclass:table={type === \"table\"}\n\tclass:gallery={type === \"gallery\"}\n\tclass:selected\n>\n\t{value}\n</div>\n\n<style>\n\t.gallery {\n\t\tpadding: var(--size-1) var(--size-2);\n\t}\n</style>\n```\n\n", "heading1": "The Example.svelte file", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "If your component deals with files, these files **should** be uploaded to the backend server. \nThe `@gradio/client` npm package provides the `upload` and `prepare_files` utility functions to help you do this.\n\nThe `prepare_files` function will convert the browser's `File` datatype to gradio's internal `FileData` type.\nYou should use the `FileData` data in your component to keep track of uploaded files.\n\nThe `upload` function will upload an array of `FileData` values to the server.\n\nHere's an example of loading files from an `<input>` element when its value changes.\n\n\n```svelte\n<script lang=\"ts\">\n    import { upload, prepare_files, type FileData } from \"@gradio/client\";\n    export let root;\n    export let value;\n    let uploaded_files;\n\n    async function handle_upload(file_data: FileData[]): Promise<void> {\n        await tick();\n        uploaded_files = await upload(file_data, root);\n    }\n\n    async function loadFiles(files: FileList): Promise<void> {\n        let _files: File[] = Array.from(files);\n        if (!files.length) {\n            return;\n        }\n        if (file_count === \"single\") {\n            _files = [files[0]];\n        }\n        let file_data = await prepare_files(_files);\n        await handle_upload(file_data);\n    }\n\n    async function loadFilesFromUpload(e: Event): Promise<void> {\n\t\tconst target = e.target;\n\n\t\tif (!target.files) return;\n\t\tawait loadFiles(target.files);\n\t}\n</script>\n\n<input\n    type=\"file\"\n    on:change={loadFilesFromUpload}\n    multiple={true}\n/>\n```\n\nThe component exposes a prop named `root`. \nThis is passed down by the parent gradio app and it represents the base url that the files will be uploaded to and fetched from.\n\nFor WASM support, you should get the upload function from the `Context` and pass that as the third parameter of the `upload` function.\n\n```typescript\n<script lang=\"ts\">\n    import { getContext } from \"svelte\";\n    const upload_fn = getContext<typeof upload_files>(\"upload_files\");\n\n    async function handle_uploa", "heading1": "Handling Files", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "he `upload` function.\n\n```typescript\n<script lang=\"ts\">\n    import { getContext } from \"svelte\";\n    const upload_fn = getContext<typeof upload_files>(\"upload_files\");\n\n    async function handle_upload(file_data: FileData[]): Promise<void> {\n        await tick();\n        await upload(file_data, root, upload_fn);\n    }\n</script>\n```\n\n", "heading1": "Handling Files", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "Most of Gradio's frontend components are published on [npm](https://www.npmjs.com/), the javascript package repository.\nThis means that you can use them to save yourself time while incorporating common patterns in your component, like uploading files.\nFor example, the `@gradio/upload` package has `Upload` and `ModifyUpload` components for properly uploading files to the Gradio server. \nHere is how you can use them to create a user interface to upload and display PDF files.\n\n```svelte\n<script>\n\timport { type FileData, Upload, ModifyUpload } from \"@gradio/upload\";\n\timport { Empty, UploadText, BlockLabel } from \"@gradio/atoms\";\n</script>\n\n<BlockLabel Icon={File} label={label || \"PDF\"} />\n{if value === null && interactive}\n    <Upload\n        filetype=\"application/pdf\"\n        on:load={handle_load}\n        {root}\n        >\n        <UploadText type=\"file\" i18n={gradio.i18n} />\n    </Upload>\n{:else if value !== null}\n    {if interactive}\n        <ModifyUpload i18n={gradio.i18n} on:clear={handle_clear}/>\n    {/if}\n    <iframe title={value.orig_name || \"PDF\"} src={value.data} height=\"{height}px\" width=\"100%\"></iframe>\n{:else}\n    <Empty size=\"large\"> <File/> </Empty>\t\n{/if}\n```\n\nYou can also combine existing Gradio components to create entirely unique experiences.\nLike rendering a gallery of chatbot conversations. \nThe possibilities are endless, please read the documentation on our javascript packages [here](https://gradio.app/main/docs/js).\nWe'll be adding more packages and documentation over the coming weeks!\n\n", "heading1": "Leveraging Existing Gradio Components", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "You can explore our component library via Storybook. You'll be able to interact with our components and see them in their various states.\n\nFor those interested in design customization, we provide the CSS variables consisting of our color palette, radii, spacing, and the icons we use - so you can easily match up your custom component with the style of our core components. This Storybook will be regularly updated with any new additions or changes.\n\n[Storybook Link](https://gradio.app/main/docs/js/storybook)\n\n", "heading1": "Matching Gradio Core's Design System", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "If you want to make use of the vast vite ecosystem, you can use the `gradio.config.js` file to configure your component's build process. This allows you to make use of tools like tailwindcss, mdsvex, and more.\n\nCurrently, it is possible to configure the following:\n\nVite options:\n- `plugins`: A list of vite plugins to use.\n\nSvelte options:\n- `preprocess`: A list of svelte preprocessors to use.\n- `extensions`: A list of file extensions to compile to `.svelte` files.\n- `build.target`: The target to build for, this may be necessary to support newer javascript features. See the [esbuild docs](https://esbuild.github.io/api/target) for more information.\n\nThe `gradio.config.js` file should be placed in the root of your component's `frontend` directory. A default config file is created for you when you create a new component. But you can also create your own config file, if one doesn't exist, and use it to customize your component's build process.\n\nExample for a Vite plugin\n\nCustom components can use Vite plugins to customize the build process. Check out the [Vite Docs](https://vitejs.dev/guide/using-plugins.html) for more information. \n\nHere we configure [TailwindCSS](https://tailwindcss.com), a utility-first CSS framework. Setup is easiest using the version 4 prerelease. \n\n```\nnpm install tailwindcss@next @tailwindcss/vite@next\n```\n\nIn `gradio.config.js`:\n\n```typescript\nimport tailwindcss from \"@tailwindcss/vite\";\nexport default {\n    plugins: [tailwindcss()]\n};\n```\n\nThen create a `style.css` file with the following content:\n\n```css\n@import \"tailwindcss\";\n```\n\nImport this file into `Index.svelte`. Note, that you need to import the css file containing `@import` and cannot just use a `<style>` tag and use `@import` there. \n\n```svelte\n<script lang=\"ts\">\n[...]\nimport \"./style.css\";\n[...]\n</script>\n```\n\nExample for Svelte options\n\nIn `gradio.config.js` you can also specify a some Svelte options to apply to the Svelte compilation. In this example we will add support for", "heading1": "Custom configuration", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": ".css\";\n[...]\n</script>\n```\n\nExample for Svelte options\n\nIn `gradio.config.js` you can also specify a some Svelte options to apply to the Svelte compilation. In this example we will add support for [`mdsvex`](https://mdsvex.pngwn.io), a Markdown preprocessor for Svelte. \n\nIn order to do this we will need to add a [Svelte Preprocessor](https://svelte.dev/docs/svelte-compilerpreprocess) to the `svelte` object in `gradio.config.js` and configure the [`extensions`](https://github.com/sveltejs/vite-plugin-svelte/blob/HEAD/docs/config.mdconfig-file) field. Other options are not currently supported.\n\nFirst, install the `mdsvex` plugin:\n\n```bash\nnpm install mdsvex\n```\n\nThen add the following to `gradio.config.js`:\n\n```typescript\nimport { mdsvex } from \"mdsvex\";\n\nexport default {\n    svelte: {\n        preprocess: [\n            mdsvex()\n        ],\n        extensions: [\".svelte\", \".svx\"]\n    }\n};\n```\n\nNow we can create `mdsvex` documents in our component's `frontend` directory and they will be compiled to `.svelte` files.\n\n```md\n<!-- HelloWorld.svx -->\n\n<script lang=\"ts\">\n    import { Block } from \"@gradio/atoms\";\n\n    export let title = \"Hello World\";\n</script>\n\n<Block label=\"Hello World\">\n\n{title}\n\nThis is a markdown file.\n\n</Block>\n```\n\nWe can then use the `HelloWorld.svx` file in our components:\n\n```svelte\n<script lang=\"ts\">\n    import HelloWorld from \"./HelloWorld.svx\";\n</script>\n\n<HelloWorld />\n```\n\n", "heading1": "Custom configuration", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "You now know how to create delightful frontends for your components!\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/frontend", "source_page_title": "Custom Components - Frontend Guide"}, {"text": "Every component in Gradio comes in a `static` variant, and most come in an `interactive` version as well.\nThe `static` version is used when a component is displaying a value, and the user can **NOT** change that value by interacting with it. \nThe `interactive` version is used when the user is able to change the value by interacting with the Gradio UI.\n\nLet's see some examples:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n   gr.Textbox(value=\"Hello\", interactive=True)\n   gr.Textbox(value=\"Hello\", interactive=False)\n\ndemo.launch()\n\n```\nThis will display two textboxes.\nThe only difference: you'll be able to edit the value of the Gradio component on top, and you won't be able to edit the variant on the bottom (i.e. the textbox will be disabled).\n\nPerhaps a more interesting example is with the `Image` component:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n   gr.Image(interactive=True)\n   gr.Image(interactive=False)\n\ndemo.launch()\n```\n\nThe interactive version of the component is much more complex -- you can upload images or snap a picture from your webcam -- while the static version can only be used to display images.\n\nNot every component has a distinct interactive version. For example, the `gr.AnnotatedImage` only appears as a static version since there's no way to interactively change the value of the annotations or the image.\n\nWhat you need to remember\n\n* Gradio will use the interactive version (if available) of a component if that component is used as the **input** to any event; otherwise, the static version will be used.\n\n* When you design custom components, you **must** accept the boolean interactive keyword in the constructor of your Python class. In the frontend, you **may** accept the `interactive` property, a `bool` which represents whether the component should be static or interactive. If you do not use this property in the frontend, the component will appear the same in interactive or static mode.\n\n", "heading1": "Interactive vs Static", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": "The most important attribute of a component is its `value`.\nEvery component has a `value`.\nThe value that is typically set by the user in the frontend (if the component is interactive) or displayed to the user (if it is static). \nIt is also this value that is sent to the backend function when a user triggers an event, or returned by the user's function e.g. at the end of a prediction.\n\nSo this value is passed around quite a bit, but sometimes the format of the value needs to change between the frontend and backend. \nTake a look at this example:\n\n```python\nimport numpy as np\nimport gradio as gr\n\ndef sepia(input_img):\n    sepia_filter = np.array([\n        [0.393, 0.769, 0.189], \n        [0.349, 0.686, 0.168], \n        [0.272, 0.534, 0.131]\n    ])\n    sepia_img = input_img.dot(sepia_filter.T)\n    sepia_img /= sepia_img.max()\n    return sepia_img\n\ndemo = gr.Interface(sepia, gr.Image(width=200, height=200), \"image\")\ndemo.launch()\n```\n\nThis will create a Gradio app which has an `Image` component as the input and the output. \nIn the frontend, the Image component will actually **upload** the file to the server and send the **filepath** but this is converted to a `numpy` array before it is sent to a user's function. \nConversely, when the user returns a `numpy` array from their function, the numpy array is converted to a file so that it can be sent to the frontend and displayed by the `Image` component.\n\nTip: By default, the `Image` component sends numpy arrays to the python function because it is a common choice for machine learning engineers, though the Image component also supports other formats using the `type` parameter.  Read the `Image` docs [here](https://www.gradio.app/docs/image) to learn more.\n\nEach component does two conversions:\n\n1. `preprocess`: Converts the `value` from the format sent by the frontend to the format expected by the python function. This usually involves going from a web-friendly **JSON** structure to a **python-native** data structure, like a `n", "heading1": "The value and how it is preprocessed/postprocessed", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": " from the format sent by the frontend to the format expected by the python function. This usually involves going from a web-friendly **JSON** structure to a **python-native** data structure, like a `numpy` array or `PIL` image. The `Audio`, `Image` components are good examples of `preprocess` methods.\n\n2. `postprocess`: Converts the value returned by the python function to the format expected by the frontend. This usually involves going from a **python-native** data-structure, like a `PIL` image to a **JSON** structure.\n\nWhat you need to remember\n\n* Every component must implement `preprocess` and `postprocess` methods. In the rare event that no conversion needs to happen, simply return the value as-is. `Textbox` and `Number` are examples of this. \n\n* As a component author, **YOU** control the format of the data displayed in the frontend as well as the format of the data someone using your component will receive. Think of an ergonomic data-structure a **python** developer will find intuitive, and control the conversion from a **Web-friendly JSON** data structure (and vice-versa) with `preprocess` and `postprocess.`\n\n", "heading1": "The value and how it is preprocessed/postprocessed", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": "Gradio apps support providing example inputs -- and these are very useful in helping users get started using your Gradio app. \nIn `gr.Interface`, you can provide examples using the `examples` keyword, and in `Blocks`, you can provide examples using the special `gr.Examples` component.\n\nAt the bottom of this screenshot, we show a miniature example image of a cheetah that, when clicked, will populate the same image in the input Image component:\n\n![img](https://user-images.githubusercontent.com/1778297/277548211-a3cb2133-2ffc-4cdf-9a83-3e8363b57ea6.png)\n\n\nTo enable the example view, you must have the following two files in the top of the `frontend` directory:\n\n* `Example.svelte`: this corresponds to the \"example version\" of your component\n* `Index.svelte`: this corresponds to the \"regular version\"\n\nIn the backend, you typically don't need to do anything. The user-provided example `value` is processed using the same `.postprocess()` method described earlier. If you'd like to do process the data differently (for example, if the `.postprocess()` method is computationally expensive), then you can write your own `.process_example()` method for your custom component, which will be used instead. \n\nThe `Example.svelte` file and `process_example()` method will be covered in greater depth in the dedicated [frontend](./frontend) and [backend](./backend) guides respectively.\n\nWhat you need to remember\n\n* If you expect your component to be used as input, it is important to define an \"Example\" view.\n* If you don't, Gradio will use a default one but it won't be as informative as it can be!\n\n", "heading1": "The \"Example Version\" of a Component", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": "Now that you know the most important pieces to remember about Gradio components, you can start to design and build your own!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/key-component-concepts", "source_page_title": "Custom Components - Key Component Concepts Guide"}, {"text": "You will need to have:\n\n* Python 3.10+ (<a href=\"https://www.python.org/downloads/\" target=\"_blank\">install here</a>)\n* pip 21.3+ (`python -m pip install --upgrade pip`)\n* Node.js 20+ (<a href=\"https://nodejs.dev/en/download/package-manager/\" target=\"_blank\">install here</a>)\n* npm 9+ (<a href=\"https://docs.npmjs.com/downloading-and-installing-node-js-and-npm/\" target=\"_blank\">install here</a>)\n* Gradio 5+ (`pip install --upgrade gradio`)\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "The Custom Components workflow consists of 4 steps: create, dev, build, and publish.\n\n1. create: creates a template for you to start developing a custom component.\n2. dev: launches a development server with a sample app & hot reloading allowing you to easily develop your custom component\n3. build: builds a python package containing to your custom component's Python and JavaScript code -- this makes things official!\n4. publish: uploads your package to [PyPi](https://pypi.org/) and/or a sample app to [HuggingFace Spaces](https://hf.co/spaces).\n\nEach of these steps is done via the Custom Component CLI. You can invoke it with `gradio cc` or `gradio component`\n\nTip: Run `gradio cc --help` to get a help menu of all available commands. There are some commands that are not covered in this guide. You can also append `--help` to any command name to bring up a help page for that command, e.g. `gradio cc create --help`.\n\n", "heading1": "The Workflow", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Bootstrap a new template by running the following in any working directory:\n\n```bash\ngradio cc create MyComponent --template SimpleTextbox\n```\n\nInstead of `MyComponent`, give your component any name.\n\nInstead of `SimpleTextbox`, you can use any Gradio component as a template. `SimpleTextbox` is actually a special component that a stripped-down version of the `Textbox` component that makes it particularly useful when creating your first custom component.\nSome other components that are good if you are starting out: `SimpleDropdown`, `SimpleImage`, or `File`.\n\nTip: Run `gradio cc show` to get a list of available component templates.\n\nThe `create` command will:\n\n1. Create a directory with your component's name in lowercase with the following structure:\n```directory\n- backend/ <- The python code for your custom component\n- frontend/ <- The javascript code for your custom component\n- demo/ <- A sample app using your custom component. Modify this to develop your component!\n- pyproject.toml <- Used to build the package and specify package metadata.\n```\n\n2. Install the component in development mode\n\nEach of the directories will have the code you need to get started developing!\n\n", "heading1": "1. create", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Once you have created your new component, you can start a development server by `entering the directory` and running\n\n```bash\ngradio cc dev\n```\n\nYou'll see several lines that are printed to the console.\nThe most important one is the one that says:\n\n> Frontend Server (Go here): http://localhost:7861/\n\nThe port number might be different for you.\nClick on that link to launch the demo app in hot reload mode.\nNow, you can start making changes to the backend and frontend you'll see the results reflected live in the sample app!\nWe'll go through a real example in a later guide.\n\nTip: You don't have to run dev mode from your custom component directory. The first argument to `dev` mode is the path to the directory. By default it uses the current directory.\n\n", "heading1": "2. dev", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Once you are satisfied with your custom component's implementation, you can `build` it to use it outside of the development server.\n\nFrom your component directory, run:\n\n```bash\ngradio cc build\n```\n\nThis will create a `tar.gz` and `.whl` file in a `dist/` subdirectory.\nIf you or anyone installs that `.whl` file (`pip install <path-to-whl>`) they will be able to use your custom component in any gradio app!\n\nThe `build` command will also generate documentation for your custom component. This takes the form of an interactive space and a static `README.md`. You can disable this by passing `--no-generate-docs`. You can read more about the documentation generator in [the dedicated guide](https://gradio.app/guides/documenting-custom-components).\n\n", "heading1": "3. build", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Right now, your package is only available on a `.whl` file on your computer.\nYou can share that file with the world with the `publish` command!\n\nSimply run the following command from your component directory:\n\n```bash\ngradio cc publish\n```\n\nThis will guide you through the following process:\n\n1. Upload your distribution files to PyPi. This makes it easier to upload the demo to Hugging Face spaces. Otherwise your package must be at a publicly available url. If you decide to upload to PyPi, you will need a PyPI username and password. You can get one [here](https://pypi.org/account/register/).\n2. Upload a demo of your component to hugging face spaces. This is also optional.\n\n\nHere is an example of what publishing looks like:\n\n<video autoplay muted loop>\n  <source src=\"https://gradio-builds.s3.amazonaws.com/assets/text_with_attachments_publish.mov\" type=\"video/mp4\" />\n</video>\n\n\n", "heading1": "4. publish", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Now that you know the high-level workflow of creating custom components, you can go in depth in the next guides!\nAfter reading the guides, check out this [collection](https://huggingface.co/collections/gradio/custom-components-65497a761c5192d981710b12) of custom components on the HuggingFace Hub so you can learn from other's code.\n\nTip: If you want to start off from someone else's custom component see this [guide](./frequently-asked-questionsdo-i-always-need-to-start-my-component-from-scratch).\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/custom-components-in-five-minutes", "source_page_title": "Custom Components - Custom Components In Five Minutes Guide"}, {"text": "Make sure you have gradio 5.0 or higher installed as well as node 20+.\nAs of the time of publication, the latest release is 4.1.1.\nAlso, please read the [Five Minute Tour](./custom-components-in-five-minutes) of custom components and the [Key Concepts](./key-component-concepts) guide before starting.\n\n\n", "heading1": "Step 0: Prerequisites", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "Navigate to a directory of your choosing and run the following command:\n\n```bash\ngradio cc create PDF\n```\n\n\nTip: You should change the name of the component.\nSome of the screenshots assume the component is called `PDF` but the concepts are the same!\n\nThis will create a subdirectory called `pdf` in your current working directory.\nThere are three main subdirectories in `pdf`: `frontend`, `backend`, and `demo`.\nIf you open `pdf` in your code editor, it will look like this:\n\n![directory structure](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/CodeStructure.png)\n\nTip: For this demo we are not templating off a current gradio component. But you can see the list of available templates with `gradio cc show` and then pass the template name to the `--template` option, e.g. `gradio cc create <Name> --template <foo>`\n\n", "heading1": "Step 1: Creating the custom component", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "We're going to use the [pdfjs](https://mozilla.github.io/pdf.js/) javascript library to display the pdfs in the frontend. \nLet's start off by adding it to our frontend project's dependencies, as well as adding a couple of other projects we'll need.\n\nFrom within the `frontend` directory, run `npm install @gradio/client @gradio/upload @gradio/icons @gradio/button` and `npm install --save-dev pdfjs-dist@3.11.174`.\nAlso, let's uninstall the `@zerodevx/svelte-json-view` dependency by running `npm uninstall @zerodevx/svelte-json-view`.\n\nThe complete `package.json` should look like this:\n\n```json\n{\n  \"name\": \"gradio_pdf\",\n  \"version\": \"0.2.0\",\n  \"description\": \"Gradio component for displaying PDFs\",\n  \"type\": \"module\",\n  \"author\": \"\",\n  \"license\": \"ISC\",\n  \"private\": false,\n  \"main_changeset\": true,\n  \"exports\": {\n    \".\": \"./Index.svelte\",\n    \"./example\": \"./Example.svelte\",\n    \"./package.json\": \"./package.json\"\n  },\n  \"devDependencies\": {\n    \"pdfjs-dist\": \"3.11.174\"\n  },\n  \"dependencies\": {\n    \"@gradio/atoms\": \"0.2.0\",\n    \"@gradio/statustracker\": \"0.3.0\",\n    \"@gradio/utils\": \"0.2.0\",\n    \"@gradio/client\": \"0.7.1\",\n    \"@gradio/upload\": \"0.3.2\",\n    \"@gradio/icons\": \"0.2.0\",\n    \"@gradio/button\": \"0.2.3\",\n    \"pdfjs-dist\": \"3.11.174\"\n  }\n}\n```\n\n\nTip: Running `npm install` will install the latest version of the package available. You can install a specific version with `npm install package@<version>`.  You can find all of the gradio javascript package documentation [here](https://www.gradio.app/main/docs/js). It is recommended you use the same versions as me as the API can change.\n\nNavigate to `Index.svelte` and delete mentions of `JSONView`\n\n```ts\nimport { JsonView } from \"@zerodevx/svelte-json-view\";\n```\n\n```svelte\n<JsonView json={value} />\n```\n\n", "heading1": "Step 2: Frontend - modify javascript dependencies", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "Run the `dev` command to launch the development server.\nThis will open the demo in `demo/app.py` in an environment where changes to the `frontend` and `backend` directories will reflect instantaneously in the launched app.\n\nAfter launching the dev server, you should see a link printed to your console that says `Frontend Server (Go here): ... `.\n \n![](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/dev_server_terminal.png)\n\nYou should see the following:\n\n![](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/frontend_start.png)\n\n\nIts not impressive yet but we're ready to start coding!\n\n", "heading1": "Step 3: Frontend - Launching the Dev Server", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "We're going to start off by first writing the skeleton of our frontend and then adding the pdf rendering logic.\nAdd the following imports and expose the following properties to the top of your file in the `<script>` tag.\nYou may get some warnings from your code editor that some props are not used.\nThat's ok.\n\n```ts\n    import { tick } from \"svelte\";\n    import type { Gradio } from \"@gradio/utils\";\n    import { Block, BlockLabel } from \"@gradio/atoms\";\n    import { File } from \"@gradio/icons\";\n    import { StatusTracker } from \"@gradio/statustracker\";\n    import type { LoadingStatus } from \"@gradio/statustracker\";\n    import type { FileData } from \"@gradio/client\";\n    import { Upload, ModifyUpload } from \"@gradio/upload\";\n\n\texport let elem_id = \"\";\n\texport let elem_classes: string[] = [];\n\texport let visible = true;\n\texport let value: FileData | null = null;\n\texport let container = true;\n\texport let scale: number | null = null;\n\texport let root: string;\n\texport let height: number | null = 500;\n\texport let label: string;\n\texport let proxy_url: string;\n\texport let min_width: number | undefined = undefined;\n\texport let loading_status: LoadingStatus;\n\texport let gradio: Gradio<{\n\t\tchange: never;\n\t\tupload: never;\n\t}>;\n\n    let _value = value;\n    let old_value = _value;\n```\n\n\nTip: The `gradio`` object passed in here contains some metadata about the application as well as some utility methods. One of these utilities is a dispatch method. We want to dispatch change and upload events whenever our PDF is changed or updated. This line provides type hints that these are the only events we will be dispatching.\n\nWe want our frontend component to let users upload a PDF document if there isn't one already loaded.\nIf it is loaded, we want to display it underneath a \"clear\" button that lets our users upload a new document. \nWe're going to use the `Upload` and `ModifyUpload` components that come with the `@gradio/upload` package to do this.\nUnderneath the `</script>` tag, delete all ", "heading1": "Step 4: Frontend - The basic skeleton", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "lets our users upload a new document. \nWe're going to use the `Upload` and `ModifyUpload` components that come with the `@gradio/upload` package to do this.\nUnderneath the `</script>` tag, delete all the current code and add the following:\n\n```svelte\n<Block {visible} {elem_id} {elem_classes} {container} {scale} {min_width}>\n    {if loading_status}\n        <StatusTracker\n            autoscroll={gradio.autoscroll}\n            i18n={gradio.i18n}\n            {...loading_status}\n        />\n    {/if}\n    <BlockLabel\n        show_label={label !== null}\n        Icon={File}\n        float={value === null}\n        label={label || \"File\"}\n    />\n    {if _value}\n        <ModifyUpload i18n={gradio.i18n} absolute />\n    {:else}\n        <Upload\n            filetype={\"application/pdf\"}\n            file_count=\"single\"\n            {root}\n        >\n            Upload your PDF\n        </Upload>\n    {/if}\n</Block>\n```\n\nYou should see the following when you navigate to your app after saving your current changes:\n\n![](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/frontend_1.png)\n\n", "heading1": "Step 4: Frontend - The basic skeleton", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "The `Upload your PDF` text looks a bit small and barebones. \nLets customize it!\n\nCreate a new file called `PdfUploadText.svelte` and copy the following code.\nIts creating a new div to display our \"upload text\" with some custom styling.\n\nTip: Notice that we're leveraging Gradio core's existing css variables here: `var(--size-60)` and `var(--body-text-color-subdued)`. This allows our component to work nicely in light mode and dark mode, as well as with Gradio's built-in themes.\n\n\n```svelte\n<script lang=\"ts\">\n\timport { Upload as UploadIcon } from \"@gradio/icons\";\n\texport let hovered = false;\n\n</script>\n\n<div class=\"wrap\">\n\t<span class=\"icon-wrap\" class:hovered><UploadIcon /> </span>\n    Drop PDF\n    <span class=\"or\">- or -</span>\n    Click to Upload\n</div>\n\n<style>\n\t.wrap {\n\t\tdisplay: flex;\n\t\tflex-direction: column;\n\t\tjustify-content: center;\n\t\talign-items: center;\n\t\tmin-height: var(--size-60);\n\t\tcolor: var(--block-label-text-color);\n\t\tline-height: var(--line-md);\n\t\theight: 100%;\n\t\tpadding-top: var(--size-3);\n\t}\n\n\t.or {\n\t\tcolor: var(--body-text-color-subdued);\n\t\tdisplay: flex;\n\t}\n\n\t.icon-wrap {\n\t\twidth: 30px;\n\t\tmargin-bottom: var(--spacing-lg);\n\t}\n\n\t@media (--screen-md) {\n\t\t.wrap {\n\t\t\tfont-size: var(--text-lg);\n\t\t}\n\t}\n\n\t.hovered {\n\t\tcolor: var(--color-accent);\n\t}\n</style>\n```\n\nNow import `PdfUploadText.svelte` in your `<script>` and pass it to the `Upload` component!\n\n```svelte\n\timport PdfUploadText from \"./PdfUploadText.svelte\";\n\n...\n\n    <Upload\n        filetype={\"application/pdf\"}\n        file_count=\"single\"\n        {root}\n    >\n        <PdfUploadText />\n    </Upload>\n```\n\nAfter saving your code, the frontend should now look like this:\n\n![](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/better_upload.png)\n\n", "heading1": "Step 5: Frontend - Nicer Upload Text", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "This is the most advanced javascript part.\nIt took me a while to figure it out!\nDo not worry if you have trouble, the important thing is to not be discouraged \ud83d\udcaa\nAsk for help in the gradio [discord](https://discord.gg/hugging-face-879548962464493619) if you need and ask for help.\n\nWith that out of the way, let's start off by importing `pdfjs` and loading the code of the pdf worker from the mozilla cdn.\n\n```ts\n\timport pdfjsLib from \"pdfjs-dist\";\n    ...\n    pdfjsLib.GlobalWorkerOptions.workerSrc =  \"https://cdn.bootcss.com/pdf.js/3.11.174/pdf.worker.js\";\n```\n\nAlso create the following variables:\n\n```ts\n    let pdfDoc;\n    let numPages = 1;\n    let currentPage = 1;\n    let canvasRef;\n```\n\nNow, we will use `pdfjs` to render a given page of the PDF onto an `html` document.\nAdd the following code to `Index.svelte`:\n\n```ts\n    async function get_doc(value: FileData) {\n        const loadingTask = pdfjsLib.getDocument(value.url);\n        pdfDoc = await loadingTask.promise;\n        numPages = pdfDoc.numPages;\n        render_page();\n    }\n\n    function render_page() {\n    // Render a specific page of the PDF onto the canvas\n        pdfDoc.getPage(currentPage).then(page => {\n            const ctx  = canvasRef.getContext('2d')\n            ctx.clearRect(0, 0, canvasRef.width, canvasRef.height);\n            let viewport = page.getViewport({ scale: 1 });\n            let scale = height / viewport.height;\n            viewport = page.getViewport({ scale: scale });\n\n            const renderContext = {\n                canvasContext: ctx,\n                viewport,\n            };\n            canvasRef.width = viewport.width;\n            canvasRef.height = viewport.height;\n            page.render(renderContext);\n        });\n    }\n\n    // If the value changes, render the PDF of the currentPage\n    $: if(JSON.stringify(old_value) != JSON.stringify(_value)) {\n        if (_value){\n            get_doc(_value);\n        }\n        old_value = _value;\n        gradio.dispatch(\"change\");\n    }\n```\n\n\n", "heading1": "Step 6: PDF Rendering logic", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "    $: if(JSON.stringify(old_value) != JSON.stringify(_value)) {\n        if (_value){\n            get_doc(_value);\n        }\n        old_value = _value;\n        gradio.dispatch(\"change\");\n    }\n```\n\n\nTip: The `$:` syntax in svelte is how you declare statements to be reactive. Whenever any of the inputs of the statement change, svelte will automatically re-run that statement.\n\nNow place the `canvas` underneath the `ModifyUpload` component:\n\n```svelte\n<div class=\"pdf-canvas\" style=\"height: {height}px\">\n    <canvas bind:this={canvasRef}></canvas>\n</div>\n```\n\nAnd add the following styles to the `<style>` tag:\n\n```svelte\n<style>\n    .pdf-canvas {\n        display: flex;\n        justify-content: center;\n        align-items: center;\n    }\n</style>\n```\n\n", "heading1": "Step 6: PDF Rendering logic", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "Now for the fun part - actually rendering the PDF when the file is uploaded!\nAdd the following functions to the `<script>` tag:\n\n```ts\n    async function handle_clear() {\n        _value = null;\n        await tick();\n        gradio.dispatch(\"change\");\n    }\n\n    async function handle_upload({detail}: CustomEvent<FileData>): Promise<void> {\n        value = detail;\n        await tick();\n        gradio.dispatch(\"change\");\n        gradio.dispatch(\"upload\");\n    }\n```\n\n\nTip: The `gradio.dispatch` method is actually what is triggering the `change` or `upload` events in the backend. For every event defined in the component's backend, we will explain how to do this in Step 9, there must be at least one `gradio.dispatch(\"<event-name>\")` call. These are called `gradio` events and they can be listended from the entire Gradio application. You can dispatch a built-in `svelte` event with the `dispatch` function. These events can only be listened to from the component's direct parent. Learn about svelte events from the [official documentation](https://learn.svelte.dev/tutorial/component-events).\n\nNow we will run these functions whenever the `Upload` component uploads a file and whenever the `ModifyUpload` component clears the current file. The `<Upload>` component dispatches a `load` event with a payload of type `FileData` corresponding to the uploaded file. The `on:load` syntax tells `Svelte` to automatically run this function in response to the event.\n\n```svelte\n    <ModifyUpload i18n={gradio.i18n} on:clear={handle_clear} absolute />\n    \n    ...\n    \n    <Upload\n        on:load={handle_upload}\n        filetype={\"application/pdf\"}\n        file_count=\"single\"\n        {root}\n    >\n        <PdfUploadText/>\n    </Upload>\n```\n\nCongratulations! You have a working pdf uploader!\n\n![upload-gif](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/pdf_component_gif_docs.gif)\n\n", "heading1": "Step 7: Handling The File Upload And Clear", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "If a user uploads a PDF document with multiple pages, they will only be able to see the first one.\nLet's add some buttons to help them navigate the page.\nWe will use the `BaseButton` from `@gradio/button` so that they look like regular Gradio buttons.\n\nImport the `BaseButton` and add the following functions that will render the next and previous page of the PDF.\n\n```ts\n    import { BaseButton } from \"@gradio/button\";\n\n    ...\n\n    function next_page() {\n        if (currentPage >= numPages) {\n            return;\n        }\n        currentPage++;\n        render_page();\n    }\n\n    function prev_page() {\n        if (currentPage == 1) {\n            return;\n        }\n        currentPage--;\n        render_page();\n    }\n```\n\nNow we will add them underneath the canvas in a separate `<div>`\n\n```svelte\n    ...\n\n    <ModifyUpload i18n={gradio.i18n} on:clear={handle_clear} absolute />\n    <div class=\"pdf-canvas\" style=\"height: {height}px\">\n        <canvas bind:this={canvasRef}></canvas>\n    </div>\n    <div class=\"button-row\">\n        <BaseButton on:click={prev_page}>\n            \u2b05\ufe0f\n        </BaseButton>\n        <span class=\"page-count\"> {currentPage} / {numPages} </span>\n        <BaseButton on:click={next_page}>\n            \u27a1\ufe0f\n        </BaseButton>\n    </div>\n    \n    ...\n\n<style>\n    .button-row {\n        display: flex;\n        flex-direction: row;\n        width: 100%;\n        justify-content: center;\n        align-items: center;\n    }\n\n    .page-count {\n        margin: 0 10px;\n        font-family: var(--font-mono);\n    }\n```\n\nCongratulations! The frontend is almost complete \ud83c\udf89\n\n![multipage-pdf-gif](https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/pdf_multipage.gif)\n\n", "heading1": "Step 8: Adding buttons to navigate pages", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "We're going to want users of our component to get a preview of the PDF if its used as an `example` in a `gr.Interface` or `gr.Examples`.\n\nTo do so, we're going to add some of the pdf rendering logic in `Index.svelte` to `Example.svelte`.\n\n\n```svelte\n<script lang=\"ts\">\n\texport let value: string;\n\texport let type: \"gallery\" | \"table\";\n\texport let selected = false;\n\timport pdfjsLib from \"pdfjs-dist\";\n\tpdfjsLib.GlobalWorkerOptions.workerSrc =  \"https://cdn.bootcss.com/pdf.js/3.11.174/pdf.worker.js\";\n\t\n\tlet pdfDoc;\n\tlet canvasRef;\n\n\tasync function get_doc(url: string) {\n\t\tconst loadingTask = pdfjsLib.getDocument(url);\n\t\tpdfDoc = await loadingTask.promise;\n\t\trenderPage();\n\t\t}\n\n\tfunction renderPage() {\n\t\t// Render a specific page of the PDF onto the canvas\n\t\t\tpdfDoc.getPage(1).then(page => {\n\t\t\t\tconst ctx  = canvasRef.getContext('2d')\n\t\t\t\tctx.clearRect(0, 0, canvasRef.width, canvasRef.height);\n\t\t\t\t\n\t\t\t\tconst viewport = page.getViewport({ scale: 0.2 });\n\t\t\t\t\n\t\t\t\tconst renderContext = {\n\t\t\t\t\tcanvasContext: ctx,\n\t\t\t\t\tviewport\n\t\t\t\t};\n\t\t\t\tcanvasRef.width = viewport.width;\n\t\t\t\tcanvasRef.height = viewport.height;\n\t\t\t\tpage.render(renderContext);\n\t\t\t});\n\t\t}\n\t\n\t$: get_doc(value);\n</script>\n\n<div\n\tclass:table={type === \"table\"}\n\tclass:gallery={type === \"gallery\"}\n\tclass:selected\n\tstyle=\"justify-content: center; align-items: center; display: flex; flex-direction: column;\"\n>\n\t<canvas bind:this={canvasRef}></canvas>\n</div>\n\n<style>\n\t.gallery {\n\t\tpadding: var(--size-1) var(--size-2);\n\t}\n</style>\n```\n\n\nTip: Exercise for the reader - reduce the code duplication between `Index.svelte` and `Example.svelte` \ud83d\ude0a\n\n\nYou will not be able to render examples until we make some changes to the backend code in the next step!\n\n", "heading1": "Step 8.5: The Example view", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "The backend changes needed are smaller.\nWe're almost done!\n\nWhat we're going to do is:\n* Add `change` and `upload` events to our component.\n* Add a `height` property to let users control the height of the PDF.\n* Set the `data_model` of our component to be `FileData`. This is so that Gradio can automatically cache and safely serve any files that are processed by our component.\n* Modify the `preprocess` method to return a string corresponding to the path of our uploaded PDF.\n* Modify the `postprocess` to turn a path to a PDF created in an event handler to a `FileData`.\n\nWhen all is said an done, your component's backend code should look like this:\n\n```python\nfrom __future__ import annotations\nfrom typing import Any, Callable, TYPE_CHECKING\n\nfrom gradio.components.base import Component\nfrom gradio.data_classes import FileData\nfrom gradio import processing_utils\nif TYPE_CHECKING:\n    from gradio.components import Timer\n\nclass PDF(Component):\n\n    EVENTS = [\"change\", \"upload\"]\n\n    data_model = FileData\n\n    def __init__(self, value: Any = None, *,\n                 height: int | None = None,\n                 label: str | I18nData | None = None,\n                 info: str | I18nData | None = None,\n                 show_label: bool | None = None,\n                 container: bool = True,\n                 scale: int | None = None,\n                 min_width: int | None = None,\n                 interactive: bool | None = None,\n                 visible: bool = True,\n                 elem_id: str | None = None,\n                 elem_classes: list[str] | str | None = None,\n                 render: bool = True,\n                 load_fn: Callable[..., Any] | None = None,\n                 every: Timer | float | None = None):\n        super().__init__(value, label=label, info=info,\n                         show_label=show_label, container=container,\n                         scale=scale, min_width=min_width,\n                         interactive=interactive, visible=visible,\n          ", "heading1": "Step 9: The backend", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "                    show_label=show_label, container=container,\n                         scale=scale, min_width=min_width,\n                         interactive=interactive, visible=visible,\n                         elem_id=elem_id, elem_classes=elem_classes,\n                         render=render, load_fn=load_fn, every=every)\n        self.height = height\n\n    def preprocess(self, payload: FileData) -> str:\n        return payload.path\n\n    def postprocess(self, value: str | None) -> FileData:\n        if not value:\n            return None\n        return FileData(path=value)\n\n    def example_payload(self):\n        return \"https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/fw9.pdf\"\n\n    def example_value(self):\n        return \"https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/fw9.pdf\"\n```\n\n", "heading1": "Step 9: The backend", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "To test our backend code, let's add a more complex demo that performs Document Question and Answering with huggingface transformers.\n\nIn our `demo` directory, create a `requirements.txt` file with the following packages\n\n```\ntorch\ntransformers\npdf2image\npytesseract\n```\n\n\nTip: Remember to install these yourself and restart the dev server! You may need to install extra non-python dependencies for `pdf2image`. See [here](https://pypi.org/project/pdf2image/). Feel free to write your own demo if you have trouble.\n\n\n```python\nimport gradio as gr\nfrom gradio_pdf import PDF\nfrom pdf2image import convert_from_path\nfrom transformers import pipeline\nfrom pathlib import Path\n\ndir_ = Path(__file__).parent\n\np = pipeline(\n    \"document-question-answering\",\n    model=\"impira/layoutlm-document-qa\",\n)\n\ndef qa(question: str, doc: str) -> str:\n    img = convert_from_path(doc)[0]\n    output = p(img, question)\n    return sorted(output, key=lambda x: x[\"score\"], reverse=True)[0]['answer']\n\n\ndemo = gr.Interface(\n    qa,\n    [gr.Textbox(label=\"Question\"), PDF(label=\"Document\")],\n    gr.Textbox(),\n)\n\ndemo.launch()\n```\n\nSee our demo in action below!\n\n<video autoplay muted loop>\n  <source src=\"https://gradio-builds.s3.amazonaws.com/assets/pdf-guide/PDFDemo.mov\" type=\"video/mp4\" />\n</video>\n\nFinally lets build our component with `gradio cc build` and publish it with the `gradio cc publish` command!\nThis will guide you through the process of uploading your component to [PyPi](https://pypi.org/) and [HuggingFace Spaces](https://huggingface.co/spaces).\n\n\nTip: You may need to add the following lines to the `Dockerfile` of your HuggingFace Space.\n\n```Dockerfile\nRUN mkdir -p /tmp/cache/\nRUN chmod a+rwx -R /tmp/cache/\nRUN apt-get update && apt-get install -y poppler-utils tesseract-ocr\n\nENV TRANSFORMERS_CACHE=/tmp/cache/\n```\n\n", "heading1": "Step 10: Add a demo and publish!", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "In order to use our new component in **any** gradio 4.0 app, simply install it with pip, e.g. `pip install gradio-pdf`. Then you can use it like the built-in `gr.File()` component (except that it will only accept and display PDF files).\n\nHere is a simple demo with the Blocks api:\n\n```python\nimport gradio as gr\nfrom gradio_pdf import PDF\n\nwith gr.Blocks() as demo:\n    pdf = PDF(label=\"Upload a PDF\", interactive=True)\n    name = gr.Textbox()\n    pdf.upload(lambda f: f, pdf, name)\n\ndemo.launch()\n```\n\n\nI hope you enjoyed this tutorial!\nThe complete source code for our component is [here](https://huggingface.co/spaces/freddyaboulton/gradio_pdf/tree/main/src).\nPlease don't hesitate to reach out to the gradio community on the [HuggingFace Discord](https://discord.gg/hugging-face-879548962464493619) if you get stuck.\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/pdf-component-example", "source_page_title": "Custom Components - Pdf Component Example Guide"}, {"text": "The documentation will be generated when running `gradio cc build`. You can pass the `--no-generate-docs` argument to turn off this behaviour.\n\nThere is also a standalone `docs` command that allows for greater customisation. If you are running this command manually it should be run _after_ the `version` in your `pyproject.toml` has been bumped but before building the component.\n\nAll arguments are optional.\n\n```bash\ngradio cc docs\n  path The directory of the custom component.\n  --demo-dir Path to the demo directory.\n  --demo-name Name of the demo file\n  --space-url URL of the Hugging Face Space to link to\n  --generate-space create a documentation space.\n  --no-generate-space do not create a documentation space\n  --readme-path Path to the README.md file.\n  --generate-readme create a REAMDE.md file\n  --no-generate-readme do not create a README.md file\n  --suppress-demo-check suppress validation checks and warnings\n```\n\n", "heading1": "How do I use it?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "The `gradio cc docs` command will generate an interactive Gradio app and a static README file with various features. You can see an example here:\n\n- [Gradio app deployed on Hugging Face Spaces]()\n- [README.md rendered by GitHub]()\n\nThe README.md and space both have the following features:\n\n- A description.\n- Installation instructions.\n- A fully functioning code snippet.\n- Optional links to PyPi, GitHub, and Hugging Face Spaces.\n- API documentation including:\n  - An argument table for component initialisation showing types, defaults, and descriptions.\n  - A description of how the component affects the user's predict function.\n  - A table of events and their descriptions.\n  - Any additional interfaces or classes that may be used during initialisation or in the pre- or post- processors.\n\nAdditionally, the Gradio includes:\n\n- A live demo.\n- A richer, interactive version of the parameter tables.\n- Nicer styling!\n\n", "heading1": "What gets generated?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "The documentation generator uses existing standards to extract the necessary information, namely Type Hints and Docstrings. There are no Gradio-specific APIs for documentation, so following best practices will generally yield the best results.\n\nIf you already use type hints and docstrings in your component source code, you don't need to do much to benefit from this feature, but there are some details that you should be aware of.\n\nPython version\n\nTo get the best documentation experience, you need to use Python `3.10` or greater when generating documentation. This is because some introspection features used to generate the documentation were only added in `3.10`.\n\nType hints\n\nPython type hints are used extensively to provide helpful information for users. \n\n<details> \n<summary> What are type hints?</summary>\n\n\nIf you need to become more familiar with type hints in Python, they are a simple way to express what Python types are expected for arguments and return values of functions and methods. They provide a helpful in-editor experience, aid in maintenance, and integrate with various other tools. These types can be simple primitives, like `list` `str` `bool`; they could be more compound types like `list[str]`, `str | None` or `tuple[str, float | int]`; or they can be more complex types using utility classed like [`TypedDict`](https://peps.python.org/pep-0589/abstract).\n\n[Read more about type hints in Python.](https://realpython.com/lessons/type-hinting/)\n\n\n</details>\n\nWhat do I need to add hints to?\n\nYou do not need to add type hints to every part of your code. For the documentation to work correctly, you will need to add type hints to the following component methods:\n\n- `__init__` parameters should be typed.\n- `postprocess` parameters and return value should be typed.\n- `preprocess` parameters and return value should be typed.\n\nIf you are using `gradio cc create`, these types should already exist, but you may need to tweak them based on any changes you ma", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "be typed.\n- `preprocess` parameters and return value should be typed.\n\nIf you are using `gradio cc create`, these types should already exist, but you may need to tweak them based on any changes you make.\n\n`__init__`\n\nHere, you only need to type the parameters. If you have cloned a template with `gradio` cc create`, these should already be in place. You will only need to add new hints for anything you have added or changed:\n\n```py\ndef __init__(\n  self,\n  value: str | None = None,\n  *,\n  sources: Literal[\"upload\", \"microphone\"] = \"upload,\n  every: Timer | float | None = None,\n  ...\n):\n  ...\n```\n\n`preprocess` and `postprocess`\n\nThe `preprocess` and `postprocess` methods determine the value passed to the user function and the value that needs to be returned.\n\nEven if the design of your component is primarily as an input or an output, it is worth adding type hints to both the input parameters and the return values because Gradio has no way of limiting how components can be used.\n\nIn this case, we specifically care about:\n\n- The return type of `preprocess`.\n- The input type of `postprocess`.\n\n```py\ndef preprocess(\n  self, payload: FileData | None input is optional\n) -> tuple[int, str] | str | None:\n\nuser function input  is the preprocess return \u25b2\nuser function output is the postprocess input \u25bc\n\ndef postprocess(\n  self, value: tuple[int, str] | None\n) -> FileData | bytes | None: return is optional\n  ...\n```\n\nDocstrings\n\nDocstrings are also used extensively to extract more meaningful, human-readable descriptions of certain parts of the API.\n\n<details> \n<summary> What are docstrings?</summary>\n\n\nIf you need to become more familiar with docstrings in Python, they are a way to annotate parts of your code with human-readable decisions and explanations. They offer a rich in-editor experience like type hints, but unlike type hints, they don't have any specific syntax requirements. They are simple strings and can take almost any form. The only requirement i", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "offer a rich in-editor experience like type hints, but unlike type hints, they don't have any specific syntax requirements. They are simple strings and can take almost any form. The only requirement is where they appear. Docstrings should be \"a string literal that occurs as the first statement in a module, function, class, or method definition\".\n\n[Read more about Python docstrings.](https://peps.python.org/pep-0257/what-is-a-docstring)\n\n</details>\n\nWhile docstrings don't have any syntax requirements, we need a particular structure for documentation purposes.\n\nAs with type hint, the specific information we care about is as follows:\n\n- `__init__` parameter docstrings.\n- `preprocess` return docstrings.\n- `postprocess` input parameter docstrings.\n\nEverything else is optional.\n\nDocstrings should always take this format to be picked up by the documentation generator:\n\nClasses\n\n```py\n\"\"\"\nA description of the class.\n\nThis can span multiple lines and can _contain_ *markdown*.\n\"\"\"\n```\n\nMethods and functions \n\nMarkdown in these descriptions will not be converted into formatted text.\n\n```py\n\"\"\"\nParameters:\n    param_one: A description for this parameter.\n    param_two: A description for this parameter.\nReturns:\n    A description for this return value.\n\"\"\"\n```\n\nEvents\n\nIn custom components, events are expressed as a list stored on the `events` field of the component class. While we do not need types for events, we _do_ need a human-readable description so users can understand the behaviour of the event.\n\nTo facilitate this, we must create the event in a specific way.\n\nThere are two ways to add events to a custom component.\n\nBuilt-in events\n\nGradio comes with a variety of built-in events that may be enough for your component. If you are using built-in events, you do not need to do anything as they already have descriptions we can extract:\n\n```py\nfrom gradio.events import Events\n\nclass ParamViewer(Component):\n  ...\n\n  EVENTS = [\n    Events.change,\n    Events.up", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "do not need to do anything as they already have descriptions we can extract:\n\n```py\nfrom gradio.events import Events\n\nclass ParamViewer(Component):\n  ...\n\n  EVENTS = [\n    Events.change,\n    Events.upload,\n  ]\n```\n\nCustom events\n\nYou can define a custom event if the built-in events are unsuitable for your use case. This is a straightforward process, but you must create the event in this way for docstrings to work correctly:\n\n```py\nfrom gradio.events import Events, EventListener\n\nclass ParamViewer(Component):\n  ...\n\n  EVENTS = [\n    Events.change,\n    EventListener(\n        \"bingbong\",\n        doc=\"This listener is triggered when the user does a bingbong.\"\n      )\n  ]\n```\n\nDemo\n\nThe `demo/app.py`, often used for developing the component, generates the live demo and code snippet. The only strict rule here is that the `demo.launch()` command must be contained with a `__name__ == \"__main__\"` conditional as below:\n\n```py\nif __name__ == \"__main__\":\n  demo.launch()\n```\n\nThe documentation generator will scan for such a clause and error if absent. If you are _not_ launching the demo inside the `demo/app.py`, then you can pass `--suppress-demo-check` to turn off this check.\n\nDemo recommendations\n\nAlthough there are no additional rules, there are some best practices you should bear in mind to get the best experience from the documentation generator.\n\nThese are only guidelines, and every situation is unique, but they are sound principles to remember.\n\nKeep the demo compact\n\nCompact demos look better and make it easier for users to understand what the demo does. Try to remove as many extraneous UI elements as possible to focus the users' attention on the core use case. \n\nSometimes, it might make sense to have a `demo/app.py` just for the docs and an additional, more complex app for your testing purposes. You can also create other spaces, showcasing more complex examples and linking to them from the main class docstring or the `pyproject.toml` description.\n\n", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "ore complex app for your testing purposes. You can also create other spaces, showcasing more complex examples and linking to them from the main class docstring or the `pyproject.toml` description.\n\nKeep the code concise\n\nThe 'getting started' snippet utilises the demo code, which should be as short as possible to keep users engaged and avoid confusion.\n\nIt isn't the job of the sample snippet to demonstrate the whole API; this snippet should be the shortest path to success for a new user. It should be easy to type or copy-paste and easy to understand. Explanatory comments should be brief and to the point.\n\nAvoid external dependencies\n\nAs mentioned above, users should be able to copy-paste a snippet and have a fully working app. Try to avoid third-party library dependencies to facilitate this.\n\nYou should carefully consider any examples; avoiding examples that require additional files or that make assumptions about the environment is generally a good idea.\n\nEnsure the `demo` directory is self-contained\n\nOnly the `demo` directory will be uploaded to Hugging Face spaces in certain instances, as the component will be installed via PyPi if possible. It is essential that this directory is self-contained and any files needed for the correct running of the demo are present.\n\nAdditional URLs\n\nThe documentation generator will generate a few buttons, providing helpful information and links to users. They are obtained automatically in some cases, but some need to be explicitly included in the `pyproject.yaml`. \n\n- PyPi Version and link - This is generated automatically.\n- GitHub Repository - This is populated via the `pyproject.toml`'s `project.urls.repository`.\n- Hugging Face Space - This is populated via the `pyproject.toml`'s `project.urls.space`.\n\nAn example `pyproject.toml` urls section might look like this:\n\n```toml\n[project.urls]\nrepository = \"https://github.com/user/repo-name\"\nspace = \"https://huggingface.co/spaces/user/space-name\"\n```", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "pyproject.toml` urls section might look like this:\n\n```toml\n[project.urls]\nrepository = \"https://github.com/user/repo-name\"\nspace = \"https://huggingface.co/spaces/user/space-name\"\n```", "heading1": "What do I need to do?", "source_page_url": "https://gradio.app/guides/documenting-custom-components", "source_page_title": "Custom Components - Documenting Custom Components Guide"}, {"text": "By default, all custom component packages are called `gradio_<component-name>` where `component-name` is the name of the component's python class in lowercase.\n\nAs an example, let's walkthrough changing the name of a component from `gradio_mytextbox` to `supertextbox`. \n\n1. Modify the `name` in the `pyproject.toml` file. \n\n```bash\n[project]\nname = \"supertextbox\"\n```\n\n2. Change all occurrences of `gradio_<component-name>` in `pyproject.toml` to `<component-name>`\n\n```bash\n[tool.hatch.build]\nartifacts = [\"/backend/supertextbox/templates\", \"*.pyi\"]\n\n[tool.hatch.build.targets.wheel]\npackages = [\"/backend/supertextbox\"]\n```\n\n3. Rename the `gradio_<component-name>` directory in `backend/` to `<component-name>`\n\n```bash\nmv backend/gradio_mytextbox backend/supertextbox\n```\n\n\nTip: Remember to change the import statement in `demo/app.py`!\n\n", "heading1": "The Package Name", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "By default, only the custom component python class is a top level export. \nThis means that when users type `from gradio_<component-name> import ...`, the only class that will be available is the custom component class.\nTo add more classes as top level exports, modify the `__all__` property in `__init__.py`\n\n```python\nfrom .mytextbox import MyTextbox\nfrom .mytextbox import AdditionalClass, additional_function\n\n__all__ = ['MyTextbox', 'AdditionalClass', 'additional_function']\n```\n\n", "heading1": "Top Level Python Exports", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "You can add python dependencies by modifying the `dependencies` key in `pyproject.toml`\n\n```bash\ndependencies = [\"gradio\", \"numpy\", \"PIL\"]\n```\n\n\nTip: Remember to run `gradio cc install` when you add dependencies!\n\n", "heading1": "Python Dependencies", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "You can add JavaScript dependencies by modifying the `\"dependencies\"` key in `frontend/package.json`\n\n```json\n\"dependencies\": {\n    \"@gradio/atoms\": \"0.2.0-beta.4\",\n    \"@gradio/statustracker\": \"0.3.0-beta.6\",\n    \"@gradio/utils\": \"0.2.0-beta.4\",\n    \"your-npm-package\": \"<version>\"\n}\n```\n\n", "heading1": "Javascript Dependencies", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "By default, the CLI will place the Python code in `backend` and the JavaScript code in `frontend`.\nIt is not recommended to change this structure since it makes it easy for a potential contributor to look at your source code and know where everything is.\nHowever, if you did want to this is what you would have to do:\n\n1. Place the Python code in the subdirectory of your choosing. Remember to modify the `[tool.hatch.build]` `[tool.hatch.build.targets.wheel]` in the `pyproject.toml` to match!\n\n2. Place the JavaScript code in the subdirectory of your choosing.\n\n2. Add the `FRONTEND_DIR` property on the component python class. It must be the relative path from the file where the class is defined to the location of the JavaScript directory.\n\n```python\nclass SuperTextbox(Component):\n    FRONTEND_DIR = \"../../frontend/\"\n```\n\nThe JavaScript and Python directories must be under the same common directory!\n\n", "heading1": "Directory Structure", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "Sticking to the defaults will make it easy for others to understand and contribute to your custom component.\nAfter all, the beauty of open source is that anyone can help improve your code!\nBut if you ever need to deviate from the defaults, you know how!", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/configuration", "source_page_title": "Custom Components - Configuration Guide"}, {"text": "For this demo we will be tweaking the existing Gradio `Chatbot` component to display text and media files in the same message.\nLet's create a new custom component directory by templating off of the `Chatbot` component source code.\n\n```bash\ngradio cc create MultimodalChatbot --template Chatbot\n```\n\nAnd we're ready to go!\n\nTip: Make sure to modify the `Author` key in the `pyproject.toml` file.\n\n", "heading1": "Part 1 - Creating our project", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "Open up the `multimodalchatbot.py` file in your favorite code editor and let's get started modifying the backend of our component.\n\nThe first thing we will do is create the `data_model` of our component.\nThe `data_model` is the data format that your python component will receive and send to the javascript client running the UI.\nYou can read more about the `data_model` in the [backend guide](./backend).\n\nFor our component, each chatbot message will consist of two keys: a `text` key that displays the text message and an optional list of media files that can be displayed underneath the text.\n\nImport the `FileData` and `GradioModel` classes from `gradio.data_classes` and modify the existing `ChatbotData` class to look like the following:\n\n```python\nclass FileMessage(GradioModel):\n    file: FileData\n    alt_text: Optional[str] = None\n\n\nclass MultimodalMessage(GradioModel):\n    text: Optional[str] = None\n    files: Optional[List[FileMessage]] = None\n\n\nclass ChatbotData(GradioRootModel):\n    root: List[Tuple[Optional[MultimodalMessage], Optional[MultimodalMessage]]]\n\n\nclass MultimodalChatbot(Component):\n    ...\n    data_model = ChatbotData\n```\n\n\nTip: The `data_model`s are implemented using `Pydantic V2`. Read the documentation [here](https://docs.pydantic.dev/latest/).\n\nWe've done the hardest part already!\n\n", "heading1": "Part 2a - The backend data_model", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "For the `preprocess` method, we will keep it simple and pass a list of `MultimodalMessage`s to the python functions that use this component as input. \nThis will let users of our component access the chatbot data with `.text` and `.files` attributes.\nThis is a design choice that you can modify in your implementation!\nWe can return the list of messages with the `root` property of the `ChatbotData` like so:\n\n```python\ndef preprocess(\n    self,\n    payload: ChatbotData | None,\n) -> List[MultimodalMessage] | None:\n    if payload is None:\n        return payload\n    return payload.root\n```\n\n\nTip: Learn about the reasoning behind the `preprocess` and `postprocess` methods in the [key concepts guide](./key-component-concepts)\n\nIn the `postprocess` method we will coerce each message returned by the python function to be a `MultimodalMessage` class. \nWe will also clean up any indentation in the `text` field so that it can be properly displayed as markdown in the frontend.\n\nWe can leave the `postprocess` method as is and modify the `_postprocess_chat_messages`\n\n```python\ndef _postprocess_chat_messages(\n    self, chat_message: MultimodalMessage | dict | None\n) -> MultimodalMessage | None:\n    if chat_message is None:\n        return None\n    if isinstance(chat_message, dict):\n        chat_message = MultimodalMessage(**chat_message)\n    chat_message.text = inspect.cleandoc(chat_message.text or \"\")\n    for file_ in chat_message.files:\n        file_.file.mime_type = client_utils.get_mimetype(file_.file.path)\n    return chat_message\n```\n\nBefore we wrap up with the backend code, let's modify the `example_value` and `example_payload` method to return a valid dictionary representation of the `ChatbotData`:\n\n```python\ndef example_value(self) -> Any:\n    return [[{\"text\": \"Hello!\", \"files\": []}, None]]\n\ndef example_payload(self) -> Any:\n    return [[{\"text\": \"Hello!\", \"files\": []}, None]]\n```\n\nCongrats - the backend is complete!\n\n", "heading1": "Part 2b - The pre and postprocess methods", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "The frontend for the `Chatbot` component is divided into two parts - the `Index.svelte` file and the `shared/Chatbot.svelte` file.\nThe `Index.svelte` file applies some processing to the data received from the server and then delegates the rendering of the conversation to the `shared/Chatbot.svelte` file.\nFirst we will modify the `Index.svelte` file to apply processing to the new data type the backend will return.\n\nLet's begin by porting our custom types  from our python `data_model` to typescript.\nOpen `frontend/shared/utils.ts` and add the following type definitions at the top of the file:\n\n```ts\nexport type FileMessage = {\n\tfile: FileData;\n\talt_text?: string;\n};\n\n\nexport type MultimodalMessage = {\n\ttext: string;\n\tfiles?: FileMessage[];\n}\n```\n\nNow let's import them in `Index.svelte` and modify the type annotations for `value` and `_value`.\n\n```ts\nimport type { FileMessage, MultimodalMessage } from \"./shared/utils\";\n\nexport let value: [\n    MultimodalMessage | null,\n    MultimodalMessage | null\n][] = [];\n\nlet _value: [\n    MultimodalMessage | null,\n    MultimodalMessage | null\n][];\n```\n\nWe need to normalize each message to make sure each file has a proper URL to fetch its contents from.\nWe also need to format any embedded file links in the `text` key.\nLet's add a `process_message` utility function and apply it whenever the `value` changes.\n\n```ts\nfunction process_message(msg: MultimodalMessage | null): MultimodalMessage | null {\n    if (msg === null) {\n        return msg;\n    }\n    msg.text = redirect_src_url(msg.text);\n    msg.files = msg.files.map(normalize_messages);\n    return msg;\n}\n\n$: _value = value\n    ? value.map(([user_msg, bot_msg]) => [\n            process_message(user_msg),\n            process_message(bot_msg)\n        ])\n    : [];\n```\n\n", "heading1": "Part 3a - The Index.svelte file", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "Let's begin similarly to the `Index.svelte` file and let's first modify the type annotations.\nImport `Mulimodal` message at the top of the `<script>` section and use it to type the `value` and `old_value` variables.\n\n```ts\nimport type { MultimodalMessage } from \"./utils\";\n\nexport let value:\n    | [\n            MultimodalMessage | null,\n            MultimodalMessage | null\n        ][]\n    | null;\nlet old_value:\n    | [\n            MultimodalMessage | null,\n            MultimodalMessage | null\n        ][]\n    | null = null;\n```\n\nWe also need to modify the `handle_select` and `handle_like` functions:\n\n```ts\nfunction handle_select(\n    i: number,\n    j: number,\n    message: MultimodalMessage | null\n): void {\n    dispatch(\"select\", {\n        index: [i, j],\n        value: message\n    });\n}\n\nfunction handle_like(\n    i: number,\n    j: number,\n    message: MultimodalMessage | null,\n    liked: boolean\n): void {\n    dispatch(\"like\", {\n        index: [i, j],\n        value: message,\n        liked: liked\n    });\n}\n```\n\nNow for the fun part, actually rendering the text and files in the same message!\n\nYou should see some code like the following that determines whether a file or a markdown message should be displayed depending on the type of the message:\n\n```svelte\n{if typeof message === \"string\"}\n    <Markdown\n        {message}\n        {latex_delimiters}\n        {sanitize_html}\n        {render_markdown}\n        {line_breaks}\n        on:load={scroll}\n    />\n{:else if message !== null && message.file?.mime_type?.includes(\"audio\")}\n    <audio\n        data-testid=\"chatbot-audio\"\n        controls\n        preload=\"metadata\"\n        ...\n```\n\nWe will modify this code to always display the text message and then loop through the files and display all of them that are present:\n\n```svelte\n<Markdown\n    message={message.text}\n    {latex_delimiters}\n    {sanitize_html}\n    {render_markdown}\n    {line_breaks}\n    on:load={scroll}\n/>\n{each message.files as file, k}\n    {if file !== null && fil", "heading1": "Part 3b - the Chatbot.svelte file", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "own\n    message={message.text}\n    {latex_delimiters}\n    {sanitize_html}\n    {render_markdown}\n    {line_breaks}\n    on:load={scroll}\n/>\n{each message.files as file, k}\n    {if file !== null && file.file.mime_type?.includes(\"audio\")}\n        <audio\n            data-testid=\"chatbot-audio\"\n            controls\n            preload=\"metadata\"\n            src={file.file?.url}\n            title={file.alt_text}\n            on:play\n            on:pause\n            on:ended\n        />\n    {:else if message !== null && file.file?.mime_type?.includes(\"video\")}\n        <video\n            data-testid=\"chatbot-video\"\n            controls\n            src={file.file?.url}\n            title={file.alt_text}\n            preload=\"auto\"\n            on:play\n            on:pause\n            on:ended\n        >\n            <track kind=\"captions\" />\n        </video>\n    {:else if message !== null && file.file?.mime_type?.includes(\"image\")}\n        <img\n            data-testid=\"chatbot-image\"\n            src={file.file?.url}\n            alt={file.alt_text}\n        />\n    {:else if message !== null && file.file?.url !== null}\n        <a\n            data-testid=\"chatbot-file\"\n            href={file.file?.url}\n            target=\"_blank\"\n            download={window.__is_colab__\n                ? null\n                : file.file?.orig_name || file.file?.path}\n        >\n            {file.file?.orig_name || file.file?.path}\n        </a>\n    {:else if pending_message && j === 1}\n        <Pending {layout} />\n    {/if}\n{/each}\n```\n\nWe did it! \ud83c\udf89\n\n", "heading1": "Part 3b - the Chatbot.svelte file", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "For this tutorial, let's keep the demo simple and just display a static conversation between a hypothetical user and a bot.\nThis demo will show how both the user and the bot can send files. \nIn part 2 of this tutorial series we will build a fully functional chatbot demo!\n\nThe demo code will look like the following:\n\n```python\nimport gradio as gr\nfrom gradio_multimodalchatbot import MultimodalChatbot\nfrom gradio.data_classes import FileData\n\nuser_msg1 = {\"text\": \"Hello, what is in this image?\",\n             \"files\": [{\"file\": FileData(path=\"https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg\")}]\n             }\nbot_msg1 = {\"text\": \"It is a very cute dog\",\n            \"files\": []}\n\nuser_msg2 = {\"text\": \"Describe this audio clip please.\",\n             \"files\": [{\"file\": FileData(path=\"cantina.wav\")}]}\nbot_msg2 = {\"text\": \"It is the cantina song from Star Wars\",\n            \"files\": []}\n\nuser_msg3 = {\"text\": \"Give me a video clip please.\",\n             \"files\": []}\nbot_msg3 = {\"text\": \"Here is a video clip of the world\",\n            \"files\": [{\"file\": FileData(path=\"world.mp4\")},\n                      {\"file\": FileData(path=\"cantina.wav\")}]}\n\nconversation = [[user_msg1, bot_msg1], [user_msg2, bot_msg2], [user_msg3, bot_msg3]]\n\nwith gr.Blocks() as demo:\n    MultimodalChatbot(value=conversation, height=800)\n\n\ndemo.launch()\n```\n\n\nTip: Change the filepaths so that they correspond to files on your machine. Also, if you are running in development mode, make sure the files are located in the top level of your custom component directory.\n\n", "heading1": "Part 4 - The demo", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "Let's build and deploy our demo with `gradio cc build` and `gradio cc deploy`!\n\nYou can check out our component deployed to [HuggingFace Spaces](https://huggingface.co/spaces/freddyaboulton/gradio_multimodalchatbot) and all of the source code is available [here](https://huggingface.co/spaces/freddyaboulton/gradio_multimodalchatbot/tree/main/src).\n\nSee you in the next installment of this series!", "heading1": "Part 5 - Deploying and Conclusion", "source_page_url": "https://gradio.app/guides/multimodal-chatbot-part1", "source_page_title": "Custom Components - Multimodal Chatbot Part1 Guide"}, {"text": "Before using Custom Components, make sure you have Python 3.10+, Node.js v18+, npm 9+, and Gradio 4.0+ (preferably Gradio 5.0+) installed.\n\n", "heading1": "What do I need to install before using Custom Components?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "Custom components built with Gradio 5.0 should be compatible with Gradio 4.0. If you built your custom component in Gradio 4.0 you will have to rebuild your component to be compatible with Gradio 5.0. Simply follow these steps:\n1. Update the `@gradio/preview` package. `cd` into the `frontend` directory and run `npm update`.\n2.  Modify the `dependencies` key in `pyproject.toml` to pin the maximum allowed Gradio version at version 5, e.g. `dependencies = [\"gradio>=4.0,<6.0\"]`.\n3. Run the build and publish commands\n\n", "heading1": "Are custom components compatible between Gradio 4.0 and 5.0?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "Run `gradio cc show` to see the list of built-in templates.\nYou can also start off from other's custom components!\nSimply `git clone` their repository and make your modifications.\n\n", "heading1": "What templates can I use to create my custom component?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "When you run `gradio cc dev`, a development server will load and run a Gradio app of your choosing.\nThis is like when you run `python <app-file>.py`, however the `gradio` command will hot reload so you can instantly see your changes. \n\n", "heading1": "What is the development server?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "**1. Check your terminal and browser console**\n\nMake sure there are no syntax errors or other obvious problems in your code. Exceptions triggered from python will be displayed in the terminal. Exceptions from javascript will be displayed in the browser console and/or the terminal.\n\n**2. Are you developing on Windows?**\n\nChrome on Windows will block the local compiled svelte files for security reasons. We recommend developing your custom component in the windows subsystem for linux (WSL) while the team looks at this issue.\n\n**3. Inspect the window.__GRADIO_CC__ variable**\n\nIn the browser console, print the `window.__GRADIO__CC` variable (just type it into the console). If it is an empty object, that means\nthat the CLI could not find your custom component source code. Typically, this happens when the custom component is installed in a different virtual environment than the one used to run the dev command. Please use the `--python-path` and `gradio-path` CLI arguments to specify the path of the python and gradio executables for the environment your component is installed in. For example, if you are using a virtualenv located at `/Users/mary/venv`, pass in `/Users/mary/bin/python` and `/Users/mary/bin/gradio` respectively.\n\nIf the `window.__GRADIO__CC` variable is not empty (see below for an example), then the dev server should be working correctly. \n\n![](https://gradio-builds.s3.amazonaws.com/demo-files/gradio_CC_DEV.png)\n\n**4. Make sure you are using a virtual environment**\nIt is highly recommended you use a virtual environment to prevent conflicts with other python dependencies installed in your system.\n\n\n", "heading1": "The development server didn't work for me", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "No! You can start off from an existing gradio component as a template, see the [five minute guide](./custom-components-in-five-minutes).\nYou can also start from an existing custom component if you'd like to tweak it further. Once you find the source code of a custom component you like, clone the code to your computer and run `gradio cc install`. Then you can run the development server to make changes.If you run into any issues, contact the author of the component by opening an issue in their repository. The [gallery](https://www.gradio.app/custom-components/gallery) is a good place to look for published components. For example, to start from the [PDF component](https://www.gradio.app/custom-components/gallery?id=freddyaboulton%2Fgradio_pdf), clone the space with `git clone https://huggingface.co/spaces/freddyaboulton/gradio_pdf`, `cd` into the `src` directory, and run `gradio cc install`.\n\n\n", "heading1": "Do I always need to start my component from scratch?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "You can develop and build your custom component without hosting or connecting to HuggingFace.\nIf you would like to share your component with the gradio community, it is recommended to publish your package to PyPi and host a demo on HuggingFace so that anyone can install it or try it out.\n\n", "heading1": "Do I need to host my custom component on HuggingFace Spaces?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "You must implement the `preprocess`, `postprocess`, `example_payload`, and `example_value` methods. If your component does not use a data model, you must also define the `api_info`, `flag`, and `read_from_flag` methods. Read more in the [backend guide](./backend).\n\n", "heading1": "What methods are mandatory for implementing a custom component in Gradio?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "A `data_model` defines the expected data format for your component, simplifying the component development process and self-documenting your code. It streamlines API usage and example caching.\n\n", "heading1": "What is the purpose of a `data_model` in Gradio custom components?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "Utilizing `FileData` is crucial for components that expect file uploads. It ensures secure file handling, automatic caching, and streamlined client library functionality.\n\n", "heading1": "Why is it important to use `FileData` for components dealing with file uploads?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "You can define event triggers in the `EVENTS` class attribute by listing the desired event names, which automatically adds corresponding methods to your component.\n\n", "heading1": "How can I add event triggers to my custom Gradio component?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "Yes, it is possible to create custom components without a `data_model`, but you are going to have to manually implement `api_info`, `flag`, and `read_from_flag` methods.\n\n", "heading1": "Can I implement a custom Gradio component without defining a `data_model`?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "We have prepared this [collection](https://huggingface.co/collections/gradio/custom-components-65497a761c5192d981710b12) of custom components on the HuggingFace Hub that you can use to get started!\n\n", "heading1": "Are there sample custom components I can learn from?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "We're working on creating a gallery to make it really easy to discover new custom components.\nIn the meantime, you can search for HuggingFace Spaces that are tagged as a `gradio-custom-component` [here](https://huggingface.co/search/full-text?q=gradio-custom-component&type=space)", "heading1": "How can I find custom components created by the Gradio community?", "source_page_url": "https://gradio.app/guides/frequently-asked-questions", "source_page_title": "Custom Components - Frequently Asked Questions Guide"}, {"text": "All components inherit from one of three classes `Component`, `FormComponent`, or `BlockContext`.\nYou need to inherit from one so that your component behaves like all other gradio components.\nWhen you start from a template with `gradio cc create --template`, you don't need to worry about which one to choose since the template uses the correct one. \nFor completeness, and in the event that you need to make your own component from scratch, we explain what each class is for.\n\n* `FormComponent`: Use this when you want your component to be grouped together in the same `Form` layout with other `FormComponents`. The `Slider`, `Textbox`, and `Number` components are all `FormComponents`.\n* `BlockContext`: Use this when you want to place other components \"inside\" your component. This enabled `with MyComponent() as component:` syntax.\n* `Component`: Use this for all other cases.\n\nTip: If your component supports streaming output, inherit from the `StreamingOutput` class.\n\nTip: If you inherit from `BlockContext`, you also need to set the metaclass to be `ComponentMeta`. See example below.\n\n```python\nfrom gradio.blocks import BlockContext\nfrom gradio.component_meta import ComponentMeta\n\n\n\n\n@document()\nclass Row(BlockContext, metaclass=ComponentMeta):\n    pass\n```\n\n", "heading1": "Which Class to Inherit From", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "When you inherit from any of these classes, the following methods must be implemented.\nOtherwise the Python interpreter will raise an error when you instantiate your component!\n\n`preprocess` and `postprocess`\n\nExplained in the [Key Concepts](./key-component-conceptsthe-value-and-how-it-is-preprocessed-postprocessed) guide. \nThey handle the conversion from the data sent by the frontend to the format expected by the python function.\n\n```python\n    def preprocess(self, x: Any) -> Any:\n        \"\"\"\n        Convert from the web-friendly (typically JSON) value in the frontend to the format expected by the python function.\n        \"\"\"\n        return x\n\n    def postprocess(self, y):\n        \"\"\"\n        Convert from the data returned by the python function to the web-friendly (typically JSON) value expected by the frontend.\n        \"\"\"\n        return y\n```\n\n`process_example`\n\nTakes in the original Python value and returns the modified value that should be displayed in the examples preview in the app. \nIf not provided, the `.postprocess()` method is used instead. Let's look at the following example from the `SimpleDropdown` component.\n\n```python\ndef process_example(self, input_data):\n    return next((c[0] for c in self.choices if c[1] == input_data), None)\n```\n\nSince `self.choices` is a list of tuples corresponding to (`display_name`, `value`), this converts the value that a user provides to the display value (or if the value is not present in `self.choices`, it is converted to `None`).\n\n\n`api_info`\n\nA JSON-schema representation of the value that the `preprocess` expects. \nThis powers api usage via the gradio clients. \nYou do **not** need to implement this yourself if you components specifies a `data_model`. \nThe `data_model` in the following section.\n\n```python\ndef api_info(self) -> dict[str, list[str]]:\n    \"\"\"\n    A JSON-schema representation of the value that the `preprocess` expects and the `postprocess` returns.\n    \"\"\"\n    pass\n```\n\n`example_payload`\n\nA", "heading1": "The methods you need to implement", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "pi_info(self) -> dict[str, list[str]]:\n    \"\"\"\n    A JSON-schema representation of the value that the `preprocess` expects and the `postprocess` returns.\n    \"\"\"\n    pass\n```\n\n`example_payload`\n\nAn example payload for your component, e.g. something that can be passed into the `.preprocess()` method\nof your component. The example input is displayed in the `View API` page of a Gradio app that uses your custom component. \nMust be JSON-serializable. If your component expects a file, it is best to use a publicly accessible URL.\n\n```python\ndef example_payload(self) -> Any:\n    \"\"\"\n    The example inputs for this component for API usage. Must be JSON-serializable.\n    \"\"\"\n    pass\n```\n\n`example_value`\n\nAn example value for your component, e.g. something that can be passed into the `.postprocess()` method\nof your component. This is used as the example value in the default app that is created in custom component development.\n\n```python\ndef example_payload(self) -> Any:\n    \"\"\"\n    The example inputs for this component for API usage. Must be JSON-serializable.\n    \"\"\"\n    pass\n```\n\n`flag`\n\nWrite the component's value to a format that can be stored in the `csv` or `json` file used for flagging.\nYou do **not** need to implement this yourself if you components specifies a `data_model`. \nThe `data_model` in the following section.\n\n```python\ndef flag(self, x: Any | GradioDataModel, flag_dir: str | Path = \"\") -> str:\n    pass\n```\n\n`read_from_flag`\nConvert from the format stored in the `csv` or `json` file used for flagging to the component's python `value`.\nYou do **not** need to implement this yourself if you components specifies a `data_model`. \nThe `data_model` in the following section.\n\n```python\ndef read_from_flag(\n    self,\n    x: Any,\n) -> GradioDataModel | Any:\n    \"\"\"\n    Convert the data from the csv or jsonl file into the component state.\n    \"\"\"\n    return x\n```\n\n", "heading1": "The methods you need to implement", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "    \"\"\"\n    Convert the data from the csv or jsonl file into the component state.\n    \"\"\"\n    return x\n```\n\n", "heading1": "The methods you need to implement", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "The `data_model` is how you define the expected data format your component's value will be stored in the frontend.\nIt specifies the data format your `preprocess` method expects and the format the `postprocess` method returns.\nIt is not necessary to define a `data_model` for your component but it greatly simplifies the process of creating a custom component.\nIf you define a custom component you only need to implement four methods - `preprocess`, `postprocess`, `example_payload`, and `example_value`!\n\nYou define a `data_model` by defining a [pydantic model](https://docs.pydantic.dev/latest/concepts/models/basic-model-usage) that inherits from either `GradioModel` or `GradioRootModel`.\n\nThis is best explained with an example. Let's look at the core `Video` component, which stores the video data as a JSON object with two keys `video` and `subtitles` which point to separate files.\n\n```python\nfrom gradio.data_classes import FileData, GradioModel\n\nclass VideoData(GradioModel):\n    video: FileData\n    subtitles: Optional[FileData] = None\n\nclass Video(Component):\n    data_model = VideoData\n```\n\nBy adding these four lines of code, your component automatically implements the methods needed for API usage, the flagging methods, and example caching methods!\nIt also has the added benefit of self-documenting your code.\nAnyone who reads your component code will know exactly the data it expects.\n\nTip: If your component expects files to be uploaded from the frontend, your must use the `FileData` model! It will be explained in the following section. \n\nTip: Read the pydantic docs [here](https://docs.pydantic.dev/latest/concepts/models/basic-model-usage).\n\nThe difference between a `GradioModel` and a `GradioRootModel` is that the `RootModel` will not serialize the data to a dictionary.\nFor example, the `Names` model will serialize the data to `{'names': ['freddy', 'pete']}` whereas the `NamesRoot` model will serialize it to `['freddy', 'pete']`.\n\n```python\nfrom typing import List\n\nclas", "heading1": "The `data_model`", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": " example, the `Names` model will serialize the data to `{'names': ['freddy', 'pete']}` whereas the `NamesRoot` model will serialize it to `['freddy', 'pete']`.\n\n```python\nfrom typing import List\n\nclass Names(GradioModel):\n    names: List[str]\n\nclass NamesRoot(GradioRootModel):\n    root: List[str]\n```\n\nEven if your component does not expect a \"complex\" JSON data structure it can be beneficial to define a `GradioRootModel` so that you don't have to worry about implementing the API and flagging methods.\n\nTip: Use classes from the Python typing library to type your models. e.g. `List` instead of `list`.\n\n", "heading1": "The `data_model`", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "If your component expects uploaded files as input, or returns saved files to the frontend, you **MUST** use the `FileData` to type the files in your `data_model`.\n\nWhen you use the `FileData`:\n\n* Gradio knows that it should allow serving this file to the frontend. Gradio automatically blocks requests to serve arbitrary files in the computer running the server.\n\n* Gradio will automatically place the file in a cache so that duplicate copies of the file don't get saved.\n\n* The client libraries will automatically know that they should upload input files prior to sending the request. They will also automatically download files.\n\nIf you do not use the `FileData`, your component will not work as expected!\n\n\n", "heading1": "Handling Files", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "The events triggers for your component are defined in the `EVENTS` class attribute.\nThis is a list that contains the string names of the events.\nAdding an event to this list will automatically add a method with that same name to your component!\n\nYou can import the `Events` enum from `gradio.events` to access commonly used events in the core gradio components.\n\nFor example, the following code will define `text_submit`, `file_upload` and `change` methods in the `MyComponent` class.\n\n```python\nfrom gradio.events import Events\nfrom gradio.components import FormComponent\n\nclass MyComponent(FormComponent):\n\n    EVENTS = [\n        \"text_submit\",\n        \"file_upload\",\n        Events.change\n    ]\n```\n\n\nTip: Don't forget to also handle these events in the JavaScript code!\n\n", "heading1": "Adding Event Triggers To Your Component", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/backend", "source_page_title": "Custom Components - Backend Guide"}, {"text": "Plots accept a pandas Dataframe as their value. The plot also takes `x` and `y` which represent the names of the columns that represent the x and y axes respectively. Here's a simple example:\n\n$code_plot_guide_line\n$demo_plot_guide_line\n\nAll plots have the same API, so you could swap this out with a `gr.ScatterPlot`:\n\n$code_plot_guide_scatter\n$demo_plot_guide_scatter\n\nThe y axis column in the dataframe should have a numeric type, but the x axis column can be anything from strings, numbers, categories, or datetimes.\n\n$code_plot_guide_scatter_nominal\n$demo_plot_guide_scatter_nominal\n\n", "heading1": "Creating a Plot with a pd.Dataframe", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can break out your plot into series using the `color` argument.\n\n$code_plot_guide_series_nominal\n$demo_plot_guide_series_nominal\n\nIf you wish to assign series specific colors, use the `color_map` arg, e.g. `gr.ScatterPlot(..., color_map={'white': 'FF9988', 'asian': '88EEAA', 'black': '333388'})`\n\nThe color column can be numeric type as well.\n\n$code_plot_guide_series_quantitative\n$demo_plot_guide_series_quantitative\n\n", "heading1": "Breaking out Series by Color", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can aggregate values into groups using the `x_bin` and `y_aggregate` arguments. If your x-axis is numeric, providing an `x_bin` will create a histogram-style binning:\n\n$code_plot_guide_aggregate_quantitative\n$demo_plot_guide_aggregate_quantitative\n\nIf your x-axis is a string type instead, they will act as the category bins automatically:\n\n$code_plot_guide_aggregate_nominal\n$demo_plot_guide_aggregate_nominal\n\n", "heading1": "Aggregating Values", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "You can use the `.select` listener to select regions of a plot. Click and drag on the plot below to select part of the plot.\n\n$code_plot_guide_selection\n$demo_plot_guide_selection\n\nYou can combine this and the `.double_click` listener to create some zoom in/out effects by changing `x_lim` which sets the bounds of the x-axis:\n\n$code_plot_guide_zoom\n$demo_plot_guide_zoom\n\nIf you had multiple plots with the same x column, your event listeners could target the x limits of all other plots so that the x-axes stay in sync.\n\n$code_plot_guide_zoom_sync\n$demo_plot_guide_zoom_sync\n\n", "heading1": "Selecting Regions", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "Take a look how you can have an interactive dashboard where the plots are functions of other Components.\n\n$code_plot_guide_interactive\n$demo_plot_guide_interactive\n\nIt's that simple to filter and control the data presented in your visualization!", "heading1": "Making an Interactive Dashboard", "source_page_url": "https://gradio.app/guides/creating-plots", "source_page_title": "Data Science And Plots - Creating Plots Guide"}, {"text": "```python\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\nengine = create_engine('sqlite:///your_database.db')\n\nwith gr.Blocks() as demo:\n    gr.LinePlot(pd.read_sql_query(\"SELECT time, price from flight_info;\", engine), x=\"time\", y=\"price\")\n```\n\nLet's see a a more interactive plot involving filters that modify your SQL query:\n\n```python\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\nengine = create_engine('sqlite:///your_database.db')\n\nwith gr.Blocks() as demo:\n    origin = gr.Dropdown([\"DFW\", \"DAL\", \"HOU\"], value=\"DFW\", label=\"Origin\")\n\n    gr.LinePlot(lambda origin: pd.read_sql_query(f\"SELECT time, price from flight_info WHERE origin = {origin};\", engine), inputs=origin, x=\"time\", y=\"price\")\n```\n\n", "heading1": "SQLite", "source_page_url": "https://gradio.app/guides/connecting-to-a-database", "source_page_title": "Data Science And Plots - Connecting To A Database Guide"}, {"text": "If you're using a different database format, all you have to do is swap out the engine, e.g.\n\n```python\nengine = create_engine('postgresql://username:password@host:port/database_name')\n```\n\n```python\nengine = create_engine('mysql://username:password@host:port/database_name')\n```\n\n```python\nengine = create_engine('oracle://username:password@host:port/database_name')\n```", "heading1": "Postgres, mySQL, and other databases", "source_page_url": "https://gradio.app/guides/connecting-to-a-database", "source_page_title": "Data Science And Plots - Connecting To A Database Guide"}, {"text": "Use any of the standard Gradio form components to filter your data. You can do this via event listeners or function-as-value syntax. Let's look at the event listener approach first:\n\n$code_plot_guide_filters_events\n$demo_plot_guide_filters_events\n\nAnd this would be the function-as-value approach for the same demo.\n\n$code_plot_guide_filters\n\n", "heading1": "Filters", "source_page_url": "https://gradio.app/guides/filters-tables-and-stats", "source_page_title": "Data Science And Plots - Filters Tables And Stats Guide"}, {"text": "Add `gr.DataFrame` and `gr.Label` to your dashboard for some hard numbers.\n\n$code_plot_guide_tables_stats\n$demo_plot_guide_tables_stats\n", "heading1": "Tables and Stats", "source_page_url": "https://gradio.app/guides/filters-tables-and-stats", "source_page_title": "Data Science And Plots - Filters Tables And Stats Guide"}, {"text": "Time plots need a datetime column on the x-axis. Here's a simple example with some flight data:\n\n$code_plot_guide_temporal\n$demo_plot_guide_temporal\n\n", "heading1": "Creating a Plot with a pd.Dataframe", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "You may wish to bin data by time buckets. Use `x_bin` to do so, using a string suffix with \"s\", \"m\", \"h\" or \"d\", such as \"15m\" or \"1d\".\n\n$code_plot_guide_aggregate_temporal\n$demo_plot_guide_aggregate_temporal\n\n", "heading1": "Aggregating by Time", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "You can use `gr.DateTime` to accept input datetime data. This works well with plots for defining the x-axis range for the data.\n\n$code_plot_guide_datetime\n$demo_plot_guide_datetime\n\nNote how `gr.DateTime` can accept a full datetime string, or a shorthand using `now - [0-9]+[smhd]` format to refer to a past time.\n\nYou will often have many time plots in which case you'd like to keep the x-axes in sync. The `DateTimeRange` custom component keeps a set of datetime plots in sync, and also uses the `.select` listener of plots to allow you to zoom into plots while keeping plots in sync. \n\nBecause it is a custom component, you first need to `pip install gradio_datetimerange`. Then run the following:\n\n$code_plot_guide_datetimerange\n$demo_plot_guide_datetimerange\n\nTry zooming around in the plots and see how DateTimeRange updates. All the plots updates their `x_lim` in sync. You also have a \"Back\" link in the component to allow you to quickly zoom in and out.\n\n", "heading1": "DateTime Components", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "In many cases, you're working with live, realtime date, not a static dataframe. In this case, you'd update the plot regularly with a `gr.Timer()`. Assuming there's a `get_data` method that gets the latest dataframe:\n\n```python\nwith gr.Blocks() as demo:\n    timer = gr.Timer(5)\n    plot1 = gr.BarPlot(x=\"time\", y=\"price\")\n    plot2 = gr.BarPlot(x=\"time\", y=\"price\", color=\"origin\")\n\n    timer.tick(lambda: [get_data(), get_data()], outputs=[plot1, plot2])\n```\n\nYou can also use the `every` shorthand to attach a `Timer` to a component that has a function value:\n\n```python\nwith gr.Blocks() as demo:\n    timer = gr.Timer(5)\n    plot1 = gr.BarPlot(get_data, x=\"time\", y=\"price\", every=timer)\n    plot2 = gr.BarPlot(get_data, x=\"time\", y=\"price\", color=\"origin\", every=timer)\n```\n\n\n", "heading1": "RealTime Data", "source_page_url": "https://gradio.app/guides/time-plots", "source_page_title": "Data Science And Plots - Time Plots Guide"}, {"text": "Let's start by using `llama-index` on top of `openai` to build a RAG chatbot on any text or PDF files that you can demo and share in less than 30 lines of code. You'll need to have an OpenAI key for this example (keep reading for the free, open-source equivalent!)\n\n$code_llm_llamaindex\n\n", "heading1": "Llama Index", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "Here's an example using `langchain` on top of `openai` to build a general-purpose chatbot. As before, you'll need to have an OpenAI key for this example.\n\n$code_llm_langchain\n\nTip: For quick prototyping, the community-maintained <a href='https://github.com/AK391/langchain-gradio'>langchain-gradio repo</a>  makes it even easier to build chatbots on top of LangChain.\n\n", "heading1": "LangChain", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "Of course, we could also use the `openai` library directy. Here a similar example to the LangChain , but this time with streaming as well:\n\nTip: For quick prototyping, the  <a href='https://github.com/gradio-app/openai-gradio'>openai-gradio library</a> makes it even easier to build chatbots on top of OpenAI models.\n\n\n", "heading1": "OpenAI", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "Of course, in many cases you want to run a chatbot locally. Here's the equivalent example using the SmolLM2-135M-Instruct model using the Hugging Face `transformers` library.\n\n$code_llm_hf_transformers\n\n", "heading1": "Hugging Face `transformers`", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "The SambaNova Cloud API provides access to full-precision open-source models, such as the Llama family. Here's an example of how to build a Gradio app around the SambaNova API\n\n$code_llm_sambanova\n\nTip: For quick prototyping, the  <a href='https://github.com/gradio-app/sambanova-gradio'>sambanova-gradio library</a> makes it even easier to build chatbots on top of SambaNova models.\n\n", "heading1": "SambaNova", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "The Hyperbolic AI API provides access to many open-source models, such as the Llama family. Here's an example of how to build a Gradio app around the Hyperbolic\n\n$code_llm_hyperbolic\n\nTip: For quick prototyping, the  <a href='https://github.com/HyperbolicLabs/hyperbolic-gradio'>hyperbolic-gradio library</a> makes it even easier to build chatbots on top of Hyperbolic models.\n\n\n", "heading1": "Hyperbolic", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "Anthropic's Claude model can also be used via API. Here's a simple 20 questions-style game built on top of the Anthropic API:\n\n$code_llm_claude\n\n\n", "heading1": "Anthropic's Claude", "source_page_url": "https://gradio.app/guides/chatinterface-examples", "source_page_title": "Chatbots - Chatinterface Examples Guide"}, {"text": "The Slack bot will listen to messages mentioning it in channels. When it receives a message (which can include text as well as files), it will send it to your Gradio app via Gradio's built-in API. Your bot will reply with the response it receives from the API. \n\nBecause Gradio's API is very flexible, you can create Slack bots that support text, images, audio, streaming, chat history, and a wide variety of other features very easily. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-19%20at%203.30.00%E2%80%AFPM.gif)\n\n", "heading1": "How does it work?", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "* Install the latest version of `gradio` and the `slack-bolt` library:\n\n```bash\npip install --upgrade gradio slack-bolt~=1.0\n```\n\n* Have a running Gradio app. This app can be running locally or on Hugging Face Spaces. In this example, we will be using the [Gradio Playground Space](https://huggingface.co/spaces/abidlabs/gradio-playground-bot), which takes in an image and/or text and generates the code to generate the corresponding Gradio app.\n\nNow, we are ready to get started!\n\n1. Create a Slack App\n\n1. Go to [api.slack.com/apps](https://api.slack.com/apps) and click \"Create New App\"\n2. Choose \"From scratch\" and give your app a name\n3. Select the workspace where you want to develop your app\n4. Under \"OAuth & Permissions\", scroll to \"Scopes\" and add these Bot Token Scopes:\n   - `app_mentions:read`\n   - `chat:write`\n   - `files:read`\n   - `files:write`\n5. In the same \"OAuth & Permissions\" page, scroll back up and click the button to install the app to your workspace.\n6. Note the \"Bot User OAuth Token\" (starts with `xoxb-`) that appears as we'll need it later\n7. Click on \"Socket Mode\" in the menu bar. When the page loads, click the toggle to \"Enable Socket Mode\"\n8. Give your token a name, such as `socket-token` and copy the token that is generated (starts with `xapp-`) as we'll need it later.\n9. Finally, go to the \"Event Subscription\" option in the menu bar. Click the toggle to \"Enable Events\" and subscribe to the `app_mention` bot event.\n\n2. Write a Slack bot\n\nLet's start by writing a very simple Slack bot, just to make sure that everything is working. Write the following Python code in a file called `bot.py`, pasting the two tokens from step 6 and step 8 in the previous section.\n\n```py\nfrom slack_bolt import App\nfrom slack_bolt.adapter.socket_mode import SocketModeHandler\n\nSLACK_BOT_TOKEN = PASTE YOUR SLACK BOT TOKEN HERE\nSLACK_APP_TOKEN = PASTE YOUR SLACK APP TOKEN HERE\n\napp = App(token=SLACK_BOT_TOKEN)\n\n@app.event(\"app_mention\")\ndef handle_app_mention_ev", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "eHandler\n\nSLACK_BOT_TOKEN = PASTE YOUR SLACK BOT TOKEN HERE\nSLACK_APP_TOKEN = PASTE YOUR SLACK APP TOKEN HERE\n\napp = App(token=SLACK_BOT_TOKEN)\n\n@app.event(\"app_mention\")\ndef handle_app_mention_events(body, say):\n    user_id = body[\"event\"][\"user\"]\n    say(f\"Hi <@{user_id}>! You mentioned me and said: {body['event']['text']}\")\n\nif __name__ == \"__main__\":\n    handler = SocketModeHandler(app, SLACK_APP_TOKEN)\n    handler.start()\n```\n\nIf that is working, we are ready to add Gradio-specific code. We will be using the [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) to query the Gradio Playground Space mentioned above. Here's the updated `bot.py` file:\n\n```python\nfrom slack_bolt import App\nfrom slack_bolt.adapter.socket_mode import SocketModeHandler\n\nSLACK_BOT_TOKEN = PASTE YOUR SLACK BOT TOKEN HERE\nSLACK_APP_TOKEN = PASTE YOUR SLACK APP TOKEN HERE\n\napp = App(token=SLACK_BOT_TOKEN)\ngradio_client = Client(\"abidlabs/gradio-playground-bot\")\n\ndef download_image(url, filename):\n    headers = {\"Authorization\": f\"Bearer {SLACK_BOT_TOKEN}\"}\n    response = httpx.get(url, headers=headers)\n    image_path = f\"./images/{filename}\"\n    os.makedirs(\"./images\", exist_ok=True)\n    with open(image_path, \"wb\") as f:\n        f.write(response.content)\n    return image_path\n\ndef slackify_message(message):   \n    Replace markdown links with slack format and remove code language specifier after triple backticks\n    pattern = r'\\[(.*?)\\]\\((.*?)\\)'\n    cleaned = re.sub(pattern, r'<\\2|\\1>', message)\n    cleaned = re.sub(r'```\\w+\\n', '```', cleaned)\n    return cleaned.strip()\n\n@app.event(\"app_mention\")\ndef handle_app_mention_events(body, say):\n    Extract the message content without the bot mention\n    text = body[\"event\"][\"text\"]\n    bot_user_id = body[\"authorizations\"][0][\"user_id\"]\n    clean_message = text.replace(f\"<@{bot_user_id}>\", \"\").strip()\n    \n    Handle images if present\n    files = []\n    if \"files\" in body[\"event\"]:\n        for", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "= body[\"authorizations\"][0][\"user_id\"]\n    clean_message = text.replace(f\"<@{bot_user_id}>\", \"\").strip()\n    \n    Handle images if present\n    files = []\n    if \"files\" in body[\"event\"]:\n        for file in body[\"event\"][\"files\"]:\n            if file[\"filetype\"] in [\"png\", \"jpg\", \"jpeg\", \"gif\", \"webp\"]:\n                image_path = download_image(file[\"url_private_download\"], file[\"name\"])\n                files.append(handle_file(image_path))\n                break\n    \n    Submit to Gradio and send responses back to Slack\n    for response in gradio_client.submit(\n        message={\"text\": clean_message, \"files\": files},\n    ):\n        cleaned_response = slackify_message(response[-1])\n        say(cleaned_response)\n\nif __name__ == \"__main__\":\n    handler = SocketModeHandler(app, SLACK_APP_TOKEN)\n    handler.start()\n```\n3. Add the bot to your Slack Workplace\n\nNow, create a new channel or navigate to an existing channel in your Slack workspace where you want to use the bot. Click the \"+\" button next to \"Channels\" in your Slack sidebar and follow the prompts to create a new channel.\n\nFinally, invite your bot to the channel:\n1. In your new channel, type `/invite @YourBotName`\n2. Select your bot from the dropdown\n3. Click \"Invite to Channel\"\n\n4. That's it!\n\nNow you can mention your bot in any channel it's in, optionally attach an image, and it will respond with generated Gradio app code!\n\nThe bot will:\n1. Listen for mentions\n2. Process any attached images\n3. Send the text and images to your Gradio app\n4. Stream the responses back to the Slack channel\n\nThis is just a basic example - you can extend it to handle more types of files, add error handling, or integrate with different Gradio apps!\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-19%20at%203.30.00%E2%80%AFPM.gif)\n\nIf you build a Slack bot from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gr", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "/main/gradio-guides/Screen%20Recording%202024-12-19%20at%203.30.00%E2%80%AFPM.gif)\n\nIf you build a Slack bot from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gradio), and we are happy to help you amplify!", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-slack-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Slack Bot From A Gradio App Guide"}, {"text": "Each message in Gradio's chatbot is a dataclass of type `ChatMessage` (this is assuming that chatbot's `type=\"message\"`, which is strongly recommended). The schema of `ChatMessage` is as follows:\n\n ```py\n@dataclass\nclass ChatMessage:\n    content: str | Component\n    role: Literal[\"user\", \"assistant\"]\n    metadata: MetadataDict = None\n    options: list[OptionDict] = None\n\nclass MetadataDict(TypedDict):\n    title: NotRequired[str]\n    id: NotRequired[int | str]\n    parent_id: NotRequired[int | str]\n    log: NotRequired[str]\n    duration: NotRequired[float]\n    status: NotRequired[Literal[\"pending\", \"done\"]]\n\nclass OptionDict(TypedDict):\n    label: NotRequired[str]\n    value: str\n ```\n\n\nFor our purposes, the most important key is the `metadata` key, which accepts a dictionary. If this dictionary includes a `title` for the message, it will be displayed in a collapsible accordion representing a thought. It's that simple! Take a look at this example:\n\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    chatbot = gr.Chatbot(\n        type=\"messages\",\n        value=[\n            gr.ChatMessage(\n                role=\"user\", \n                content=\"What is the weather in San Francisco?\"\n            ),\n            gr.ChatMessage(\n                role=\"assistant\", \n                content=\"I need to use the weather API tool?\",\n                metadata={\"title\":  \"\ud83e\udde0 Thinking\"}\n        ]\n    )\n\ndemo.launch()\n```\n\n\n\nIn addition to `title`, the dictionary provided to `metadata` can take several optional keys:\n\n* `log`: an optional string value to be displayed in a subdued font next to the thought title.\n* `duration`: an optional numeric value representing the duration of the thought/tool usage, in seconds. Displayed in a subdued font next inside parentheses next to the thought title.\n* `status`: if set to `\"pending\"`, a spinner appears next to the thought title and the accordion is initialized open.  If `status` is `\"done\"`, the thought accordion is initialized closed. I", "heading1": "The `ChatMessage` dataclass", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "ht title.\n* `status`: if set to `\"pending\"`, a spinner appears next to the thought title and the accordion is initialized open.  If `status` is `\"done\"`, the thought accordion is initialized closed. If `status` is not provided, the thought accordion is initialized open and no spinner is displayed.\n* `id` and `parent_id`: if these are provided, they can be used to nest thoughts inside other thoughts.\n\nBelow, we show several complete examples of using `gr.Chatbot` and `gr.ChatInterface` to display tool use or thinking UIs.\n\n", "heading1": "The `ChatMessage` dataclass", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "A real example using transformers.agents\n\nWe'll create a Gradio application simple agent that has access to a text-to-image tool.\n\nTip: Make sure you read the [smolagents documentation](https://huggingface.co/docs/smolagents/index) first\n\nWe'll start by importing the necessary classes from transformers and gradio. \n\n```python\nimport gradio as gr\nfrom gradio import ChatMessage\nfrom transformers import Tool, ReactCodeAgent  type: ignore\nfrom transformers.agents import stream_to_gradio, HfApiEngine  type: ignore\n\nImport tool from Hub\nimage_generation_tool = Tool.from_space(\n    space_id=\"black-forest-labs/FLUX.1-schnell\",\n    name=\"image_generator\",\n    description=\"Generates an image following your prompt. Returns a PIL Image.\",\n    api_name=\"/infer\",\n)\n\nllm_engine = HfApiEngine(\"Qwen/Qwen2.5-Coder-32B-Instruct\")\nInitialize the agent with both tools and engine\nagent = ReactCodeAgent(tools=[image_generation_tool], llm_engine=llm_engine)\n```\n\nThen we'll build the UI:\n\n```python\ndef interact_with_agent(prompt, history):\n    messages = []\n    yield messages\n    for msg in stream_to_gradio(agent, prompt):\n        messages.append(asdict(msg))\n        yield messages\n    yield messages\n\n\ndemo = gr.ChatInterface(\n    interact_with_agent,\n    chatbot= gr.Chatbot(\n        label=\"Agent\",\n        type=\"messages\",\n        avatar_images=(\n            None,\n            \"https://em-content.zobj.net/source/twitter/53/robot-face_1f916.png\",\n        ),\n    ),\n    examples=[\n        [\"Generate an image of an astronaut riding an alligator\"],\n        [\"I am writing a children's book for my daughter. Can you help me with some illustrations?\"],\n    ],\n    type=\"messages\",\n)\n```\n\nYou can see the full demo code [here](https://huggingface.co/spaces/gradio/agent_chatbot/blob/main/app.py).\n\n\n![transformers_agent_code](https://github.com/freddyaboulton/freddyboulton/assets/41651716/c8d21336-e0e6-4878-88ea-e6fcfef3552d)\n\n\nA real example using langchain agents\n\nWe'll create a UI for l", "heading1": "Building with Agents", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "\n\n\n![transformers_agent_code](https://github.com/freddyaboulton/freddyboulton/assets/41651716/c8d21336-e0e6-4878-88ea-e6fcfef3552d)\n\n\nA real example using langchain agents\n\nWe'll create a UI for langchain agent that has access to a search engine.\n\nWe'll begin with imports and setting up the langchain agent. Note that you'll need an .env file with the following environment variables set - \n\n```\nSERPAPI_API_KEY=\nHF_TOKEN=\nOPENAI_API_KEY=\n```\n\n```python\nfrom langchain import hub\nfrom langchain.agents import AgentExecutor, create_openai_tools_agent, load_tools\nfrom langchain_openai import ChatOpenAI\nfrom gradio import ChatMessage\nimport gradio as gr\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nmodel = ChatOpenAI(temperature=0, streaming=True)\n\ntools = load_tools([\"serpapi\"])\n\nGet the prompt to use - you can modify this!\nprompt = hub.pull(\"hwchase17/openai-tools-agent\")\nagent = create_openai_tools_agent(\n    model.with_config({\"tags\": [\"agent_llm\"]}), tools, prompt\n)\nagent_executor = AgentExecutor(agent=agent, tools=tools).with_config(\n    {\"run_name\": \"Agent\"}\n)\n```\n\nThen we'll create the Gradio UI\n\n```python\nasync def interact_with_langchain_agent(prompt, messages):\n    messages.append(ChatMessage(role=\"user\", content=prompt))\n    yield messages\n    async for chunk in agent_executor.astream(\n        {\"input\": prompt}\n    ):\n        if \"steps\" in chunk:\n            for step in chunk[\"steps\"]:\n                messages.append(ChatMessage(role=\"assistant\", content=step.action.log,\n                                  metadata={\"title\": f\"\ud83d\udee0\ufe0f Used tool {step.action.tool}\"}))\n                yield messages\n        if \"output\" in chunk:\n            messages.append(ChatMessage(role=\"assistant\", content=chunk[\"output\"]))\n            yield messages\n\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with a LangChain Agent \ud83e\udd9c\u26d3\ufe0f and see its thoughts \ud83d\udcad\")\n    chatbot = gr.Chatbot(\n        type=\"messages\",\n        label=\"Agent\",\n        avatar_images=(\n            None,\n         ", "heading1": "Building with Agents", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "   gr.Markdown(\"Chat with a LangChain Agent \ud83e\udd9c\u26d3\ufe0f and see its thoughts \ud83d\udcad\")\n    chatbot = gr.Chatbot(\n        type=\"messages\",\n        label=\"Agent\",\n        avatar_images=(\n            None,\n            \"https://em-content.zobj.net/source/twitter/141/parrot_1f99c.png\",\n        ),\n    )\n    input = gr.Textbox(lines=1, label=\"Chat Message\")\n    input.submit(interact_with_langchain_agent, [input_2, chatbot_2], [chatbot_2])\n\ndemo.launch()\n```\n\n![langchain_agent_code](https://github.com/freddyaboulton/freddyboulton/assets/41651716/762283e5-3937-47e5-89e0-79657279ea67)\n\nThat's it! See our finished langchain demo [here](https://huggingface.co/spaces/gradio/langchain-agent).\n\n\n", "heading1": "Building with Agents", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "The Gradio Chatbot can natively display intermediate thoughts of a _thinking_ LLM. This makes it perfect for creating UIs that show how an AI model \"thinks\" while generating responses. Below guide will show you how to build a chatbot that displays Gemini AI's thought process in real-time.\n\n\nA real example using Gemini 2.0 Flash Thinking API\n\nLet's create a complete chatbot that shows its thoughts and responses in real-time. We'll use Google's Gemini API for accessing Gemini 2.0 Flash Thinking LLM and Gradio for the UI.\n\nWe'll begin with imports and setting up the gemini client. Note that you'll need to [acquire a Google Gemini API key](https://aistudio.google.com/apikey) first -\n\n```python\nimport gradio as gr\nfrom gradio import ChatMessage\nfrom typing import Iterator\nimport google.generativeai as genai\n\ngenai.configure(api_key=\"your-gemini-api-key\")\nmodel = genai.GenerativeModel(\"gemini-2.0-flash-thinking-exp-1219\")\n```\n\nFirst, let's set up our streaming function that handles the model's output:\n\n```python\ndef stream_gemini_response(user_message: str, messages: list) -> Iterator[list]:\n    \"\"\"\n    Streams both thoughts and responses from the Gemini model.\n    \"\"\"\n    Initialize response from Gemini\n    response = model.generate_content(user_message, stream=True)\n    \n    Initialize buffers\n    thought_buffer = \"\"\n    response_buffer = \"\"\n    thinking_complete = False\n    \n    Add initial thinking message\n    messages.append(\n        ChatMessage(\n            role=\"assistant\",\n            content=\"\",\n            metadata={\"title\": \"\u23f3Thinking: *The thoughts produced by the Gemini2.0 Flash model are experimental\"}\n        )\n    )\n    \n    for chunk in response:\n        parts = chunk.candidates[0].content.parts\n        current_chunk = parts[0].text\n        \n        if len(parts) == 2 and not thinking_complete:\n            Complete thought and start response\n            thought_buffer += current_chunk\n            messages[-1] = ChatMessage(\n                rol", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": " if len(parts) == 2 and not thinking_complete:\n            Complete thought and start response\n            thought_buffer += current_chunk\n            messages[-1] = ChatMessage(\n                role=\"assistant\",\n                content=thought_buffer,\n                metadata={\"title\": \"\u23f3Thinking: *The thoughts produced by the Gemini2.0 Flash model are experimental\"}\n            )\n            \n            Add response message\n            messages.append(\n                ChatMessage(\n                    role=\"assistant\",\n                    content=parts[1].text\n                )\n            )\n            thinking_complete = True\n            \n        elif thinking_complete:\n            Continue streaming response\n            response_buffer += current_chunk\n            messages[-1] = ChatMessage(\n                role=\"assistant\",\n                content=response_buffer\n            )\n            \n        else:\n            Continue streaming thoughts\n            thought_buffer += current_chunk\n            messages[-1] = ChatMessage(\n                role=\"assistant\",\n                content=thought_buffer,\n                metadata={\"title\": \"\u23f3Thinking: *The thoughts produced by the Gemini2.0 Flash model are experimental\"}\n            )\n        \n        yield messages\n```\n\nThen, let's create the Gradio interface:\n\n```python\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with Gemini 2.0 Flash and See its Thoughts \ud83d\udcad\")\n    \n    chatbot = gr.Chatbot(\n        type=\"messages\",\n        label=\"Gemini2.0 'Thinking' Chatbot\",\n        render_markdown=True,\n    )\n    \n    input_box = gr.Textbox(\n        lines=1,\n        label=\"Chat Message\",\n        placeholder=\"Type your message here and press Enter...\"\n    )\n    \n    Set up event handlers\n    msg_store = gr.State(\"\")  Store for preserving user message\n    \n    input_box.submit(\n        lambda msg: (msg, msg, \"\"),  Store message and clear input\n        inputs=[input_box],\n        outputs=[msg_store, input_box, inp", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "Store for preserving user message\n    \n    input_box.submit(\n        lambda msg: (msg, msg, \"\"),  Store message and clear input\n        inputs=[input_box],\n        outputs=[msg_store, input_box, input_box],\n        queue=False\n    ).then(\n        user_message,  Add user message to chat\n        inputs=[msg_store, chatbot],\n        outputs=[input_box, chatbot],\n        queue=False\n    ).then(\n        stream_gemini_response,  Generate and stream response\n        inputs=[msg_store, chatbot],\n        outputs=chatbot\n    )\n\ndemo.launch()\n```\n\nThis creates a chatbot that:\n\n- Displays the model's thoughts in a collapsible section\n- Streams the thoughts and final response in real-time\n- Maintains a clean chat history\n\n That's it! You now have a chatbot that not only responds to users but also shows its thinking process, creating a more transparent and engaging interaction. See our finished Gemini 2.0 Flash Thinking demo [here](https://huggingface.co/spaces/ysharma/Gemini2-Flash-Thinking).\n\n\n Building with Citations \n\nThe Gradio Chatbot can display citations from LLM responses, making it perfect for creating UIs that show source documentation and references. This guide will show you how to build a chatbot that displays Claude's citations in real-time.\n\nA real example using Anthropic's Citations API\nLet's create a complete chatbot that shows both responses and their supporting citations. We'll use Anthropic's Claude API with citations enabled and Gradio for the UI.\n\nWe'll begin with imports and setting up the Anthropic client. Note that you'll need an `ANTHROPIC_API_KEY` environment variable set:\n\n```python\nimport gradio as gr\nimport anthropic\nimport base64\nfrom typing import List, Dict, Any\n\nclient = anthropic.Anthropic()\n```\n\nFirst, let's set up our message formatting functions that handle document preparation:\n\n```python\ndef encode_pdf_to_base64(file_obj) -> str:\n    \"\"\"Convert uploaded PDF file to base64 string.\"\"\"\n    if file_obj is None:\n        return None\n", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "ng functions that handle document preparation:\n\n```python\ndef encode_pdf_to_base64(file_obj) -> str:\n    \"\"\"Convert uploaded PDF file to base64 string.\"\"\"\n    if file_obj is None:\n        return None\n    with open(file_obj.name, 'rb') as f:\n        return base64.b64encode(f.read()).decode('utf-8')\n\ndef format_message_history(\n    history: list, \n    enable_citations: bool,\n    doc_type: str,\n    text_input: str,\n    pdf_file: str\n) -> List[Dict]:\n    \"\"\"Convert Gradio chat history to Anthropic message format.\"\"\"\n    formatted_messages = []\n    \n    Add previous messages\n    for msg in history[:-1]:\n        if msg[\"role\"] == \"user\":\n            formatted_messages.append({\"role\": \"user\", \"content\": msg[\"content\"]})\n    \n    Prepare the latest message with document\n    latest_message = {\"role\": \"user\", \"content\": []}\n    \n    if enable_citations:\n        if doc_type == \"plain_text\":\n            latest_message[\"content\"].append({\n                \"type\": \"document\",\n                \"source\": {\n                    \"type\": \"text\",\n                    \"media_type\": \"text/plain\",\n                    \"data\": text_input.strip()\n                },\n                \"title\": \"Text Document\",\n                \"citations\": {\"enabled\": True}\n            })\n        elif doc_type == \"pdf\" and pdf_file:\n            pdf_data = encode_pdf_to_base64(pdf_file)\n            if pdf_data:\n                latest_message[\"content\"].append({\n                    \"type\": \"document\",\n                    \"source\": {\n                        \"type\": \"base64\",\n                        \"media_type\": \"application/pdf\",\n                        \"data\": pdf_data\n                    },\n                    \"title\": pdf_file.name,\n                    \"citations\": {\"enabled\": True}\n                })\n    \n    Add the user's question\n    latest_message[\"content\"].append({\"type\": \"text\", \"text\": history[-1][\"content\"]})\n    \n    formatted_messages.append(latest_message)\n    return formatted_messages\n```\n\nThen, ", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": " the user's question\n    latest_message[\"content\"].append({\"type\": \"text\", \"text\": history[-1][\"content\"]})\n    \n    formatted_messages.append(latest_message)\n    return formatted_messages\n```\n\nThen, let's create our bot response handler that processes citations:\n\n```python\ndef bot_response(\n    history: list,\n    enable_citations: bool,\n    doc_type: str,\n    text_input: str,\n    pdf_file: str\n) -> List[Dict[str, Any]]:\n    try:\n        messages = format_message_history(history, enable_citations, doc_type, text_input, pdf_file)\n        response = client.messages.create(model=\"claude-3-5-sonnet-20241022\", max_tokens=1024, messages=messages)\n        \n        Initialize main response and citations\n        main_response = \"\"\n        citations = []\n        \n        Process each content block\n        for block in response.content:\n            if block.type == \"text\":\n                main_response += block.text\n                if enable_citations and hasattr(block, 'citations') and block.citations:\n                    for citation in block.citations:\n                        if citation.cited_text not in citations:\n                            citations.append(citation.cited_text)\n        \n        Add main response\n        history.append({\"role\": \"assistant\", \"content\": main_response})\n        \n        Add citations in a collapsible section\n        if enable_citations and citations:\n            history.append({\n                \"role\": \"assistant\",\n                \"content\": \"\\n\".join([f\"\u2022 {cite}\" for cite in citations]),\n                \"metadata\": {\"title\": \"\ud83d\udcda Citations\"}\n            })\n        \n        return history\n            \n    except Exception as e:\n        history.append({\n            \"role\": \"assistant\",\n            \"content\": \"I apologize, but I encountered an error while processing your request.\"\n        })\n        return history\n```\n\nFinally, let's create the Gradio interface:\n\n```python\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with Citations\"", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "an error while processing your request.\"\n        })\n        return history\n```\n\nFinally, let's create the Gradio interface:\n\n```python\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with Citations\")\n    \n    with gr.Row(scale=1):\n        with gr.Column(scale=4):\n            chatbot = gr.Chatbot(type=\"messages\", bubble_full_width=False, show_label=False, scale=1)\n            msg = gr.Textbox(placeholder=\"Enter your message here...\", show_label=False, container=False)\n            \n        with gr.Column(scale=1):\n            enable_citations = gr.Checkbox(label=\"Enable Citations\", value=True, info=\"Toggle citation functionality\" )\n            doc_type_radio = gr.Radio( choices=[\"plain_text\", \"pdf\"], value=\"plain_text\", label=\"Document Type\", info=\"Choose the type of document to use\")\n            text_input = gr.Textbox(label=\"Document Content\", lines=10, info=\"Enter the text you want to reference\")\n            pdf_input = gr.File(label=\"Upload PDF\", file_types=[\".pdf\"], file_count=\"single\", visible=False)\n    \n    Handle message submission\n    msg.submit(\n        user_message,\n        [msg, chatbot, enable_citations, doc_type_radio, text_input, pdf_input],\n        [msg, chatbot]\n    ).then(\n        bot_response,\n        [chatbot, enable_citations, doc_type_radio, text_input, pdf_input],\n        chatbot\n    )\n\ndemo.launch()\n```\n\nThis creates a chatbot that:\n- Supports both plain text and PDF documents for Claude to cite from \n- Displays Citations in collapsible sections using our `metadata` feature\n- Shows source quotes directly from the given documents\n\nThe citations feature works particularly well with the Gradio Chatbot's `metadata` support, allowing us to create collapsible sections that keep the chat interface clean while still providing easy access to source documentation.\n\nThat's it! You now have a chatbot that not only responds to users but also shows its sources, creating a more transparent and trustworthy interaction. See our finished Citations demo [her", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "umentation.\n\nThat's it! You now have a chatbot that not only responds to users but also shows its sources, creating a more transparent and trustworthy interaction. See our finished Citations demo [here](https://huggingface.co/spaces/ysharma/anthropic-citations-with-gradio-metadata-key).\n\n", "heading1": "Building with Visibly Thinking LLMs", "source_page_url": "https://gradio.app/guides/agents-and-tool-usage", "source_page_title": "Chatbots - Agents And Tool Usage Guide"}, {"text": "First, we'll build the UI without handling these events and build from there. \nWe'll use the Hugging Face InferenceClient in order to get started without setting up\nany API keys.\n\nThis is what the first draft of our application looks like:\n\n```python\nfrom huggingface_hub import InferenceClient\nimport gradio as gr\n\nclient = InferenceClient()\n\ndef respond(\n    prompt: str,\n    history,\n):\n    if not history:\n        history = [{\"role\": \"system\", \"content\": \"You are a friendly chatbot\"}]\n    history.append({\"role\": \"user\", \"content\": prompt})\n\n    yield history\n\n    response = {\"role\": \"assistant\", \"content\": \"\"}\n    for message in client.chat_completion(\n        history,\n        temperature=0.95,\n        top_p=0.9,\n        max_tokens=512,\n        stream=True,\n        model=\"HuggingFaceH4/zephyr-7b-beta\"\n    ):\n        response[\"content\"] += message.choices[0].delta.content or \"\"\n\n        yield history + [response]\n\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Chat with Hugging Face Zephyr 7b \ud83e\udd17\")\n    chatbot = gr.Chatbot(\n        label=\"Agent\",\n        type=\"messages\",\n        avatar_images=(\n            None,\n            \"https://em-content.zobj.net/source/twitter/376/hugging-face_1f917.png\",\n        ),\n    )\n    prompt = gr.Textbox(max_lines=1, label=\"Chat Message\")\n    prompt.submit(respond, [prompt, chatbot], [chatbot])\n    prompt.submit(lambda: \"\", None, [prompt])\n\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\n", "heading1": "The UI", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "Our undo event will populate the textbox with the previous user message and also remove all subsequent assistant responses.\n\nIn order to know the index of the last user message, we can pass `gr.UndoData` to our event handler function like so:\n\n```python\ndef handle_undo(history, undo_data: gr.UndoData):\n    return history[:undo_data.index], history[undo_data.index]['content']\n```\n\nWe then pass this function to the `undo` event!\n\n```python\n    chatbot.undo(handle_undo, chatbot, [chatbot, prompt])\n```\n\nYou'll notice that every bot response will now have an \"undo icon\" you can use to undo the response - \n\n![undo_event](https://github.com/user-attachments/assets/180b5302-bc4a-4c3e-903c-f14ec2adcaa6)\n\nTip: You can also access the content of the user message with `undo_data.value`\n\n", "heading1": "The Undo Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "The retry event will work similarly. We'll use `gr.RetryData` to get the index of the previous user message and remove all the subsequent messages from the history. Then we'll use the `respond` function to generate a new response. We could also get the previous prompt via the `value` property of `gr.RetryData`.\n\n```python\ndef handle_retry(history, retry_data: gr.RetryData):\n    new_history = history[:retry_data.index]\n    previous_prompt = history[retry_data.index]['content']\n    yield from respond(previous_prompt, new_history)\n\n...\n\nchatbot.retry(handle_retry, chatbot, chatbot)\n```\n\nYou'll see that the bot messages have a \"retry\" icon now -\n\n![retry_event](https://github.com/user-attachments/assets/cec386a7-c4cd-4fb3-a2d7-78fd806ceac6)\n\nTip: The Hugging Face inference API caches responses, so in this demo, the retry button will not generate a new response.\n\n", "heading1": "The Retry Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "By now you should hopefully be seeing the pattern!\nTo let users like a message, we'll add a `.like` event to our chatbot.\nWe'll pass it a function that accepts a `gr.LikeData` object.\nIn this case, we'll just print the message that was either liked or disliked.\n\n```python\ndef handle_like(data: gr.LikeData):\n    if data.liked:\n        print(\"You upvoted this response: \", data.value)\n    else:\n        print(\"You downvoted this response: \", data.value)\n\n...\n\nchatbot.like(vote, None, None)\n```\n\n", "heading1": "The Like Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "Same idea with the edit listener! with `gr.Chatbot(editable=True)`, you can capture user edits. The `gr.EditData` object tells us the index of the message edited and the new text of the mssage. Below, we use this object to edit the history, and delete any subsequent messages. \n\n```python\ndef handle_edit(history, edit_data: gr.EditData):\n    new_history = history[:edit_data.index]\n    new_history[-1]['content'] = edit_data.value\n    return new_history\n\n...\n\nchatbot.edit(handle_edit, chatbot, chatbot)\n```\n\n", "heading1": "The Edit Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "As a bonus, we'll also cover the `.clear()` event, which is triggered when the user clicks the clear icon to clear all messages. As a developer, you can attach additional events that should happen when this icon is clicked, e.g. to handle clearing of additional chatbot state:\n\n```python\nfrom uuid import uuid4\nimport gradio as gr\n\n\ndef clear():\n    print(\"Cleared uuid\")\n    return uuid4()\n\n\ndef chat_fn(user_input, history, uuid):\n    return f\"{user_input} with uuid {uuid}\"\n\n\nwith gr.Blocks() as demo:\n    uuid_state = gr.State(\n        uuid4\n    )\n    chatbot = gr.Chatbot(type=\"messages\")\n    chatbot.clear(clear, outputs=[uuid_state])\n\n    gr.ChatInterface(\n        chat_fn,\n        additional_inputs=[uuid_state],\n        chatbot=chatbot,\n        type=\"messages\"\n    )\n\ndemo.launch()\n```\n\nIn this example, the `clear` function, bound to the `chatbot.clear` event, returns a new UUID into our session state, when the chat history is cleared via the trash icon. This can be seen in the `chat_fn` function, which references the UUID saved in our session state.\n\nThis example also shows that you can use these events with `gr.ChatInterface` by passing in a custom `gr.Chatbot` object.\n\n", "heading1": "The Clear Event", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "That's it! You now know how you can implement the retry, undo, like, and clear events for the Chatbot.\n\n\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/chatbot-specific-events", "source_page_title": "Chatbots - Chatbot Specific Events Guide"}, {"text": "Chatbots are a popular application of large language models (LLMs). Using Gradio, you can easily build a chat application and share that with your users, or try it yourself using an intuitive UI.\n\nThis tutorial uses `gr.ChatInterface()`, which is a high-level abstraction that allows you to create your chatbot UI fast, often with a _few lines of Python_. It can be easily adapted to support multimodal chatbots, or chatbots that require further customization.\n\n**Prerequisites**: please make sure you are using the latest version of Gradio:\n\n```bash\n$ pip install --upgrade gradio\n```\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "If you have a chat server serving an OpenAI-API compatible endpoint (such as Ollama), you can spin up a ChatInterface in a single line of Python. First, also run `pip install openai`. Then, with your own URL, model, and optional token:\n\n```python\nimport gradio as gr\n\ngr.load_chat(\"http://localhost:11434/v1/\", model=\"llama3.2\", token=\"***\").launch()\n```\n\nRead about `gr.load_chat` in [the docs](https://www.gradio.app/docs/gradio/load_chat). If you have your own model, keep reading to see how to create an application around any chat model in Python!\n\n", "heading1": "Note for OpenAI-API compatible endpoints", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "To create a chat application with `gr.ChatInterface()`, the first thing you should do is define your **chat function**. In the simplest case, your chat function should accept two arguments: `message` and `history` (the arguments can be named anything, but must be in this order).\n\n- `message`: a `str` representing the user's most recent message.\n- `history`: a list of openai-style dictionaries with `role` and `content` keys, representing the previous conversation history. May also include additional keys representing message metadata.\n\nFor example, the `history` could look like this:\n\n```python\n[\n    {\"role\": \"user\", \"content\": \"What is the capital of France?\"},\n    {\"role\": \"assistant\", \"content\": \"Paris\"}\n]\n```\n\nwhile the next `message` would be:\n\n```py\n\"And what is its largest city?\"\n```\n\nYour chat function simply needs to return: \n\n* a `str` value, which is the chatbot's response based on the chat `history` and most recent `message`, for example, in this case:\n\n```\nParis is also the largest city.\n```\n\nLet's take a look at a few example chat functions:\n\n**Example: a chatbot that randomly responds with yes or no**\n\nLet's write a chat function that responds `Yes` or `No` randomly.\n\nHere's our chat function:\n\n```python\nimport random\n\ndef random_response(message, history):\n    return random.choice([\"Yes\", \"No\"])\n```\n\nNow, we can plug this into `gr.ChatInterface()` and call the `.launch()` method to create the web interface:\n\n```python\nimport gradio as gr\n\ngr.ChatInterface(\n    fn=random_response, \n    type=\"messages\"\n).launch()\n```\n\nTip: Always set type=\"messages\" in gr.ChatInterface. The default value (type=\"tuples\") is deprecated and will be removed in a future version of Gradio.\n\nThat's it! Here's our running demo, try it out:\n\n$demo_chatinterface_random_response\n\n**Example: a chatbot that alternates between agreeing and disagreeing**\n\nOf course, the previous example was very simplistic, it didn't take user input or the previous history into account! Here's another", "heading1": "Defining a chat function", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "ample: a chatbot that alternates between agreeing and disagreeing**\n\nOf course, the previous example was very simplistic, it didn't take user input or the previous history into account! Here's another simple example showing how to incorporate a user's input as well as the history.\n\n```python\nimport gradio as gr\n\ndef alternatingly_agree(message, history):\n    if len([h for h in history if h['role'] == \"assistant\"]) % 2 == 0:\n        return f\"Yes, I do think that: {message}\"\n    else:\n        return \"I don't think so\"\n\ngr.ChatInterface(\n    fn=alternatingly_agree, \n    type=\"messages\"\n).launch()\n```\n\nWe'll look at more realistic examples of chat functions in our next Guide, which shows [examples of using `gr.ChatInterface` with popular LLMs](../guides/chatinterface-examples). \n\n", "heading1": "Defining a chat function", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "In your chat function, you can use `yield` to generate a sequence of partial responses, each replacing the previous ones. This way, you'll end up with a streaming chatbot. It's that simple!\n\n```python\nimport time\nimport gradio as gr\n\ndef slow_echo(message, history):\n    for i in range(len(message)):\n        time.sleep(0.3)\n        yield \"You typed: \" + message[: i+1]\n\ngr.ChatInterface(\n    fn=slow_echo, \n    type=\"messages\"\n).launch()\n```\n\nWhile the response is streaming, the \"Submit\" button turns into a \"Stop\" button that can be used to stop the generator function.\n\nTip: Even though you are yielding the latest message at each iteration, Gradio only sends the \"diff\" of each message from the server to the frontend, which reduces latency and data consumption over your network.\n\n", "heading1": "Streaming chatbots", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "If you're familiar with Gradio's `gr.Interface` class, the `gr.ChatInterface` includes many of the same arguments that you can use to customize the look and feel of your Chatbot. For example, you can:\n\n- add a title and description above your chatbot using `title` and `description` arguments.\n- add a theme or custom css using `theme` and `css` arguments respectively.\n- add `examples` and even enable `cache_examples`, which make your Chatbot easier for users to try it out.\n- customize the chatbot (e.g. to change the height or add a placeholder) or textbox (e.g. to add a max number of characters or add a placeholder).\n\n**Adding examples**\n\nYou can add preset examples to your `gr.ChatInterface` with the `examples` parameter, which takes a list of string examples. Any examples will appear as \"buttons\" within the Chatbot before any messages are sent. If you'd like to include images or other files as part of your examples, you can do so by using this dictionary format for each example instead of a string: `{\"text\": \"What's in this image?\", \"files\": [\"cheetah.jpg\"]}`. Each file will be a separate message that is added to your Chatbot history.\n\nYou can change the displayed text for each example by using the `example_labels` argument. You can add icons to each example as well using the `example_icons` argument. Both of these arguments take a list of strings, which should be the same length as the `examples` list.\n\nIf you'd like to cache the examples so that they are pre-computed and the results appear instantly, set `cache_examples=True`.\n\n**Customizing the chatbot or textbox component**\n\nIf you want to customize the `gr.Chatbot` or `gr.Textbox` that compose the `ChatInterface`, then you can pass in your own chatbot or textbox components. Here's an example of how we to apply the parameters we've discussed in this section:\n\n```python\nimport gradio as gr\n\ndef yes_man(message, history):\n    if message.endswith(\"?\"):\n        return \"Yes\"\n    else:\n        return \"Ask me anything", "heading1": "Customizing the Chat UI", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": " parameters we've discussed in this section:\n\n```python\nimport gradio as gr\n\ndef yes_man(message, history):\n    if message.endswith(\"?\"):\n        return \"Yes\"\n    else:\n        return \"Ask me anything!\"\n\ngr.ChatInterface(\n    yes_man,\n    type=\"messages\",\n    chatbot=gr.Chatbot(height=300),\n    textbox=gr.Textbox(placeholder=\"Ask me a yes or no question\", container=False, scale=7),\n    title=\"Yes Man\",\n    description=\"Ask Yes Man any question\",\n    theme=\"ocean\",\n    examples=[\"Hello\", \"Am I cool?\", \"Are tomatoes vegetables?\"],\n    cache_examples=True,\n).launch()\n```\n\nHere's another example that adds a \"placeholder\" for your chat interface, which appears before the user has started chatting. The `placeholder` argument of `gr.Chatbot` accepts Markdown or HTML:\n\n```python\ngr.ChatInterface(\n    yes_man,\n    type=\"messages\",\n    chatbot=gr.Chatbot(placeholder=\"<strong>Your Personal Yes-Man</strong><br>Ask Me Anything\"),\n...\n```\n\nThe placeholder appears vertically and horizontally centered in the chatbot.\n\n", "heading1": "Customizing the Chat UI", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "You may want to add multimodal capabilities to your chat interface. For example, you may want users to be able to upload images or files to your chatbot and ask questions about them. You can make your chatbot \"multimodal\" by passing in a single parameter (`multimodal=True`) to the `gr.ChatInterface` class.\n\nWhen `multimodal=True`, the signature of your chat function changes slightly: the first parameter of your function (what we referred to as `message` above) should accept a dictionary consisting of the submitted text and uploaded files that looks like this: \n\n```py\n{\n    \"text\": \"user input\", \n    \"files\": [\n        \"updated_file_1_path.ext\",\n        \"updated_file_2_path.ext\", \n        ...\n    ]\n}\n```\n\nThis second parameter of your chat function, `history`, will be in the same openai-style dictionary format as before. However, if the history contains uploaded files, the `content` key for a file will be not a string, but rather a single-element tuple consisting of the filepath. Each file will be a separate message in the history. So after uploading two files and asking a question, your history might look like this:\n\n```python\n[\n    {\"role\": \"user\", \"content\": (\"cat1.png\")},\n    {\"role\": \"user\", \"content\": (\"cat2.png\")},\n    {\"role\": \"user\", \"content\": \"What's the difference between these two images?\"},\n]\n```\n\nThe return type of your chat function does *not change* when setting `multimodal=True` (i.e. in the simplest case, you should still return a string value). We discuss more complex cases, e.g. returning files [below](returning-complex-responses).\n\nIf you are customizing a multimodal chat interface, you should pass in an instance of `gr.MultimodalTextbox` to the `textbox` parameter. You can customize the `MultimodalTextbox` further by passing in the `sources` parameter, which is a list of sources to enable. Here's an example that illustrates how to set up and customize and multimodal chat interface:\n \n\n```python\nimport gradio as gr\n\ndef count_images(message, hi", "heading1": "Multimodal Chat Interface", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "eter, which is a list of sources to enable. Here's an example that illustrates how to set up and customize and multimodal chat interface:\n \n\n```python\nimport gradio as gr\n\ndef count_images(message, history):\n    num_images = len(message[\"files\"])\n    total_images = 0\n    for message in history:\n        if isinstance(message[\"content\"], tuple):\n            total_images += 1\n    return f\"You just uploaded {num_images} images, total uploaded: {total_images+num_images}\"\n\ndemo = gr.ChatInterface(\n    fn=count_images, \n    type=\"messages\", \n    examples=[\n        {\"text\": \"No files\", \"files\": []}\n    ], \n    multimodal=True,\n    textbox=gr.MultimodalTextbox(file_count=\"multiple\", file_types=[\"image\"], sources=[\"upload\", \"microphone\"])\n)\n\ndemo.launch()\n```\n\n", "heading1": "Multimodal Chat Interface", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "You may want to add additional inputs to your chat function and expose them to your users through the chat UI. For example, you could add a textbox for a system prompt, or a slider that sets the number of tokens in the chatbot's response. The `gr.ChatInterface` class supports an `additional_inputs` parameter which can be used to add additional input components.\n\nThe `additional_inputs` parameters accepts a component or a list of components. You can pass the component instances directly, or use their string shortcuts (e.g. `\"textbox\"` instead of `gr.Textbox()`). If you pass in component instances, and they have _not_ already been rendered, then the components will appear underneath the chatbot within a `gr.Accordion()`. \n\nHere's a complete example:\n\n$code_chatinterface_system_prompt\n\nIf the components you pass into the `additional_inputs` have already been rendered in a parent `gr.Blocks()`, then they will _not_ be re-rendered in the accordion. This provides flexibility in deciding where to lay out the input components. In the example below, we position the `gr.Textbox()` on top of the Chatbot UI, while keeping the slider underneath.\n\n```python\nimport gradio as gr\nimport time\n\ndef echo(message, history, system_prompt, tokens):\n    response = f\"System prompt: {system_prompt}\\n Message: {message}.\"\n    for i in range(min(len(response), int(tokens))):\n        time.sleep(0.05)\n        yield response[: i+1]\n\nwith gr.Blocks() as demo:\n    system_prompt = gr.Textbox(\"You are helpful AI.\", label=\"System Prompt\")\n    slider = gr.Slider(10, 100, render=False)\n\n    gr.ChatInterface(\n        echo, additional_inputs=[system_prompt, slider], type=\"messages\"\n    )\n\ndemo.launch()\n```\n\n**Examples with additional inputs**\n\nYou can also add example values for your additional inputs. Pass in a list of lists to the `examples` parameter, where each inner list represents one sample, and each inner list should be `1 + len(additional_inputs)` long. The first element in the inner list should ", "heading1": "Additional Inputs", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "n a list of lists to the `examples` parameter, where each inner list represents one sample, and each inner list should be `1 + len(additional_inputs)` long. The first element in the inner list should be the example value for the chat message, and each subsequent element should be an example value for one of the additional inputs, in order. When additional inputs are provided, examples are rendered in a table underneath the chat interface.\n\nIf you need to create something even more custom, then its best to construct the chatbot UI using the low-level `gr.Blocks()` API. We have [a dedicated guide for that here](/guides/creating-a-custom-chatbot-with-blocks).\n\n", "heading1": "Additional Inputs", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "In the same way that you can accept additional inputs into your chat function, you can also return additional outputs. Simply pass in a list of components to the `additional_outputs` parameter in `gr.ChatInterface` and return additional values for each component from your chat function. Here's an example that extracts code and outputs it into a separate `gr.Code` component:\n\n$code_chatinterface_artifacts\n\n**Note:** unlike the case of additional inputs, the components passed in `additional_outputs` must be already defined in your `gr.Blocks` context -- they are not rendered automatically. If you need to render them after your `gr.ChatInterface`, you can set `render=False` when they are first defined and then `.render()` them in the appropriate section of your `gr.Blocks()` as we do in the example above.\n\n", "heading1": "Additional Outputs", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "We mentioned earlier that in the simplest case, your chat function should return a `str` response, which will be rendered as Markdown in the chatbot. However, you can also return more complex responses as we discuss below:\n\n\n**Returning files or Gradio components**\n\nCurrently, the following Gradio components can be displayed inside the chat interface:\n* `gr.Image`\n* `gr.Plot`\n* `gr.Audio`\n* `gr.HTML`\n* `gr.Video`\n* `gr.Gallery`\n* `gr.File`\n\nSimply return one of these components from your function to use it with `gr.ChatInterface`. Here's an example that returns an audio file:\n\n```py\nimport gradio as gr\n\ndef music(message, history):\n    if message.strip():\n        return gr.Audio(\"https://github.com/gradio-app/gradio/raw/main/test/test_files/audio_sample.wav\")\n    else:\n        return \"Please provide the name of an artist\"\n\ngr.ChatInterface(\n    music,\n    type=\"messages\",\n    textbox=gr.Textbox(placeholder=\"Which artist's music do you want to listen to?\", scale=7),\n).launch()\n```\n\nSimilarly, you could return image files with `gr.Image`, video files with `gr.Video`, or arbitrary files with the `gr.File` component.\n\n**Returning Multiple Messages**\n\nYou can return multiple assistant messages from your chat function simply by returning a `list` of messages, each of which is a valid chat type. This lets you, for example, send a message along with files, as in the following example:\n\n$code_chatinterface_echo_multimodal\n\n\n**Displaying intermediate thoughts or tool usage**\n\nThe `gr.ChatInterface` class supports displaying intermediate thoughts or tool usage direct in the chatbot.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/nested-thought.png)\n\n To do this, you will need to return a `gr.ChatMessage` object from your chat function. Here is the schema of the `gr.ChatMessage` data class as well as two internal typed dictionaries:\n \n ```py\n@dataclass\nclass ChatMessage:\n    content: str | Component\n    metadata: MetadataDict = ", "heading1": "Returning Complex Responses", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "ion. Here is the schema of the `gr.ChatMessage` data class as well as two internal typed dictionaries:\n \n ```py\n@dataclass\nclass ChatMessage:\n    content: str | Component\n    metadata: MetadataDict = None\n    options: list[OptionDict] = None\n\nclass MetadataDict(TypedDict):\n    title: NotRequired[str]\n    id: NotRequired[int | str]\n    parent_id: NotRequired[int | str]\n    log: NotRequired[str]\n    duration: NotRequired[float]\n    status: NotRequired[Literal[\"pending\", \"done\"]]\n\nclass OptionDict(TypedDict):\n    label: NotRequired[str]\n    value: str\n ```\n \nAs you can see, the `gr.ChatMessage` dataclass is similar to the openai-style message format, e.g. it has a \"content\" key that refers to the chat message content. But it also includes a \"metadata\" key whose value is a dictionary. If this dictionary includes a \"title\" key, the resulting message is displayed as an intermediate thought with the title being displayed on top of the thought. Here's an example showing the usage:\n\n$code_chatinterface_thoughts\n\nYou can even show nested thoughts, which is useful for agent demos in which one tool may call other tools. To display nested thoughts, include \"id\" and \"parent_id\" keys in the \"metadata\" dictionary. Read our [dedicated guide on displaying intermediate thoughts and tool usage](/guides/agents-and-tool-usage) for more realistic examples.\n\n**Providing preset responses**\n\nWhen returning an assistant message, you may want to provide preset options that a user can choose in response. To do this, again, you will again return a `gr.ChatMessage` instance from your chat function. This time, make sure to set the `options` key specifying the preset responses.\n\nAs shown in the schema for `gr.ChatMessage` above, the value corresponding to the `options` key should be a list of dictionaries, each with a `value` (a string that is the value that should be sent to the chat function when this response is clicked) and an optional `label` (if provided, is the text displayed as the preset r", "heading1": "Returning Complex Responses", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "ies, each with a `value` (a string that is the value that should be sent to the chat function when this response is clicked) and an optional `label` (if provided, is the text displayed as the preset response instead of the `value`). \n\nThis example illustrates how to use preset responses:\n\n$code_chatinterface_options\n\n", "heading1": "Returning Complex Responses", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "You may wish to modify the value of the chatbot with your own events, other than those prebuilt in the `gr.ChatInterface`. For example, you could create a dropdown that prefills the chat history with certain conversations or add a separate button to clear the conversation history. The `gr.ChatInterface` supports these events, but you need to use the `gr.ChatInterface.chatbot_value` as the input or output component in such events. In this example, we use a `gr.Radio` component to prefill the the chatbot with certain conversations:\n\n$code_chatinterface_prefill\n\n", "heading1": "Modifying the Chatbot Value Directly", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "Once you've built your Gradio chat interface and are hosting it on [Hugging Face Spaces](https://hf.space) or somewhere else, then you can query it with a simple API at the `/chat` endpoint. The endpoint just expects the user's message and will return the response, internally keeping track of the message history.\n\n![](https://github.com/gradio-app/gradio/assets/1778297/7b10d6db-6476-4e2e-bebd-ecda802c3b8f)\n\nTo use the endpoint, you should use either the [Gradio Python Client](/guides/getting-started-with-the-python-client) or the [Gradio JS client](/guides/getting-started-with-the-js-client). Or, you can deploy your Chat Interface to other platforms, such as a:\n\n* Discord bot [[tutorial]](../guides/creating-a-discord-bot-from-a-gradio-app)\n* Slack bot [[tutorial]](../guides/creating-a-slack-bot-from-a-gradio-app)\n* Website widget [[tutorial]](../guides/creating-a-website-widget-from-a-gradio-chatbot)\n\n", "heading1": "Using Your Chatbot via API", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "You can enable persistent chat history for your ChatInterface, allowing users to maintain multiple conversations and easily switch between them. When enabled, conversations are stored locally and privately in the user's browser using local storage. So if you deploy a ChatInterface e.g. on [Hugging Face Spaces](https://hf.space), each user will have their own separate chat history that won't interfere with other users' conversations. This means multiple users can interact with the same ChatInterface simultaneously while maintaining their own private conversation histories.\n\nTo enable this feature, simply set `gr.ChatInterface(save_history=True)` (as shown in the example in the next section). Users will then see their previous conversations in a side panel and can continue any previous chat or start a new one.\n\n", "heading1": "Chat History", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "To gather feedback on your chat model, set `gr.ChatInterface(flagging_mode=\"manual\")` and users will be able to thumbs-up or thumbs-down assistant responses. Each flagged response, along with the entire chat history, will get saved in a CSV file in the app working directory (this can be configured via the `flagging_dir` parameter). \n\nYou can also change the feedback options via `flagging_options` parameter. The default options are \"Like\" and \"Dislike\", which appear as the thumbs-up and thumbs-down icons. Any other options appear under a dedicated flag icon. This example shows a ChatInterface that has both chat history (mentioned in the previous section) and user feedback enabled:\n\n$code_chatinterface_streaming_echo\n\nNote that in this example, we set several flagging options: \"Like\", \"Spam\", \"Inappropriate\", \"Other\". Because the case-sensitive string \"Like\" is one of the flagging options, the user will see a thumbs-up icon next to each assistant message. The three other flagging options will appear in a dropdown under the flag icon.\n\n", "heading1": "Collecting User Feedback", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "Now that you've learned about the `gr.ChatInterface` class and how it can be used to create chatbot UIs quickly, we recommend reading one of the following:\n\n* [Our next Guide](../guides/chatinterface-examples) shows examples of how to use `gr.ChatInterface` with popular LLM libraries.\n* If you'd like to build very custom chat applications from scratch, you can build them using the low-level Blocks API, as [discussed in this Guide](../guides/creating-a-custom-chatbot-with-blocks).\n* Once you've deployed your Gradio Chat Interface, its easy to use in other applications because of the built-in API. Here's a tutorial on [how to deploy a Gradio chat interface as a Discord bot](../guides/creating-a-discord-bot-from-a-gradio-app).\n\n\n", "heading1": "What's Next?", "source_page_url": "https://gradio.app/guides/creating-a-chatbot-fast", "source_page_title": "Chatbots - Creating A Chatbot Fast Guide"}, {"text": "**Important Note**: if you are getting started, we recommend using the `gr.ChatInterface` to create chatbots -- its a high-level abstraction that makes it possible to create beautiful chatbot applications fast, often with a single line of code. [Read more about it here](/guides/creating-a-chatbot-fast).\n\nThis tutorial will show how to make chatbot UIs from scratch with Gradio's low-level Blocks API. This will give you full control over your Chatbot UI. You'll start by first creating a a simple chatbot to display text, a second one to stream text responses, and finally a chatbot that can handle media files as well. The chatbot interface that we create will look something like this:\n\n$demo_chatbot_streaming\n\n**Prerequisite**: We'll be using the `gradio.Blocks` class to build our Chatbot demo.\nYou can [read the Guide to Blocks first](https://gradio.app/blocks-and-event-listeners) if you are not already familiar with it. Also please make sure you are using the **latest version** version of Gradio: `pip install --upgrade gradio`.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "Let's start with recreating the simple demo above. As you may have noticed, our bot simply randomly responds \"How are you?\", \"Today is a great day\", or \"I'm very hungry\" to any input. Here's the code to create this with Gradio:\n\n$code_chatbot_simple\n\nThere are three Gradio components here:\n\n- A `Chatbot`, whose value stores the entire history of the conversation, as a list of response pairs between the user and bot.\n- A `Textbox` where the user can type their message, and then hit enter/submit to trigger the chatbot response\n- A `ClearButton` button to clear the Textbox and entire Chatbot history\n\nWe have a single function, `respond()`, which takes in the entire history of the chatbot, appends a random message, waits 1 second, and then returns the updated chat history. The `respond()` function also clears the textbox when it returns.\n\nOf course, in practice, you would replace `respond()` with your own more complex function, which might call a pretrained model or an API, to generate a response.\n\n$demo_chatbot_simple\n\nTip: For better type hinting and auto-completion in your IDE, you can use the `gr.ChatMessage` dataclass:\n\n```python\nfrom gradio import ChatMessage\n\ndef chat_function(message, history):\n    history.append(ChatMessage(role=\"user\", content=message))\n    history.append(ChatMessage(role=\"assistant\", content=\"Hello, how can I help you?\"))\n    return history\n```\n\n", "heading1": "A Simple Chatbot Demo", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "There are several ways we can improve the user experience of the chatbot above. First, we can stream responses so the user doesn't have to wait as long for a message to be generated. Second, we can have the user message appear immediately in the chat history, while the chatbot's response is being generated. Here's the code to achieve that:\n\n$code_chatbot_streaming\n\nYou'll notice that when a user submits their message, we now _chain_ two event events with `.then()`:\n\n1. The first method `user()` updates the chatbot with the user message and clears the input field. Because we want this to happen instantly, we set `queue=False`, which would skip any queue had it been enabled. The chatbot's history is appended with `{\"role\": \"user\", \"content\": user_message}`.\n\n2. The second method, `bot()` updates the chatbot history with the bot's response. Finally, we construct the message character by character and `yield` the intermediate outputs as they are being constructed. Gradio automatically turns any function with the `yield` keyword [into a streaming output interface](/guides/key-features/iterative-outputs).\n\n\nOf course, in practice, you would replace `bot()` with your own more complex function, which might call a pretrained model or an API, to generate a response.\n\n\n", "heading1": "Add Streaming to your Chatbot", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "The `gr.Chatbot` component supports a subset of markdown including bold, italics, and code. For example, we could write a function that responds to a user's message, with a bold **That's cool!**, like this:\n\n```py\ndef bot(history):\n    response = {\"role\": \"assistant\", \"content\": \"**That's cool!**\"}\n    history.append(response)\n    return history\n```\n\nIn addition, it can handle media files, such as images, audio, and video. You can use the `MultimodalTextbox` component to easily upload all types of media files to your chatbot. You can customize the `MultimodalTextbox` further by passing in the `sources` parameter, which is a list of sources to enable. To pass in a media file, we must pass in the file a dictionary with a `path` key pointing to a local file and an `alt_text` key. The `alt_text` is optional, so you can also just pass in a tuple with a single element `{\"path\": \"filepath\"}`, like this:\n\n```python\ndef add_message(history, message):\n    for x in message[\"files\"]:\n        history.append({\"role\": \"user\", \"content\": {\"path\": x}})\n    if message[\"text\"] is not None:\n        history.append({\"role\": \"user\", \"content\": message[\"text\"]})\n    return history, gr.MultimodalTextbox(value=None, interactive=False, file_types=[\"image\"], sources=[\"upload\", \"microphone\"])\n```\n\nPutting this together, we can create a _multimodal_ chatbot with a multimodal textbox for a user to submit text and media files. The rest of the code looks pretty much the same as before:\n\n$code_chatbot_multimodal\n$demo_chatbot_multimodal\n\nAnd you're done! That's all the code you need to build an interface for your chatbot model. Finally, we'll end our Guide with some links to Chatbots that are running on Spaces so that you can get an idea of what else is possible:\n\n- [project-baize/Baize-7B](https://huggingface.co/spaces/project-baize/Baize-7B): A stylized chatbot that allows you to stop generation as well as regenerate responses.\n- [MAGAer13/mPLUG-Owl](https://huggingface.co/spaces/MAGAer13/mPLUG-Ow", "heading1": "Adding Markdown, Images, Audio, or Videos", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "ingface.co/spaces/project-baize/Baize-7B): A stylized chatbot that allows you to stop generation as well as regenerate responses.\n- [MAGAer13/mPLUG-Owl](https://huggingface.co/spaces/MAGAer13/mPLUG-Owl): A multimodal chatbot that allows you to upvote and downvote responses.\n", "heading1": "Adding Markdown, Images, Audio, or Videos", "source_page_url": "https://gradio.app/guides/creating-a-custom-chatbot-with-blocks", "source_page_title": "Chatbots - Creating A Custom Chatbot With Blocks Guide"}, {"text": "The Discord bot will listen to messages mentioning it in channels. When it receives a message (which can include text as well as files), it will send it to your Gradio app via Gradio's built-in API. Your bot will reply with the response it receives from the API. \n\nBecause Gradio's API is very flexible, you can create Discord bots that support text, images, audio, streaming, chat history, and a wide variety of other features very easily. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-18%20at%204.26.55%E2%80%AFPM.gif)\n\n", "heading1": "How does it work?", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": "* Install the latest version of `gradio` and the `discord.py` libraries:\n\n```\npip install --upgrade gradio discord.py~=2.0\n```\n\n* Have a running Gradio app. This app can be running locally or on Hugging Face Spaces. In this example, we will be using the [Gradio Playground Space](https://huggingface.co/spaces/abidlabs/gradio-playground-bot), which takes in an image and/or text and generates the code to generate the corresponding Gradio app.\n\nNow, we are ready to get started!\n\n\n1. Create a Discord application\n\nFirst, go to the [Discord apps dashboard](https://discord.com/developers/applications). Look for the \"New Application\" button and click it. Give your application a name, and then click \"Create\".\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/discord-4.png)\n\nOn the resulting screen, you will see basic information about your application. Under the Settings section, click on the \"Bot\" option. You can update your bot's username if you would like.\n\nThen click on the \"Reset Token\" button. A new token will be generated. Copy it as we will need it for the next step.\n\nScroll down to the section that says \"Privileged Gateway Intents\". Your bot will need certain permissions to work correctly. In this tutorial, we will only be using the \"Message Content Intent\" so click the toggle to enable this intent. Save the changes.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/discord-3.png)\n\n\n\n2. Write a Discord bot\n\nLet's start by writing a very simple Discord bot, just to make sure that everything is working. Write the following Python code in a file called `bot.py`, pasting the discord bot token from the previous step:\n\n```python\nbot.py\nimport discord\n\nTOKEN = PASTE YOUR DISCORD BOT TOKEN HERE\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n    print(f'{client.user} has connected to Discord!')\n\nclient.run(TOKEN)\n```\n\nNow, run this file: `python bot.py`, w", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": "CORD BOT TOKEN HERE\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n    print(f'{client.user} has connected to Discord!')\n\nclient.run(TOKEN)\n```\n\nNow, run this file: `python bot.py`, which should run and print a message like:\n\n```text\nWe have logged in as GradioPlaygroundBot1451\n```\n\nIf that is working, we are ready to add Gradio-specific code. We will be using the [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) to query the Gradio Playground Space mentioned above. Here's the updated `bot.py` file:\n\n```python\nimport discord\nfrom gradio_client import Client, handle_file\nimport httpx\nimport os\n\nTOKEN = PASTE YOUR DISCORD BOT TOKEN HERE\n\nintents = discord.Intents.default()\nintents.message_content = True\n\nclient = discord.Client(intents=intents)\ngradio_client = Client(\"abidlabs/gradio-playground-bot\")\n\ndef download_image(attachment):\n    response = httpx.get(attachment.url)\n    image_path = f\"./images/{attachment.filename}\"\n    os.makedirs(\"./images\", exist_ok=True)\n    with open(image_path, \"wb\") as f:\n        f.write(response.content)\n    return image_path\n\n@client.event\nasync def on_ready():\n    print(f'We have logged in as {client.user}')\n\n@client.event\nasync def on_message(message):\n    Ignore messages from the bot itself\n    if message.author == client.user:\n        return\n\n    Check if the bot is mentioned in the message and reply\n    if client.user in message.mentions:\n        Extract the message content without the bot mention\n        clean_message = message.content.replace(f\"<@{client.user.id}>\", \"\").strip()\n\n        Handle images (only the first image is used)\n        files = []\n        if message.attachments:\n            for attachment in message.attachments:\n                if any(attachment.filename.lower().endswith(ext) for ext in ['png', 'jpg', 'jpeg', 'gif', 'webp']):\n                    image_path = download_image(attachment)\n                    files.append(handle_file(image_path))", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": ".filename.lower().endswith(ext) for ext in ['png', 'jpg', 'jpeg', 'gif', 'webp']):\n                    image_path = download_image(attachment)\n                    files.append(handle_file(image_path))\n                    break\n        \n        Stream the responses to the channel\n        for response in gradio_client.submit(\n            message={\"text\": clean_message, \"files\": files},\n        ):\n            await message.channel.send(response[-1])\n\nclient.run(TOKEN)\n```\n\n3. Add the bot to your Discord Server\n\nNow we are ready to install the bot on our server. Go back to the [Discord apps dashboard](https://discord.com/developers/applications). Under the Settings section, click on the \"OAuth2\" option. Scroll down to the \"OAuth2 URL Generator\" box and select the \"bot\" checkbox:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/discord-2.png)\n\n\n\nThen in \"Bot Permissions\" box that pops up underneath, enable the following permissions:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/discord-1.png)\n\n\nCopy the generated URL that appears underneath, which should look something like:\n\n```text\nhttps://discord.com/oauth2/authorize?client_id=1319011745452265575&permissions=377957238784&integration_type=0&scope=bot\n```\n\nPaste it into your browser, which should allow you to add the Discord bot to any Discord server that you manage.\n\n\n4. That's it!\n\nNow you can mention your bot from any channel in your Discord server, optionally attach an image, and it will respond with generated Gradio app code!\n\nThe bot will:\n1. Listen for mentions\n2. Process any attached images\n3. Send the text and images to your Gradio app\n4. Stream the responses back to the Discord channel\n\n This is just a basic example - you can extend it to handle more types of files, add error handling, or integrate with different Gradio apps.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": "c example - you can extend it to handle more types of files, add error handling, or integrate with different Gradio apps.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-18%20at%204.26.55%E2%80%AFPM.gif)\n\nIf you build a Discord bot from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gradio), and we are happy to help you amplify!", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-discord-bot-from-a-gradio-app", "source_page_title": "Chatbots - Creating A Discord Bot From A Gradio App Guide"}, {"text": "The chat widget appears as a small button in the corner of your website. When clicked, it opens a chat interface that communicates with your Gradio app via the JavaScript Client API. Users can ask questions and receive responses directly within the widget.\n\n\n", "heading1": "How does it work?", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": "* A running Gradio app (local or on Hugging Face Spaces). In this example, we'll use the [Gradio Playground Space](https://huggingface.co/spaces/abidlabs/gradio-playground-bot), which helps generate code for Gradio apps based on natural language descriptions.\n\n1. Create and Style the Chat Widget\n\nFirst, add this HTML and CSS to your website:\n\n```html\n<div id=\"chat-widget\" class=\"chat-widget\">\n    <button id=\"chat-toggle\" class=\"chat-toggle\">\ud83d\udcac</button>\n    <div id=\"chat-container\" class=\"chat-container hidden\">\n        <div id=\"chat-header\">\n            <h3>Gradio Assistant</h3>\n            <button id=\"close-chat\">\u00d7</button>\n        </div>\n        <div id=\"chat-messages\"></div>\n        <div id=\"chat-input-area\">\n            <input type=\"text\" id=\"chat-input\" placeholder=\"Ask a question...\">\n            <button id=\"send-message\">Send</button>\n        </div>\n    </div>\n</div>\n\n<style>\n.chat-widget {\n    position: fixed;\n    bottom: 20px;\n    right: 20px;\n    z-index: 1000;\n}\n\n.chat-toggle {\n    width: 50px;\n    height: 50px;\n    border-radius: 50%;\n    background: 007bff;\n    border: none;\n    color: white;\n    font-size: 24px;\n    cursor: pointer;\n}\n\n.chat-container {\n    position: fixed;\n    bottom: 80px;\n    right: 20px;\n    width: 300px;\n    height: 400px;\n    background: white;\n    border-radius: 10px;\n    box-shadow: 0 0 10px rgba(0,0,0,0.1);\n    display: flex;\n    flex-direction: column;\n}\n\n.chat-container.hidden {\n    display: none;\n}\n\nchat-header {\n    padding: 10px;\n    background: 007bff;\n    color: white;\n    border-radius: 10px 10px 0 0;\n    display: flex;\n    justify-content: space-between;\n    align-items: center;\n}\n\nchat-messages {\n    flex-grow: 1;\n    overflow-y: auto;\n    padding: 10px;\n}\n\nchat-input-area {\n    padding: 10px;\n    border-top: 1px solid eee;\n    display: flex;\n}\n\nchat-input {\n    flex-grow: 1;\n    padding: 8px;\n    border: 1px solid ddd;\n    border-radius: 4px;\n    margin-right: 8px;\n}\n\n.message {\n    margin: 8px 0;\n    pad", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": " solid eee;\n    display: flex;\n}\n\nchat-input {\n    flex-grow: 1;\n    padding: 8px;\n    border: 1px solid ddd;\n    border-radius: 4px;\n    margin-right: 8px;\n}\n\n.message {\n    margin: 8px 0;\n    padding: 8px;\n    border-radius: 4px;\n}\n\n.user-message {\n    background: e9ecef;\n    margin-left: 20px;\n}\n\n.bot-message {\n    background: f8f9fa;\n    margin-right: 20px;\n}\n</style>\n```\n\n2. Add the JavaScript\n\nThen, add the following JavaScript code (which uses the Gradio JavaScript Client to connect to the Space) to your website by including this in the `<head>` section of your website:\n\n```html\n<script type=\"module\">\n    import { Client } from \"https://cdn.jsdelivr.net/npm/@gradio/client/dist/index.min.js\";\n    \n    async function initChatWidget() {\n        const client = await Client.connect(\"https://abidlabs-gradio-playground-bot.hf.space\");\n        \n        const chatToggle = document.getElementById('chat-toggle');\n        const chatContainer = document.getElementById('chat-container');\n        const closeChat = document.getElementById('close-chat');\n        const chatInput = document.getElementById('chat-input');\n        const sendButton = document.getElementById('send-message');\n        const messagesContainer = document.getElementById('chat-messages');\n    \n        chatToggle.addEventListener('click', () => {\n            chatContainer.classList.remove('hidden');\n        });\n    \n        closeChat.addEventListener('click', () => {\n            chatContainer.classList.add('hidden');\n        });\n    \n        async function sendMessage() {\n            const userMessage = chatInput.value.trim();\n            if (!userMessage) return;\n\n            appendMessage(userMessage, 'user');\n            chatInput.value = '';\n\n            try {\n                const result = await client.predict(\"/chat\", {\n                    message: {\"text\": userMessage, \"files\": []}\n                });\n                const message = result.data[0];\n                console.log(result.data[0]", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": "client.predict(\"/chat\", {\n                    message: {\"text\": userMessage, \"files\": []}\n                });\n                const message = result.data[0];\n                console.log(result.data[0]);\n                const botMessage = result.data[0].join('\\n');\n                appendMessage(botMessage, 'bot');\n            } catch (error) {\n                console.error('Error:', error);\n                appendMessage('Sorry, there was an error processing your request.', 'bot');\n            }\n        }\n    \n        function appendMessage(text, sender) {\n            const messageDiv = document.createElement('div');\n            messageDiv.className = `message ${sender}-message`;\n            \n            if (sender === 'bot') {\n                messageDiv.innerHTML = marked.parse(text);\n            } else {\n                messageDiv.textContent = text;\n            }\n            \n            messagesContainer.appendChild(messageDiv);\n            messagesContainer.scrollTop = messagesContainer.scrollHeight;\n        }\n    \n        sendButton.addEventListener('click', sendMessage);\n        chatInput.addEventListener('keypress', (e) => {\n            if (e.key === 'Enter') sendMessage();\n        });\n    }\n    \n    initChatWidget();\n</script>\n```\n\n3. That's it!\n\nYour website now has a chat widget that connects to your Gradio app! Users can click the chat button to open the widget and start interacting with your app.\n\nCustomization\n\nYou can customize the appearance of the widget by modifying the CSS. Some ideas:\n- Change the colors to match your website's theme\n- Adjust the size and position of the widget\n- Add animations for opening/closing\n- Modify the message styling\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/Screen%20Recording%202024-12-19%20at%203.32.46%E2%80%AFPM.gif)\n\nIf you build a website widget from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gradio), and we are hap", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": "%20Recording%202024-12-19%20at%203.32.46%E2%80%AFPM.gif)\n\nIf you build a website widget from a Gradio app, feel free to share it on X and tag [the Gradio account](https://x.com/Gradio), and we are happy to help you amplify!", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/creating-a-website-widget-from-a-gradio-chatbot", "source_page_title": "Chatbots - Creating A Website Widget From A Gradio Chatbot Guide"}, {"text": "Install the @gradio/client package to interact with Gradio APIs using Node.js version >=18.0.0 or in browser-based projects. Use npm or any compatible package manager:\n\n```bash\nnpm i @gradio/client\n```\n\nThis command adds @gradio/client to your project dependencies, allowing you to import it in your JavaScript or TypeScript files.\n\n", "heading1": "Installation via npm", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "For quick addition to your web project, you can use the jsDelivr CDN to load the latest version of @gradio/client directly into your HTML:\n\n```html\n<script type=\"module\">\n\timport { Client } from \"https://cdn.jsdelivr.net/npm/@gradio/client/dist/index.min.js\";\n\t...\n</script>\n```\n\nBe sure to add this to the `<head>` of your HTML. This will install the latest version but we advise hardcoding the version in production. You can find all available versions [here](https://www.jsdelivr.com/package/npm/@gradio/client). This approach is ideal for experimental or prototying purposes, though has some limitations. A complete example would look like this:\n\n```html\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n    <script type=\"module\">\n        import { Client } from \"https://cdn.jsdelivr.net/npm/@gradio/client/dist/index.min.js\";\n        const client = await Client.connect(\"abidlabs/en2fr\");\n        const result = await client.predict(\"/predict\", {\n            text: \"My name is Hannah\"\n        });\n        console.log(result);\n    </script>\n</head>\n</html>\n```\n\n", "heading1": "Installation via CDN", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Start by connecting instantiating a `client` instance and connecting it to a Gradio app that is running on Hugging Face Spaces or generally anywhere on the web.\n\n", "heading1": "Connecting to a running Gradio App", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\"); // a Space that translates from English to French\n```\n\nYou can also connect to private Spaces by passing in your HF token with the `hf_token` property of the options parameter. You can get your HF token here: https://huggingface.co/settings/tokens\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/my-private-space\", { hf_token: \"hf_...\" })\n```\n\n", "heading1": "Connecting to a Hugging Face Space", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "While you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space, and then use it to make as many requests as you'd like! You'll need to pass in your [Hugging Face token](https://huggingface.co/settings/tokens)).\n\n`Client.duplicate` is almost identical to `Client.connect`, the only difference is under the hood:\n\n```js\nimport { Client, handle_file } from \"@gradio/client\";\n\nconst response = await fetch(\n\t\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\"\n);\nconst audio_file = await response.blob();\n\nconst app = await Client.duplicate(\"abidlabs/whisper\", { hf_token: \"hf_...\" });\nconst transcription = await app.predict(\"/predict\", [handle_file(audio_file)]);\n```\n\nIf you have previously duplicated a Space, re-running `Client.duplicate` will _not_ create a new Space. Instead, the client will attach to the previously-created Space. So it is safe to re-run the `Client.duplicate` method multiple times with the same space.\n\n**Note:** if the original Space uses GPUs, your private Space will as well, and your Hugging Face account will get billed based on the price of the GPU. To minimize charges, your Space will automatically go to sleep after 5 minutes of inactivity. You can also set the hardware using the `hardware` and `timeout` properties of `duplicate`'s options object like this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.duplicate(\"abidlabs/whisper\", {\n\thf_token: \"hf_...\",\n\ttimeout: 60,\n\thardware: \"a10g-small\"\n});\n```\n\n", "heading1": "Duplicating a Space for private use", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If your app is running somewhere else, just provide the full URL instead, including the \"http://\" or \"https://\". Here's an example of making predictions to a Gradio app that is running on a share URL:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = Client.connect(\"https://bec81a83-5b5c-471e.gradio.live\");\n```\n\n", "heading1": "Connecting a general Gradio app", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If the Gradio application you are connecting to [requires a username and password](/guides/sharing-your-appauthentication), then provide them as a tuple to the `auth` argument of the `Client` class:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nClient.connect(\n  space_name,\n  { auth: [username, password] }\n)\n```\n\n\n", "heading1": "Connecting to a Gradio app with auth", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are available to you by calling the `Client`'s `view_api` method.\n\nFor the Whisper Space, we can do this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/whisper\");\n\nconst app_info = await app.view_api();\n\nconsole.log(app_info);\n```\n\nAnd we will see the following:\n\n```json\n{\n\t\"named_endpoints\": {\n\t\t\"/predict\": {\n\t\t\t\"parameters\": [\n\t\t\t\t{\n\t\t\t\t\t\"label\": \"text\",\n\t\t\t\t\t\"component\": \"Textbox\",\n\t\t\t\t\t\"type\": \"string\"\n\t\t\t\t}\n\t\t\t],\n\t\t\t\"returns\": [\n\t\t\t\t{\n\t\t\t\t\t\"label\": \"output\",\n\t\t\t\t\t\"component\": \"Textbox\",\n\t\t\t\t\t\"type\": \"string\"\n\t\t\t\t}\n\t\t\t]\n\t\t}\n\t},\n\t\"unnamed_endpoints\": {}\n}\n```\n\nThis shows us that we have 1 API endpoint in this space, and shows us how to use the API endpoint to make a prediction: we should call the `.predict()` method (which we will explore below), providing a parameter `input_audio` of type `string`, which is a url to a file.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()` method. Although this isn't necessary if a Gradio app has only 1 named endpoint, it does allow us to call different endpoints in a single app if they are available. If an app has unnamed API endpoints, these can also be displayed by running `.view_api(all_endpoints=True)`.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the \"Use via API\" link in the footer of the Gradio app, which shows us the same information, along with example usage. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \"API Recorder\" that lets you interact with the Gradio UI normally and converts your interactions into the corresponding code to run with the JS Client.\n\n\n", "heading1": "The \"View API\" Page", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()` method with the appropriate arguments:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst result = await app.predict(\"/predict\", [\"Hello\"]);\n```\n\nIf there are multiple parameters, then you should pass them as an array to `.predict()`, like this:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/calculator\");\nconst result = await app.predict(\"/predict\", [4, \"add\", 5]);\n```\n\nFor certain inputs, such as images, you should pass in a `Buffer`, `Blob` or `File` depending on what is most convenient. In node, this would be a `Buffer` or `Blob`; in a browser environment, this would be a `Blob` or `File`.\n\n```js\nimport { Client, handle_file } from \"@gradio/client\";\n\nconst response = await fetch(\n\t\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\"\n);\nconst audio_file = await response.blob();\n\nconst app = await Client.connect(\"abidlabs/whisper\");\nconst result = await app.predict(\"/predict\", [handle_file(audio_file)]);\n```\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "If the API you are working with can return results over time, or you wish to access information about the status of a job, you can use the iterable interface for more flexibility. This is especially useful for iterative endpoints or generator endpoints that will produce a series of values over time as discrete responses.\n\n```js\nimport { Client } from \"@gradio/client\";\n\nfunction log_result(payload) {\n\tconst {\n\t\tdata: [translation]\n\t} = payload;\n\n\tconsole.log(`The translated result is: ${translation}`);\n}\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst job = app.submit(\"/predict\", [\"Hello\"]);\n\nfor await (const message of job) {\n\tlog_result(message);\n}\n```\n\n", "heading1": "Using events", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The event interface also allows you to get the status of the running job by instantiating the client with the `events` options passing `status` and `data` as an array:\n\n\n```ts\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\", {\n\tevents: [\"status\", \"data\"]\n});\n```\n\nThis ensures that status messages are also reported to the client.\n\n`status`es are returned as an object with the following attributes: `status` (a human readbale status of the current job, `\"pending\" | \"generating\" | \"complete\" | \"error\"`), `code` (the detailed gradio code for the job), `position` (the current position of this job in the queue), `queue_size` (the total queue size), `eta` (estimated time this job will complete), `success` (a boolean representing whether the job completed successfully), and `time` ( as `Date` object detailing the time that the status was generated).\n\n```js\nimport { Client } from \"@gradio/client\";\n\nfunction log_status(status) {\n\tconsole.log(\n\t\t`The current status for this job is: ${JSON.stringify(status, null, 2)}.`\n\t);\n}\n\nconst app = await Client.connect(\"abidlabs/en2fr\", {\n\tevents: [\"status\", \"data\"]\n});\nconst job = app.submit(\"/predict\", [\"Hello\"]);\n\nfor await (const message of job) {\n\tif (message.type === \"status\") {\n\t\tlog_status(message);\n\t}\n}\n```\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "The job instance also has a `.cancel()` method that cancels jobs that have been queued but not started. For example, if you run:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"abidlabs/en2fr\");\nconst job_one = app.submit(\"/predict\", [\"Hello\"]);\nconst job_two = app.submit(\"/predict\", [\"Friends\"]);\n\njob_one.cancel();\njob_two.cancel();\n```\n\nIf the first job has started processing, then it will not be canceled but the client will no longer listen for updates (throwing away the job). If the second job has not yet started, it will be successfully canceled and removed from the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a series of values. You can listen for these values in real time using the iterable interface:\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/count_generator\");\nconst job = app.submit(0, [9]);\n\nfor await (const message of job) {\n\tconsole.log(message.data);\n}\n```\n\nThis will log out the values as they are generated by the endpoint.\n\nYou can also cancel jobs that that have iterative outputs, in which case the job will finish immediately.\n\n```js\nimport { Client } from \"@gradio/client\";\n\nconst app = await Client.connect(\"gradio/count_generator\");\nconst job = app.submit(0, [9]);\n\nfor await (const message of job) {\n\tconsole.log(message.data);\n}\n\nsetTimeout(() => {\n\tjob.cancel();\n}, 3000);\n```\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-js-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Js Client Guide"}, {"text": "Let's start with what seems like the most complex bit -- using machine learning to remove the music from a video.\n\nLuckily for us, there's an existing Space we can use to make this process easier: [https://huggingface.co/spaces/abidlabs/music-separation](https://huggingface.co/spaces/abidlabs/music-separation). This Space takes an audio file and produces two separate audio files: one with the instrumental music and one with all other sounds in the original clip. Perfect to use with our client!\n\nOpen a new Python file, say `main.py`, and start by importing the `Client` class from `gradio_client` and connecting it to this Space:\n\n```py\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/music-separation\")\n\ndef acapellify(audio_path):\n    result = client.predict(handle_file(audio_path), api_name=\"/predict\")\n    return result[0]\n```\n\nThat's all the code that's needed -- notice that the API endpoints returns two audio files (one without the music, and one with just the music) in a list, and so we just return the first element of the list.\n\n---\n\n**Note**: since this is a public Space, there might be other users using this Space as well, which might result in a slow experience. You can duplicate this Space with your own [Hugging Face token](https://huggingface.co/settings/tokens) and create a private Space that only you have will have access to and bypass the queue. To do that, simply replace the first two lines above with:\n\n```py\nfrom gradio_client import Client\n\nclient = Client.duplicate(\"abidlabs/music-separation\", hf_token=YOUR_HF_TOKEN)\n```\n\nEverything else remains the same!\n\n---\n\nNow, of course, we are working with video files, so we first need to extract the audio from the video files. For this, we will be using the `ffmpeg` library, which does a lot of heavy lifting when it comes to working with audio and video files. The most common way to use `ffmpeg` is through the command line, which we'll call via Python's `subprocess` module:\n\nOur video p", "heading1": "Step 1: Write the Video Processing Function", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "t of heavy lifting when it comes to working with audio and video files. The most common way to use `ffmpeg` is through the command line, which we'll call via Python's `subprocess` module:\n\nOur video processing workflow will consist of three steps:\n\n1. First, we start by taking in a video filepath and extracting the audio using `ffmpeg`.\n2. Then, we pass in the audio file through the `acapellify()` function above.\n3. Finally, we combine the new audio with the original video to produce a final acapellified video.\n\nHere's the complete code in Python, which you can add to your `main.py` file:\n\n```python\nimport subprocess\n\ndef process_video(video_path):\n    old_audio = os.path.basename(video_path).split(\".\")[0] + \".m4a\"\n    subprocess.run(['ffmpeg', '-y', '-i', video_path, '-vn', '-acodec', 'copy', old_audio])\n\n    new_audio = acapellify(old_audio)\n\n    new_video = f\"acap_{video_path}\"\n    subprocess.call(['ffmpeg', '-y', '-i', video_path, '-i', new_audio, '-map', '0:v', '-map', '1:a', '-c:v', 'copy', '-c:a', 'aac', '-strict', 'experimental', f\"static/{new_video}\"])\n    return new_video\n```\n\nYou can read up on [ffmpeg documentation](https://ffmpeg.org/ffmpeg.html) if you'd like to understand all of the command line parameters, as they are beyond the scope of this tutorial.\n\n", "heading1": "Step 1: Write the Video Processing Function", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Next up, we'll create a simple FastAPI app. If you haven't used FastAPI before, check out [the great FastAPI docs](https://fastapi.tiangolo.com/). Otherwise, this basic template, which we add to `main.py`, will look pretty familiar:\n\n```python\nimport os\nfrom fastapi import FastAPI, File, UploadFile, Request\nfrom fastapi.responses import HTMLResponse, RedirectResponse\nfrom fastapi.staticfiles import StaticFiles\nfrom fastapi.templating import Jinja2Templates\n\napp = FastAPI()\nos.makedirs(\"static\", exist_ok=True)\napp.mount(\"/static\", StaticFiles(directory=\"static\"), name=\"static\")\ntemplates = Jinja2Templates(directory=\"templates\")\n\nvideos = []\n\n@app.get(\"/\", response_class=HTMLResponse)\nasync def home(request: Request):\n    return templates.TemplateResponse(\n        \"home.html\", {\"request\": request, \"videos\": videos})\n\n@app.post(\"/uploadvideo/\")\nasync def upload_video(video: UploadFile = File(...)):\n    video_path = video.filename\n    with open(video_path, \"wb+\") as fp:\n        fp.write(video.file.read())\n\n    new_video = process_video(video.filename)\n    videos.append(new_video)\n    return RedirectResponse(url='/', status_code=303)\n```\n\nIn this example, the FastAPI app has two routes: `/` and `/uploadvideo/`.\n\nThe `/` route returns an HTML template that displays a gallery of all uploaded videos.\n\nThe `/uploadvideo/` route accepts a `POST` request with an `UploadFile` object, which represents the uploaded video file. The video file is \"acapellified\" via the `process_video()` method, and the output video is stored in a list which stores all of the uploaded videos in memory.\n\nNote that this is a very basic example and if this were a production app, you will need to add more logic to handle file storage, user authentication, and security considerations.\n\n", "heading1": "Step 2: Create a FastAPI app (Backend Routes)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Finally, we create the frontend of our web application. First, we create a folder called `templates` in the same directory as `main.py`. We then create a template, `home.html` inside the `templates` folder. Here is the resulting file structure:\n\n```csv\n\u251c\u2500\u2500 main.py\n\u251c\u2500\u2500 templates\n\u2502   \u2514\u2500\u2500 home.html\n```\n\nWrite the following as the contents of `home.html`:\n\n```html\n&lt;!DOCTYPE html> &lt;html> &lt;head> &lt;title>Video Gallery&lt;/title>\n&lt;style> body { font-family: sans-serif; margin: 0; padding: 0;\nbackground-color: f5f5f5; } h1 { text-align: center; margin-top: 30px;\nmargin-bottom: 20px; } .gallery { display: flex; flex-wrap: wrap;\njustify-content: center; gap: 20px; padding: 20px; } .video { border: 2px solid\nccc; box-shadow: 0px 0px 10px rgba(0, 0, 0, 0.2); border-radius: 5px; overflow:\nhidden; width: 300px; margin-bottom: 20px; } .video video { width: 100%; height:\n200px; } .video p { text-align: center; margin: 10px 0; } form { margin-top:\n20px; text-align: center; } input[type=\"file\"] { display: none; } .upload-btn {\ndisplay: inline-block; background-color: 3498db; color: fff; padding: 10px\n20px; font-size: 16px; border: none; border-radius: 5px; cursor: pointer; }\n.upload-btn:hover { background-color: 2980b9; } .file-name { margin-left: 10px;\n} &lt;/style> &lt;/head> &lt;body> &lt;h1>Video Gallery&lt;/h1> {% if videos %}\n&lt;div class=\"gallery\"> {% for video in videos %} &lt;div class=\"video\">\n&lt;video controls> &lt;source src=\"{{ url_for('static', path=video) }}\"\ntype=\"video/mp4\"> Your browser does not support the video tag. &lt;/video>\n&lt;p>{{ video }}&lt;/p> &lt;/div> {% endfor %} &lt;/div> {% else %} &lt;p>No\nvideos uploaded yet.&lt;/p> {% endif %} &lt;form action=\"/uploadvideo/\"\nmethod=\"post\" enctype=\"multipart/form-data\"> &lt;label for=\"video-upload\"\nclass=\"upload-btn\">Choose video file&lt;/label> &lt;input type=\"file\"\nname=\"video\" id=\"video-upload\"> &lt;span class=\"file-name\">&lt;/span> &lt;button\ntype=\"submit\" class=\"upload-btn\">Upload&lt;/butto", "heading1": "Step 3: Create a FastAPI app (Frontend Template)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "class=\"upload-btn\">Choose video file&lt;/label> &lt;input type=\"file\"\nname=\"video\" id=\"video-upload\"> &lt;span class=\"file-name\">&lt;/span> &lt;button\ntype=\"submit\" class=\"upload-btn\">Upload&lt;/button> &lt;/form> &lt;script> //\nDisplay selected file name in the form const fileUpload =\ndocument.getElementById(\"video-upload\"); const fileName =\ndocument.querySelector(\".file-name\"); fileUpload.addEventListener(\"change\", (e)\n=> { fileName.textContent = e.target.files[0].name; }); &lt;/script> &lt;/body>\n&lt;/html>\n```\n\n", "heading1": "Step 3: Create a FastAPI app (Frontend Template)", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "Finally, we are ready to run our FastAPI app, powered by the Gradio Python Client!\n\nOpen up a terminal and navigate to the directory containing `main.py`. Then run the following command in the terminal:\n\n```bash\n$ uvicorn main:app\n```\n\nYou should see an output that looks like this:\n\n```csv\nLoaded as API: https://abidlabs-music-separation.hf.space \u2714\nINFO:     Started server process [1360]\nINFO:     Waiting for application startup.\nINFO:     Application startup complete.\nINFO:     Uvicorn running on http://127.0.0.1:8000 (Press CTRL+C to quit)\n```\n\nAnd that's it! Start uploading videos and you'll get some \"acapellified\" videos in response (might take seconds to minutes to process depending on the length of your videos). Here's how the UI looks after uploading two videos:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/acapellify.png)\n\nIf you'd like to learn more about how to use the Gradio Python Client in your projects, [read the dedicated Guide](/guides/getting-started-with-the-python-client/).\n", "heading1": "Step 4: Run your FastAPI app", "source_page_url": "https://gradio.app/guides/fastapi-app-with-the-gradio-client", "source_page_title": "Gradio Clients And Lite - Fastapi App With The Gradio Client Guide"}, {"text": "`@gradio/lite` is a JavaScript library that enables you to run Gradio applications directly within your web browser. It achieves this by utilizing Pyodide, a Python runtime for WebAssembly, which allows Python code to be executed in the browser environment. With `@gradio/lite`, you can **write regular Python code for your Gradio applications**, and they will **run seamlessly in the browser** without the need for server-side infrastructure.\n\n", "heading1": "What is `@gradio/lite`?", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "Let's build a \"Hello World\" Gradio app in `@gradio/lite`\n\n\n1. Import JS and CSS\n\nStart by creating a new HTML file, if you don't have one already. Importing the JavaScript and CSS corresponding to the `@gradio/lite` package by using the following code:\n\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n</html>\n```\n\nNote that you should generally use the latest version of `@gradio/lite` that is available. You can see the [versions available here](https://www.jsdelivr.com/package/npm/@gradio/lite?tab=files).\n\n2. Create the `<gradio-lite>` tags\n\nSomewhere in the body of your HTML page (wherever you'd like the Gradio app to be rendered), create opening and closing `<gradio-lite>` tags.\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nNote: you can add the `theme` attribute to the `<gradio-lite>` tag to force the theme to be dark or light (by default, it respects the system theme). E.g.\n\n```html\n<gradio-lite theme=\"dark\">\n...\n</gradio-lite>\n```\n\n3. Write your Gradio app inside of the tags\n\nNow, write your Gradio app as you would normally, in Python! Keep in mind that since this is Python, whitespace and indentations matter.\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\n\t\timport gradio as gr\n\n\t\tdef greet(name):\n\t\t\treturn \"Hello, \" + name + \"!\"\n\n\t\tgr.Interface(greet, \"textbox\", \"textbox\").launch()\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nAn", "heading1": "Getting Started", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "head>\n\t<body>\n\t\t<gradio-lite>\n\t\timport gradio as gr\n\n\t\tdef greet(name):\n\t\t\treturn \"Hello, \" + name + \"!\"\n\n\t\tgr.Interface(greet, \"textbox\", \"textbox\").launch()\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nAnd that's it! You should now be able to open your HTML page in the browser and see the Gradio app rendered! Note that it may take a little while for the Gradio app to load initially since Pyodide can take a while to install in your browser.\n\n**Note on debugging**: to see any errors in your Gradio-lite application, open the inspector in your web browser. All errors (including Python errors) will be printed there.\n\n", "heading1": "Getting Started", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "What if you want to create a Gradio app that spans multiple files? Or that has custom Python requirements? Both are possible with `@gradio/lite`!\n\nMultiple Files\n\nAdding multiple files within a `@gradio/lite` app is very straightforward: use the `<gradio-file>` tag. You can have as many `<gradio-file>` tags as you want, but each one needs to have a `name` attribute and the entry point to your Gradio app should have the `entrypoint` attribute.\n\nHere's an example:\n\n```html\n<gradio-lite>\n\n<gradio-file name=\"app.py\" entrypoint>\nimport gradio as gr\nfrom utils import add\n\ndemo = gr.Interface(fn=add, inputs=[\"number\", \"number\"], outputs=\"number\")\n\ndemo.launch()\n</gradio-file>\n\n<gradio-file name=\"utils.py\" >\ndef add(a, b):\n\treturn a + b\n</gradio-file>\n\n</gradio-lite>\n\n```\n\nAdditional Requirements\n\nIf your Gradio app has additional requirements, it is usually possible to [install them in the browser using micropip](https://pyodide.org/en/stable/usage/loading-packages.htmlloading-packages). We've created a wrapper to make this paticularly convenient: simply list your requirements in the same syntax as a `requirements.txt` and enclose them with `<gradio-requirements>` tags.\n\nHere, we install `transformers_js_py` to run a text classification model directly in the browser!\n\n```html\n<gradio-lite>\n\n<gradio-requirements>\ntransformers_js_py\n</gradio-requirements>\n\n<gradio-file name=\"app.py\" entrypoint>\nfrom transformers_js import import_transformers_js\nimport gradio as gr\n\ntransformers = await import_transformers_js()\npipeline = transformers.pipeline\npipe = await pipeline('sentiment-analysis')\n\nasync def classify(text):\n\treturn await pipe(text)\n\ndemo = gr.Interface(classify, \"textbox\", \"json\")\ndemo.launch()\n</gradio-file>\n\n</gradio-lite>\n\n```\n\n**Try it out**: You can see this example running in [this Hugging Face Static Space](https://huggingface.co/spaces/abidlabs/gradio-lite-classify), which lets you host static (serverless) web applications for free. Visit the page and y", "heading1": "More Examples: Adding Additional Files and Requirements", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "xample running in [this Hugging Face Static Space](https://huggingface.co/spaces/abidlabs/gradio-lite-classify), which lets you host static (serverless) web applications for free. Visit the page and you'll be able to run a machine learning model without internet access!\n\nSharedWorker mode\n\nBy default, Gradio-Lite executes Python code in a [Web Worker](https://developer.mozilla.org/en-US/docs/Web/API/Web_Workers_API) with [Pyodide](https://pyodide.org/) runtime, and each Gradio-Lite app has its own worker.\nIt has some benefits such as environment isolation.\n\nHowever, when there are many Gradio-Lite apps in the same page, it may cause performance issues such as high memory usage because each app has its own worker and Pyodide runtime.\nIn such cases, you can use the **SharedWorker mode** to share a single Pyodide runtime in a [SharedWorker](https://developer.mozilla.org/en-US/docs/Web/API/SharedWorker) among multiple Gradio-Lite apps. To enable the SharedWorker mode, set the `shared-worker` attribute to the `<gradio-lite>` tag.\n\n```html\n<!-- These two Gradio-Lite apps share a single worker -->\n\n<gradio-lite shared-worker>\nimport gradio as gr\n...\n</gradio-lite>\n\n<gradio-lite shared-worker>\nimport gradio as gr\n...\n</gradio-lite>\n```\n\nWhen using the SharedWorker mode, you should be aware of the following points:\n* The apps share the same Python environment, which means that they can access the same  modules and objects. If, for example, one app makes changes to some modules, the changes will be visible to other apps.\n* The file system is shared among the apps, while each app's files are mounted in each home directory, so each app can access the files of other apps.\n\nCode and Demo Playground\n\nIf you'd like to see the code side-by-side with the demo just pass in the `playground` attribute to the gradio-lite element. This will create an interactive playground that allows you to change the code and update the demo! If you're using playground, you can also set layo", "heading1": "More Examples: Adding Additional Files and Requirements", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": " `playground` attribute to the gradio-lite element. This will create an interactive playground that allows you to change the code and update the demo! If you're using playground, you can also set layout to either 'vertical' or 'horizontal' which will determine if the code editor and preview are side-by-side or on top of each other (by default it's reposnsive with the width of the page).\n\n```html\n<gradio-lite playground layout=\"horizontal\">\nimport gradio as gr\n\ngr.Interface(fn=lambda x: x,\n\t\t\tinputs=gr.Textbox(),\n\t\t\toutputs=gr.Textbox()\n\t\t).launch()\n</gradio-lite>\n```\n\n", "heading1": "More Examples: Adding Additional Files and Requirements", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "1. Serverless Deployment\nThe primary advantage of @gradio/lite is that it eliminates the need for server infrastructure. This simplifies deployment, reduces server-related costs, and makes it easier to share your Gradio applications with others.\n\n2. Low Latency\nBy running in the browser, @gradio/lite offers low-latency interactions for users. There's no need for data to travel to and from a server, resulting in faster responses and a smoother user experience.\n\n3. Privacy and Security\nSince all processing occurs within the user's browser, `@gradio/lite` enhances privacy and security. User data remains on their device, providing peace of mind regarding data handling.\n\nLimitations\n\n* Currently, the biggest limitation in using `@gradio/lite` is that your Gradio apps will generally take more time (usually 5-15 seconds) to load initially in the browser. This is because the browser needs to load the Pyodide runtime before it can render Python code.\n\n* Not every Python package is supported by Pyodide. While `gradio` and many other popular packages (including `numpy`, `scikit-learn`, and `transformers-js`) can be installed in Pyodide, if your app has many dependencies, its worth checking whether whether the dependencies are included in Pyodide, or can be [installed with `micropip`](https://micropip.pyodide.org/en/v0.2.2/project/api.htmlmicropip.install).\n\n", "heading1": "Benefits of Using `@gradio/lite`", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "You can immediately try out `@gradio/lite` by copying and pasting this code in a local `index.html` file and opening it with your browser:\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\n\t\timport gradio as gr\n\n\t\tdef greet(name):\n\t\t\treturn \"Hello, \" + name + \"!\"\n\n\t\tgr.Interface(greet, \"textbox\", \"textbox\").launch()\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\n\nWe've also created a playground on the Gradio website that allows you to interactively edit code and see the results immediately!\n\nPlayground: https://www.gradio.app/playground\n", "heading1": "Try it out!", "source_page_url": "https://gradio.app/guides/gradio-lite", "source_page_title": "Gradio Clients And Lite - Gradio Lite Guide"}, {"text": "If you already have a recent version of `gradio`, then the `gradio_client` is included as a dependency. But note that this documentation reflects the latest version of the `gradio_client`, so upgrade if you're not sure!\n\nThe lightweight `gradio_client` package can be installed from pip (or pip3) and is tested to work with **Python versions 3.10 or higher**:\n\n```bash\n$ pip install --upgrade gradio_client\n```\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Start by connecting instantiating a `Client` object and connecting it to a Gradio app that is running on Hugging Face Spaces.\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/en2fr\")  a Space that translates from English to French\n```\n\nYou can also connect to private Spaces by passing in your HF token with the `hf_token` parameter. You can get your HF token here: https://huggingface.co/settings/tokens\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/my-private-space\", hf_token=\"...\")\n```\n\n\n", "heading1": "Connecting to a Gradio App on Hugging Face Spaces", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "While you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space,\nand then use it to make as many requests as you'd like!\n\nThe `gradio_client` includes a class method: `Client.duplicate()` to make this process simple (you'll need to pass in your [Hugging Face token](https://huggingface.co/settings/tokens) or be logged in using the Hugging Face CLI):\n\n```python\nimport os\nfrom gradio_client import Client, handle_file\n\nHF_TOKEN = os.environ.get(\"HF_TOKEN\")\n\nclient = Client.duplicate(\"abidlabs/whisper\", hf_token=HF_TOKEN)\nclient.predict(handle_file(\"audio_sample.wav\"))\n\n>> \"This is a test of the whisper speech recognition model.\"\n```\n\nIf you have previously duplicated a Space, re-running `duplicate()` will _not_ create a new Space. Instead, the Client will attach to the previously-created Space. So it is safe to re-run the `Client.duplicate()` method multiple times.\n\n**Note:** if the original Space uses GPUs, your private Space will as well, and your Hugging Face account will get billed based on the price of the GPU. To minimize charges, your Space will automatically go to sleep after 1 hour of inactivity. You can also set the hardware using the `hardware` parameter of `duplicate()`.\n\n", "heading1": "Duplicating a Space for private use", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "If your app is running somewhere else, just provide the full URL instead, including the \"http://\" or \"https://\". Here's an example of making predictions to a Gradio app that is running on a share URL:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"https://bec81a83-5b5c-471e.gradio.live\")\n```\n\n", "heading1": "Connecting a general Gradio app", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "If the Gradio application you are connecting to [requires a username and password](/guides/sharing-your-appauthentication), then provide them as a tuple to the `auth` argument of the `Client` class:\n\n```python\nfrom gradio_client import Client\n\nClient(\n  space_name,\n  auth=[username, password]\n)\n```\n\n\n", "heading1": "Connecting to a Gradio app with auth", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Once you have connected to a Gradio app, you can view the APIs that are available to you by calling the `Client.view_api()` method. For the Whisper Space, we see the following:\n\n```bash\nClient.predict() Usage Info\n---------------------------\nNamed API endpoints: 1\n\n - predict(audio, api_name=\"/predict\") -> output\n    Parameters:\n     - [Audio] audio: filepath (required)  \n    Returns:\n     - [Textbox] output: str \n```\n\nWe see  that we have 1 API endpoint in this space, and shows us how to use the API endpoint to make a prediction: we should call the `.predict()` method (which we will explore below), providing a parameter `input_audio` of type `str`, which is a `filepath or URL`.\n\nWe should also provide the `api_name='/predict'` argument to the `predict()` method. Although this isn't necessary if a Gradio app has only 1 named endpoint, it does allow us to call different endpoints in a single app if they are available.\n\n", "heading1": "Inspecting the API endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "As an alternative to running the `.view_api()` method, you can click on the \"Use via API\" link in the footer of the Gradio app, which shows us the same information, along with example usage. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe View API page also includes an \"API Recorder\" that lets you interact with the Gradio UI normally and converts your interactions into the corresponding code to run with the Python Client.\n\n", "heading1": "The \"View API\" Page", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The simplest way to make a prediction is simply to call the `.predict()` function with the appropriate arguments:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/en2fr\", api_name='/predict')\nclient.predict(\"Hello\")\n\n>> Bonjour\n```\n\nIf there are multiple parameters, then you should pass them as separate arguments to `.predict()`, like this:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"gradio/calculator\")\nclient.predict(4, \"add\", 5)\n\n>> 9.0\n```\n\nIt is recommended to provide key-word arguments instead of positional arguments:\n\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"gradio/calculator\")\nclient.predict(num1=4, operation=\"add\", num2=5)\n\n>> 9.0\n```\n\nThis allows you to take advantage of default arguments. For example, this Space includes the default value for the Slider component so you do not need to provide it when accessing it with the client.\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/image_generator\")\nclient.predict(text=\"an astronaut riding a camel\")\n```\n\nThe default value is the initial value of the corresponding Gradio component. If the component does not have an initial value, but if the corresponding argument in the predict function has a default value of `None`, then that parameter is also optional in the client. Of course, if you'd like to override it, you can include it as well:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(\"abidlabs/image_generator\")\nclient.predict(text=\"an astronaut riding a camel\", steps=25)\n```\n\nFor providing files or URLs as inputs, you should pass in the filepath or URL to the file enclosed within `gradio_client.handle_file()`. This takes care of uploading the file to the Gradio server and ensures that the file is preprocessed correctly:\n\n```python\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/whisper\")\nclient.predict(\n    audio=handle_file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/s", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "```python\nfrom gradio_client import Client, handle_file\n\nclient = Client(\"abidlabs/whisper\")\nclient.predict(\n    audio=handle_file(\"https://audio-samples.github.io/samples/mp3/blizzard_unconditional/sample-0.mp3\")\n)\n\n>> \"My thought I have nobody by a beauty and will as you poured. Mr. Rochester is serve in that so don't find simpus, and devoted abode, to at might in a r\u2014\"\n```\n\n", "heading1": "Making a prediction", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "One should note that `.predict()` is a _blocking_ operation as it waits for the operation to complete before returning the prediction.\n\nIn many cases, you may be better off letting the job run in the background until you need the results of the prediction. You can do this by creating a `Job` instance using the `.submit()` method, and then later calling `.result()` on the job to get the result. For example:\n\n```python\nfrom gradio_client import Client\n\nclient = Client(space=\"abidlabs/en2fr\")\njob = client.submit(\"Hello\", api_name=\"/predict\")  This is not blocking\n\nDo something else\n\njob.result()  This is blocking\n\n>> Bonjour\n```\n\n", "heading1": "Running jobs asynchronously", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Alternatively, one can add one or more callbacks to perform actions after the job has completed running, like this:\n\n```python\nfrom gradio_client import Client\n\ndef print_result(x):\n    print(\"The translated result is: {x}\")\n\nclient = Client(space=\"abidlabs/en2fr\")\n\njob = client.submit(\"Hello\", api_name=\"/predict\", result_callbacks=[print_result])\n\nDo something else\n\n>> The translated result is: Bonjour\n\n```\n\n", "heading1": "Adding callbacks", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The `Job` object also allows you to get the status of the running job by calling the `.status()` method. This returns a `StatusUpdate` object with the following attributes: `code` (the status code, one of a set of defined strings representing the status. See the `utils.Status` class), `rank` (the current position of this job in the queue), `queue_size` (the total queue size), `eta` (estimated time this job will complete), `success` (a boolean representing whether the job completed successfully), and `time` (the time that the status was generated).\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/calculator\")\njob = client.submit(5, \"add\", 4, api_name=\"/predict\")\njob.status()\n\n>> <Status.STARTING: 'STARTING'>\n```\n\n_Note_: The `Job` class also has a `.done()` instance method which returns a boolean indicating whether the job has completed.\n\n", "heading1": "Status", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "The `Job` class also has a `.cancel()` instance method that cancels jobs that have been queued but not started. For example, if you run:\n\n```py\nclient = Client(\"abidlabs/whisper\")\njob1 = client.submit(handle_file(\"audio_sample1.wav\"))\njob2 = client.submit(handle_file(\"audio_sample2.wav\"))\njob1.cancel()  will return False, assuming the job has started\njob2.cancel()  will return True, indicating that the job has been canceled\n```\n\nIf the first job has started processing, then it will not be canceled. If the second job\nhas not yet started, it will be successfully canceled and removed from the queue.\n\n", "heading1": "Cancelling Jobs", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Some Gradio API endpoints do not return a single value, rather they return a series of values. You can get the series of values that have been returned at any time from such a generator endpoint by running `job.outputs()`:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/count_generator\")\njob = client.submit(3, api_name=\"/count\")\nwhile not job.done():\n    time.sleep(0.1)\njob.outputs()\n\n>> ['0', '1', '2']\n```\n\nNote that running `job.result()` on a generator endpoint only gives you the _first_ value returned by the endpoint.\n\nThe `Job` object is also iterable, which means you can use it to display the results of a generator function as they are returned from the endpoint. Here's the equivalent example using the `Job` as a generator:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(src=\"gradio/count_generator\")\njob = client.submit(3, api_name=\"/count\")\n\nfor o in job:\n    print(o)\n\n>> 0\n>> 1\n>> 2\n```\n\nYou can also cancel jobs that that have iterative outputs, in which case the job will finish as soon as the current iteration finishes running.\n\n```py\nfrom gradio_client import Client\nimport time\n\nclient = Client(\"abidlabs/test-yield\")\njob = client.submit(\"abcdef\")\ntime.sleep(3)\njob.cancel()  job cancels after 2 iterations\n```\n\n", "heading1": "Generator Endpoints", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "Gradio demos can include [session state](https://www.gradio.app/guides/state-in-blocks), which provides a way for demos to persist information from user interactions within a page session.\n\nFor example, consider the following demo, which maintains a list of words that a user has submitted in a `gr.State` component. When a user submits a new word, it is added to the state, and the number of previous occurrences of that word is displayed:\n\n```python\nimport gradio as gr\n\ndef count(word, list_of_words):\n    return list_of_words.count(word), list_of_words + [word]\n\nwith gr.Blocks() as demo:\n    words = gr.State([])\n    textbox = gr.Textbox()\n    number = gr.Number()\n    textbox.submit(count, inputs=[textbox, words], outputs=[number, words])\n    \ndemo.launch()\n```\n\nIf you were to connect this this Gradio app using the Python Client, you would notice that the API information only shows a single input and output:\n\n```csv\nClient.predict() Usage Info\n---------------------------\nNamed API endpoints: 1\n\n - predict(word, api_name=\"/count\") -> value_31\n    Parameters:\n     - [Textbox] word: str (required)  \n    Returns:\n     - [Number] value_31: float \n```\n\nThat is because the Python client handles state automatically for you -- as you make a series of requests, the returned state from one request is stored internally and automatically supplied for the subsequent request. If you'd like to reset the state, you can do that by calling `Client.reset_session()`.\n", "heading1": "Demos with Session State", "source_page_url": "https://gradio.app/guides/getting-started-with-the-python-client", "source_page_title": "Gradio Clients And Lite - Getting Started With The Python Client Guide"}, {"text": "You generally don't need to install cURL, as it comes pre-installed on many operating systems. Run:\n\n```bash\ncurl --version\n```\n\nto confirm that `curl` is installed. If it is not already installed, you can install it by visiting https://curl.se/download.html. \n\n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "To query a Gradio app, you'll need its full URL. This is usually just the URL that the Gradio app is hosted on, for example: https://bec81a83-5b5c-471e.gradio.live\n\n\n**Hugging Face Spaces**\n\nHowever, if you are querying a Gradio on Hugging Face Spaces, you will need to use the URL of the embedded Gradio app, not the URL of the Space webpage. For example:\n\n```bash\n\u274c Space URL: https://huggingface.co/spaces/abidlabs/en2fr\n\u2705 Gradio app URL: https://abidlabs-en2fr.hf.space/\n```\n\nYou can get the Gradio app URL by clicking the \"view API\" link at the bottom of the page. Or, you can right-click on the page and then click on \"View Frame Source\" or the equivalent in your browser to view the URL of the embedded Gradio app.\n\nWhile you can use any public Space as an API, you may get rate limited by Hugging Face if you make too many requests. For unlimited usage of a Space, simply duplicate the Space to create a private Space,\nand then use it to make as many requests as you'd like!\n\nNote: to query private Spaces, you will need to pass in your Hugging Face (HF) token. You can get your HF token here: https://huggingface.co/settings/tokens. In this case, you will need to include an additional header in both of your `curl` calls that we'll discuss below:\n\n```bash\n-H \"Authorization: Bearer $HF_TOKEN\"\n```\n\nNow, we are ready to make the two `curl` requests.\n\n", "heading1": "Step 0: Get the URL for your Gradio App", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "The first of the two `curl` requests is `POST` request that submits the input payload to the Gradio app. \n\nThe syntax of the `POST` request is as follows:\n\n```bash\n$ curl -X POST $URL/call/$API_NAME -H \"Content-Type: application/json\" -d '{\n  \"data\": $PAYLOAD\n}'\n```\n\nHere:\n\n* `$URL` is the URL of the Gradio app as obtained in Step 0\n* `$API_NAME` is the name of the API endpoint for the event that you are running. You can get the API endpoint names by clicking the \"view API\" link at the bottom of the page.\n*  `$PAYLOAD` is a valid JSON data list containing the input payload, one element for each input component.\n\nWhen you make this `POST` request successfully, you will get an event id that is printed to the terminal in this format:\n\n```bash\n>> {\"event_id\": $EVENT_ID}   \n```\n\nThis `EVENT_ID` will be needed in the subsequent `curl` request to fetch the results of the prediction. \n\nHere are some examples of how to make the `POST` request\n\n**Basic Example**\n\nRevisiting the example at the beginning of the page, here is how to make the `POST` request for a simple Gradio application that takes in a single input text component:\n\n```bash\n$ curl -X POST https://abidlabs-en2fr.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Hello, my friend.\"] \n}'\n```\n\n**Multiple Input Components**\n\nThis [Gradio demo](https://huggingface.co/spaces/gradio/hello_world_3) accepts three inputs: a string corresponding to the `gr.Textbox`, a boolean value corresponding to the `gr.Checkbox`, and a numerical value corresponding to the `gr.Slider`. Here is the `POST` request:\n\n```bash\ncurl -X POST https://gradio-hello-world-3.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Hello\", true, 5]\n}'\n```\n\n**Private Spaces**\n\nAs mentioned earlier, if you are making a request to a private Space, you will need to pass in a [Hugging Face token](https://huggingface.co/settings/tokens) that has read access to the Space. The request will look like this:\n\n```bash\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "king a request to a private Space, you will need to pass in a [Hugging Face token](https://huggingface.co/settings/tokens) that has read access to the Space. The request will look like this:\n\n```bash\n$ curl -X POST https://private-space.hf.space/call/predict -H \"Content-Type: application/json\" -H \"Authorization: Bearer $HF_TOKEN\" -d '{\n  \"data\": [\"Hello, my friend.\"] \n}'\n```\n\n**Files**\n\nIf you are using `curl` to query a Gradio application that requires file inputs, the files *need* to be provided as URLs, and The URL needs to be enclosed in a dictionary in this format:\n\n```bash\n{\"path\": $URL}\n```\n\nHere is an example `POST` request:\n\n```bash\n$ curl -X POST https://gradio-image-mod.hf.space/call/predict -H \"Content-Type: application/json\" -d '{\n  \"data\": [{\"path\": \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\"}] \n}'\n```\n\n\n**Stateful Demos**\n\nIf your Gradio demo [persists user state](/guides/interface-state) across multiple interactions (e.g. is a chatbot), you can pass in a `session_hash` alongside the `data`. Requests with the same `session_hash` are assumed to be part of the same user session. Here's how that might look:\n\n```bash\nThese two requests will share a session\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Are you sentient?\"],\n  \"session_hash\": \"randomsequence1234\"\n}'\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Really?\"],\n  \"session_hash\": \"randomsequence1234\"\n}'\n\nThis request will be treated as a new session\n\ncurl -X POST https://gradio-chatinterface-random-response.hf.space/call/chat -H \"Content-Type: application/json\" -d '{\n  \"data\": [\"Are you sentient?\"],\n  \"session_hash\": \"newsequence5678\"\n}'\n```\n\n\n\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "ient?\"],\n  \"session_hash\": \"newsequence5678\"\n}'\n```\n\n\n\n", "heading1": "Step 1: Make a Prediction (POST)", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "Once you have received the `EVENT_ID` corresponding to your prediction, you can stream the results. Gradio stores these results  in a least-recently-used cache in the Gradio app. By default, the cache can store 2,000 results (across all users and endpoints of the app). \n\nTo stream the results for your prediction, make a `GET` request with the following syntax:\n\n```bash\n$ curl -N $URL/call/$API_NAME/$EVENT_ID\n```\n\n\nTip: If you are fetching results from a private Space, include a header with your HF token like this: `-H \"Authorization: Bearer $HF_TOKEN\"` in the `GET` request.\n\nThis should produce a stream of responses in this format:\n\n```bash\nevent: ... \ndata: ...\nevent: ... \ndata: ...\n...\n```\n\nHere: `event` can be one of the following:\n* `generating`: indicating an intermediate result\n* `complete`: indicating that the prediction is complete and the final result \n* `error`: indicating that the prediction was not completed successfully\n* `heartbeat`: sent every 15 seconds to keep the request alive\n\nThe `data` is in the same format as the input payload: valid JSON data list containing the output result, one element for each output component.\n\nHere are some examples of what results you should expect if a request is completed successfully:\n\n**Basic Example**\n\nRevisiting the example at the beginning of the page, we would expect the result to look like this:\n\n```bash\nevent: complete\ndata: [\"Bonjour, mon ami.\"]\n```\n\n**Multiple Outputs**\n\nIf your endpoint returns multiple values, they will appear as elements of the `data` list:\n\n```bash\nevent: complete\ndata: [\"Good morning Hello. It is 5 degrees today\", -15.0]\n```\n\n**Streaming Example**\n\nIf your Gradio app [streams a sequence of values](/guides/streaming-outputs), then they will be streamed directly to your terminal, like this:\n\n```bash\nevent: generating\ndata: [\"Hello, w!\"]\nevent: generating\ndata: [\"Hello, wo!\"]\nevent: generating\ndata: [\"Hello, wor!\"]\nevent: generating\ndata: [\"Hello, worl!\"]\nevent: generating\ndata: [\"Hello, w", "heading1": "Step 2: GET the result", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "```bash\nevent: generating\ndata: [\"Hello, w!\"]\nevent: generating\ndata: [\"Hello, wo!\"]\nevent: generating\ndata: [\"Hello, wor!\"]\nevent: generating\ndata: [\"Hello, worl!\"]\nevent: generating\ndata: [\"Hello, world!\"]\nevent: complete\ndata: [\"Hello, world!\"]\n```\n\n**File Example**\n\nIf your Gradio app returns a file, the file will be represented as a dictionary in this format (including potentially some additional keys):\n\n```python\n{\n    \"orig_name\": \"example.jpg\",\n    \"path\": \"/path/in/server.jpg\",\n    \"url\": \"https:/example.com/example.jpg\",\n    \"meta\": {\"_type\": \"gradio.FileData\"}\n}\n```\n\nIn your terminal, it may appear like this:\n\n```bash\nevent: complete\ndata: [{\"path\": \"/tmp/gradio/359933dc8d6cfe1b022f35e2c639e6e42c97a003/image.webp\", \"url\": \"https://gradio-image-mod.hf.space/c/file=/tmp/gradio/359933dc8d6cfe1b022f35e2c639e6e42c97a003/image.webp\", \"size\": null, \"orig_name\": \"image.webp\", \"mime_type\": null, \"is_stream\": false, \"meta\": {\"_type\": \"gradio.FileData\"}}]\n```\n\n", "heading1": "Step 2: GET the result", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "What if your Gradio application has [authentication enabled](/guides/sharing-your-appauthentication)? In that case, you'll need to make an additional `POST` request with cURL to authenticate yourself before you make any queries. Here are the complete steps:\n\nFirst, login with a `POST` request supplying a valid username and password:\n\n```bash\ncurl -X POST $URL/login \\\n     -d \"username=$USERNAME&password=$PASSWORD\" \\\n     -c cookies.txt\n```\n\nIf the credentials are correct, you'll get `{\"success\":true}` in response and the cookies will be saved in `cookies.txt`.\n\nNext, you'll need to include these cookies when you make the original `POST` request, like this:\n\n```bash\n$ curl -X POST $URL/call/$API_NAME -b cookies.txt -H \"Content-Type: application/json\" -d '{\n  \"data\": $PAYLOAD\n}'\n```\n\nFinally, you'll need to `GET` the results, again supplying the cookies from the file:\n\n```bash\ncurl -N $URL/call/$API_NAME/$EVENT_ID -b cookies.txt\n```\n", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/querying-gradio-apps-with-curl", "source_page_title": "Gradio Clients And Lite - Querying Gradio Apps With Curl Guide"}, {"text": "What are agents?\n\nA [LangChain agent](https://docs.langchain.com/docs/components/agents/agent) is a Large Language Model (LLM) that takes user input and reports an output based on using one of many tools at its disposal.\n\nWhat is Gradio?\n\n[Gradio](https://github.com/gradio-app/gradio) is the defacto standard framework for building Machine Learning Web Applications and sharing them with the world - all with just python! \ud83d\udc0d\n\n", "heading1": "Some background", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "To get started with `gradio_tools`, all you need to do is import and initialize your tools and pass them to the langchain agent!\n\nIn the following example, we import the `StableDiffusionPromptGeneratorTool` to create a good prompt for stable diffusion, the\n`StableDiffusionTool` to create an image with our improved prompt, the `ImageCaptioningTool` to caption the generated image, and\nthe `TextToVideoTool` to create a video from a prompt.\n\nWe then tell our agent to create an image of a dog riding a skateboard, but to please improve our prompt ahead of time. We also ask\nit to caption the generated image and create a video for it. The agent can decide which tool to use without us explicitly telling it.\n\n```python\nimport os\n\nif not os.getenv(\"OPENAI_API_KEY\"):\n    raise ValueError(\"OPENAI_API_KEY must be set\")\n\nfrom langchain.agents import initialize_agent\nfrom langchain.llms import OpenAI\nfrom gradio_tools import (StableDiffusionTool, ImageCaptioningTool, StableDiffusionPromptGeneratorTool,\n                          TextToVideoTool)\n\nfrom langchain.memory import ConversationBufferMemory\n\nllm = OpenAI(temperature=0)\nmemory = ConversationBufferMemory(memory_key=\"chat_history\")\ntools = [StableDiffusionTool().langchain, ImageCaptioningTool().langchain,\n         StableDiffusionPromptGeneratorTool().langchain, TextToVideoTool().langchain]\n\n\nagent = initialize_agent(tools, llm, memory=memory, agent=\"conversational-react-description\", verbose=True)\noutput = agent.run(input=(\"Please create a photo of a dog riding a skateboard \"\n                          \"but improve my prompt prior to using an image generator.\"\n                          \"Please caption the generated image and create a video for it using the improved prompt.\"))\n```\n\nYou'll note that we are using some pre-built tools that come with `gradio_tools`. Please see this [doc](https://github.com/freddyaboulton/gradio-toolsgradio-tools-gradio--llm-agents) for a complete list of the tools that come with `gradio_tools`.\nIf ", "heading1": "gradio_tools - An end-to-end example", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "that come with `gradio_tools`. Please see this [doc](https://github.com/freddyaboulton/gradio-toolsgradio-tools-gradio--llm-agents) for a complete list of the tools that come with `gradio_tools`.\nIf you would like to use a tool that's not currently in `gradio_tools`, it is very easy to add your own. That's what the next section will cover.\n\n", "heading1": "gradio_tools - An end-to-end example", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "The core abstraction is the `GradioTool`, which lets you define a new tool for your LLM as long as you implement a standard interface:\n\n```python\nclass GradioTool(BaseTool):\n\n    def __init__(self, name: str, description: str, src: str) -> None:\n\n    @abstractmethod\n    def create_job(self, query: str) -> Job:\n        pass\n\n    @abstractmethod\n    def postprocess(self, output: Tuple[Any] | Any) -> str:\n        pass\n```\n\nThe requirements are:\n\n1. The name for your tool\n2. The description for your tool. This is crucial! Agents decide which tool to use based on their description. Be precise and be sure to include example of what the input and the output of the tool should look like.\n3. The url or space id, e.g. `freddyaboulton/calculator`, of the Gradio application. Based on this value, `gradio_tool` will create a [gradio client](https://github.com/gradio-app/gradio/blob/main/client/python/README.md) instance to query the upstream application via API. Be sure to click the link and learn more about the gradio client library if you are not familiar with it.\n4. create_job - Given a string, this method should parse that string and return a job from the client. Most times, this is as simple as passing the string to the `submit` function of the client. More info on creating jobs [here](https://github.com/gradio-app/gradio/blob/main/client/python/README.mdmaking-a-prediction)\n5. postprocess - Given the result of the job, convert it to a string the LLM can display to the user.\n6. _Optional_ - Some libraries, e.g. [MiniChain](https://github.com/srush/MiniChain/tree/main), may need some info about the underlying gradio input and output types used by the tool. By default, this will return gr.Textbox() but\n   if you'd like to provide more accurate info, implement the `_block_input(self, gr)` and `_block_output(self, gr)` methods of the tool. The `gr` variable is the gradio module (the result of `import gradio as gr`). It will be\n   automatically imported by the `GradiTool` parent", "heading1": "gradio_tools - creating your own tool", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "lf, gr)` and `_block_output(self, gr)` methods of the tool. The `gr` variable is the gradio module (the result of `import gradio as gr`). It will be\n   automatically imported by the `GradiTool` parent class and passed to the `_block_input` and `_block_output` methods.\n\nAnd that's it!\n\nOnce you have created your tool, open a pull request to the `gradio_tools` repo! We welcome all contributions.\n\n", "heading1": "gradio_tools - creating your own tool", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "Here is the code for the StableDiffusion tool as an example:\n\n```python\nfrom gradio_tool import GradioTool\nimport os\n\nclass StableDiffusionTool(GradioTool):\n    \"\"\"Tool for calling stable diffusion from llm\"\"\"\n\n    def __init__(\n        self,\n        name=\"StableDiffusion\",\n        description=(\n            \"An image generator. Use this to generate images based on \"\n            \"text input. Input should be a description of what the image should \"\n            \"look like. The output will be a path to an image file.\"\n        ),\n        src=\"gradio-client-demos/stable-diffusion\",\n        hf_token=None,\n    ) -> None:\n        super().__init__(name, description, src, hf_token)\n\n    def create_job(self, query: str) -> Job:\n        return self.client.submit(query, \"\", 9, fn_index=1)\n\n    def postprocess(self, output: str) -> str:\n        return [os.path.join(output, i) for i in os.listdir(output) if not i.endswith(\"json\")][0]\n\n    def _block_input(self, gr) -> \"gr.components.Component\":\n        return gr.Textbox()\n\n    def _block_output(self, gr) -> \"gr.components.Component\":\n        return gr.Image()\n```\n\nSome notes on this implementation:\n\n1. All instances of `GradioTool` have an attribute called `client` that is a pointed to the underlying [gradio client](https://github.com/gradio-app/gradio/tree/main/client/pythongradio_client-use-a-gradio-app-as-an-api----in-3-lines-of-python). That is what you should use\n   in the `create_job` method.\n2. `create_job` just passes the query string to the `submit` function of the client with some other parameters hardcoded, i.e. the negative prompt string and the guidance scale. We could modify our tool to also accept these values from the input string in a subsequent version.\n3. The `postprocess` method simply returns the first image from the gallery of images created by the stable diffusion space. We use the `os` module to get the full path of the image.\n\n", "heading1": "Example tool - Stable Diffusion", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "You now know how to extend the abilities of your LLM with the 1000s of gradio spaces running in the wild!\nAgain, we welcome any contributions to the [gradio_tools](https://github.com/freddyaboulton/gradio-tools) library.\nWe're excited to see the tools you all build!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/gradio-and-llm-agents", "source_page_title": "Gradio Clients And Lite - Gradio And Llm Agents Guide"}, {"text": "Gradio-Lite\n\nGradio-Lite is the serverless version of Gradio, allowing you to build serverless web UI applications by embedding Python code within HTML. For a detailed introduction to Gradio-Lite itself, please read [this Guide](./gradio-lite).\n\nTransformers.js and Transformers.js.py\n\nTransformers.js is the JavaScript version of the Transformers library that allows you to run machine learning models entirely in the browser.\nSince Transformers.js is a JavaScript library, it cannot be directly used from the Python code of Gradio-Lite applications. To address this, we use a wrapper library called [Transformers.js.py](https://github.com/whitphx/transformers.js.py).\nThe name Transformers.js.py may sound unusual, but it represents the necessary technology stack for using Transformers.js from Python code within a browser environment. The regular Transformers library is not compatible with browser environments.\n\n", "heading1": "Libraries Used", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "Here's an example of how to use Gradio-Lite and Transformers.js together.\nPlease create an HTML file and paste the following code:\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n\npipe = await pipeline('sentiment-analysis')\n\ndemo = gr.Interface.from_pipeline(pipe)\n\ndemo.launch()\n\n\t\t\t<gradio-requirements>\ntransformers-js-py\n\t\t\t</gradio-requirements>\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nHere is a running example of the code above (after the app has loaded, you could disconnect your Internet connection and the app will still work since its running entirely in your browser):\n\n<gradio-lite shared-worker>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n<!-- --->\npipe = await pipeline('sentiment-analysis')\n<!-- --->\ndemo = gr.Interface.from_pipeline(pipe)\n<!-- --->\ndemo.launch()\n<gradio-requirements>\ntransformers-js-py\n</gradio-requirements>\n</gradio-lite>\n\nAnd you you can open your HTML file in a browser to see the Gradio app running!\n\nThe Python code inside the `<gradio-lite>` tag is the Gradio application code. For more details on this part, please refer to [this article](./gradio-lite).\nThe `<gradio-requirements>` tag is used to specify packages to be installed in addition to Gradio-Lite and its dependencies. In this case, we are using Transformers.js.py (`transformers-js-py`), so it is specified here.\n\nLet's break down the code:\n\n`pipe = await pipeline('sentiment-analysis')` creates a Transformers.js pipeline.\nIn this example, we create a sentiment analysis pipeline.\nFor more information on the available pipeline types and usage, please refer to the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/index).\n\n`demo = gr.Interface.from_pipeline(pipe)` creates a Gradio a", "heading1": "Sample Code", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "vailable pipeline types and usage, please refer to the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/index).\n\n`demo = gr.Interface.from_pipeline(pipe)` creates a Gradio app instance. By passing the Transformers.js.py pipeline to `gr.Interface.from_pipeline()`, we can create an interface that utilizes that pipeline with predefined input and output components.\n\nFinally, `demo.launch()` launches the created app.\n\n", "heading1": "Sample Code", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "You can modify the line `pipe = await pipeline('sentiment-analysis')` in the sample above to try different models or tasks.\n\nFor example, if you change it to `pipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment')`, you can test the same sentiment analysis task but with a different model. The second argument of the `pipeline` function specifies the model name.\nIf it's not specified like in the first example, the default model is used. For more details on these specs, refer to the [Transformers.js documentation](https://huggingface.co/docs/transformers.js/index).\n\n<gradio-lite shared-worker>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n<!-- --->\npipe = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment')\n<!-- --->\ndemo = gr.Interface.from_pipeline(pipe)\n<!-- --->\ndemo.launch()\n<gradio-requirements>\ntransformers-js-py\n</gradio-requirements>\n</gradio-lite>\n\nAs another example, changing it to `pipe = await pipeline('image-classification')` creates a pipeline for image classification instead of sentiment analysis.\nIn this case, the interface created with `demo = gr.Interface.from_pipeline(pipe)` will have a UI for uploading an image and displaying the classification result. The `gr.Interface.from_pipeline` function automatically creates an appropriate UI based on the type of pipeline.\n\n<gradio-lite shared-worker>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n<!-- --->\npipe = await pipeline('image-classification')\n<!-- --->\ndemo = gr.Interface.from_pipeline(pipe)\n<!-- --->\ndemo.launch()\n<gradio-requirements>\ntransformers-js-py\n</gradio-requirements>\n</gradio-lite>\n\n<br>\n\n**Note**: If you use an audio pipeline, such as `automatic-speech-recognition`, you will need to put `transformers-js-py[audio]` in your `<gradio-requirements>` as there are additional requirements needed to process audio files.\n\n", "heading1": "Customizing the Model or Pipeline", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "Instead of using `gr.Interface.from_pipeline()`, you can define the user interface using Gradio's regular API.\nHere's an example where the Python code inside the `<gradio-lite>` tag has been modified from the previous sample:\n\n```html\n<html>\n\t<head>\n\t\t<script type=\"module\" crossorigin src=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.js\"></script>\n\t\t<link rel=\"stylesheet\" href=\"https://cdn.jsdelivr.net/npm/@gradio/lite/dist/lite.css\" />\n\t</head>\n\t<body>\n\t\t<gradio-lite>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n\npipe = await pipeline('sentiment-analysis')\n\nasync def fn(text):\n\tresult = await pipe(text)\n\treturn result\n\ndemo = gr.Interface(\n\tfn=fn,\n\tinputs=gr.Textbox(),\n\toutputs=gr.JSON(),\n)\n\ndemo.launch()\n\n\t\t\t<gradio-requirements>\ntransformers-js-py\n\t\t\t</gradio-requirements>\n\t\t</gradio-lite>\n\t</body>\n</html>\n```\n\nIn this example, we modified the code to construct the Gradio user interface manually so that we could output the result as JSON.\n\n<gradio-lite shared-worker>\nimport gradio as gr\nfrom transformers_js_py import pipeline\n<!-- --->\npipe = await pipeline('sentiment-analysis')\n<!-- --->\nasync def fn(text):\n\tresult = await pipe(text)\n\treturn result\n<!-- --->\ndemo = gr.Interface(\n\tfn=fn,\n\tinputs=gr.Textbox(),\n\toutputs=gr.JSON(),\n)\n<!-- --->\ndemo.launch()\n<gradio-requirements>\ntransformers-js-py\n</gradio-requirements>\n</gradio-lite>\n\n", "heading1": "Customizing the UI", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "By combining Gradio-Lite and Transformers.js (and Transformers.js.py), you can create serverless machine learning applications that run entirely in the browser.\n\nGradio-Lite provides a convenient method to create an interface for a given Transformers.js pipeline, `gr.Interface.from_pipeline()`.\nThis method automatically constructs the interface based on the pipeline's task type.\n\nAlternatively, you can define the interface manually using Gradio's regular API, as shown in the second example.\n\nBy using these libraries, you can build and deploy machine learning applications without the need for server-side Python setup or external dependencies.\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/gradio-lite-and-transformers-js", "source_page_title": "Gradio Clients And Lite - Gradio Lite And Transformers Js Guide"}, {"text": "Did you know that apart from being a full-stack machine learning demo, a Gradio Blocks app is also a regular-old python function!?\n\nThis means that if you have a gradio Blocks (or Interface) app called `demo`, you can use `demo` like you would any python function.\n\nSo doing something like `output = demo(\"Hello\", \"friend\")` will run the first event defined in `demo` on the inputs \"Hello\" and \"friend\" and store it\nin the variable `output`.\n\nIf I put you to sleep \ud83e\udd71, please bear with me! By using apps like functions, you can seamlessly compose Gradio apps.\nThe following section will show how.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-blocks-like-functions", "source_page_title": "Building With Blocks - Using Blocks Like Functions Guide"}, {"text": "Let's say we have the following demo that translates english text to german text.\n\n$code_english_translator\n\nI already went ahead and hosted it in Hugging Face spaces at [gradio/english_translator](https://huggingface.co/spaces/gradio/english_translator).\n\nYou can see the demo below as well:\n\n$demo_english_translator\n\nNow, let's say you have an app that generates english text, but you wanted to additionally generate german text.\n\nYou could either:\n\n1. Copy the source code of my english-to-german translation and paste it in your app.\n\n2. Load my english-to-german translation in your app and treat it like a normal python function.\n\nOption 1 technically always works, but it often introduces unwanted complexity.\n\nOption 2 lets you borrow the functionality you want without tightly coupling our apps.\n\nAll you have to do is call the `Blocks.load` class method in your source file.\nAfter that, you can use my translation app like a regular python function!\n\nThe following code snippet and demo shows how to use `Blocks.load`.\n\nNote that the variable `english_translator` is my english to german app, but its used in `generate_text` like a regular function.\n\n$code_generate_english_german\n\n$demo_generate_english_german\n\n", "heading1": "Treating Blocks like functions", "source_page_url": "https://gradio.app/guides/using-blocks-like-functions", "source_page_title": "Building With Blocks - Using Blocks Like Functions Guide"}, {"text": "If the app you are loading defines more than one function, you can specify which function to use\nwith the `fn_index` and `api_name` parameters.\n\nIn the code for our english to german demo, you'll see the following line:\n\n```python\ntranslate_btn.click(translate, inputs=english, outputs=german, api_name=\"translate-to-german\")\n```\n\nThe `api_name` gives this function a unique name in our app. You can use this name to tell gradio which\nfunction in the upstream space you want to use:\n\n```python\nenglish_generator(text, api_name=\"translate-to-german\")[0][\"generated_text\"]\n```\n\nYou can also use the `fn_index` parameter.\nImagine my app also defined an english to spanish translation function.\nIn order to use it in our text generation app, we would use the following code:\n\n```python\nenglish_generator(text, fn_index=1)[0][\"generated_text\"]\n```\n\nFunctions in gradio spaces are zero-indexed, so since the spanish translator would be the second function in my space,\nyou would use index 1.\n\n", "heading1": "How to control which function in the app to use", "source_page_url": "https://gradio.app/guides/using-blocks-like-functions", "source_page_title": "Building With Blocks - Using Blocks Like Functions Guide"}, {"text": "We showed how treating a Blocks app like a regular python helps you compose functionality across different apps.\nAny Blocks app can be treated like a function, but a powerful pattern is to `load` an app hosted on\n[Hugging Face Spaces](https://huggingface.co/spaces) prior to treating it like a function in your own app.\nYou can also load models hosted on the [Hugging Face Model Hub](https://huggingface.co/models) - see the [Using Hugging Face Integrations](/using_hugging_face_integrations) guide for an example.\n\nHappy building! \u2692\ufe0f\n", "heading1": "Parting Remarks", "source_page_url": "https://gradio.app/guides/using-blocks-like-functions", "source_page_title": "Building With Blocks - Using Blocks Like Functions Guide"}, {"text": "Take a look at the demo below.\n\n$code_hello_blocks\n$demo_hello_blocks\n\n- First, note the `with gr.Blocks() as demo:` clause. The Blocks app code will be contained within this clause.\n- Next come the Components. These are the same Components used in `Interface`. However, instead of being passed to some constructor, Components are automatically added to the Blocks as they are created within the `with` clause.\n- Finally, the `click()` event listener. Event listeners define the data flow within the app. In the example above, the listener ties the two Textboxes together. The Textbox `name` acts as the input and Textbox `output` acts as the output to the `greet` method. This dataflow is triggered when the Button `greet_btn` is clicked. Like an Interface, an event listener can take multiple inputs or outputs.\n\nYou can also attach event listeners using decorators - skip the `fn` argument and assign `inputs` and `outputs` directly:\n\n$code_hello_blocks_decorator\n\n", "heading1": "Blocks Structure", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "In the example above, you'll notice that you are able to edit Textbox `name`, but not Textbox `output`. This is because any Component that acts as an input to an event listener is made interactive. However, since Textbox `output` acts only as an output, Gradio determines that it should not be made interactive. You can override the default behavior and directly configure the interactivity of a Component with the boolean `interactive` keyword argument, e.g. `gr.Textbox(interactive=True)`.\n\n```python\noutput = gr.Textbox(label=\"Output\", interactive=True)\n```\n\n_Note_: What happens if a Gradio component is neither an input nor an output? If a component is constructed with a default value, then it is presumed to be displaying content and is rendered non-interactive. Otherwise, it is rendered interactive. Again, this behavior can be overridden by specifying a value for the `interactive` argument.\n\n", "heading1": "Event Listeners and Interactivity", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Take a look at the demo below:\n\n$code_blocks_hello\n$demo_blocks_hello\n\nInstead of being triggered by a click, the `welcome` function is triggered by typing in the Textbox `inp`. This is due to the `change()` event listener. Different Components support different event listeners. For example, the `Video` Component supports a `play()` event listener, triggered when a user presses play. See the [Docs](http://gradio.app/docscomponents) for the event listeners for each Component.\n\n", "heading1": "Types of Event Listeners", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "A Blocks app is not limited to a single data flow the way Interfaces are. Take a look at the demo below:\n\n$code_reversible_flow\n$demo_reversible_flow\n\nNote that `num1` can act as input to `num2`, and also vice-versa! As your apps get more complex, you will have many data flows connecting various Components.\n\nHere's an example of a \"multi-step\" demo, where the output of one model (a speech-to-text model) gets fed into the next model (a sentiment classifier).\n\n$code_blocks_speech_text_sentiment\n$demo_blocks_speech_text_sentiment\n\n", "heading1": "Multiple Data Flows", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "The event listeners you've seen so far have a single input component. If you'd like to have multiple input components pass data to the function, you have two options on how the function can accept input component values:\n\n1. as a list of arguments, or\n2. as a single dictionary of values, keyed by the component\n\nLet's see an example of each:\n$code_calculator_list_and_dict\n\nBoth `add()` and `sub()` take `a` and `b` as inputs. However, the syntax is different between these listeners.\n\n1. To the `add_btn` listener, we pass the inputs as a list. The function `add()` takes each of these inputs as arguments. The value of `a` maps to the argument `num1`, and the value of `b` maps to the argument `num2`.\n2. To the `sub_btn` listener, we pass the inputs as a set (note the curly brackets!). The function `sub()` takes a single dictionary argument `data`, where the keys are the input components, and the values are the values of those components.\n\nIt is a matter of preference which syntax you prefer! For functions with many input components, option 2 may be easier to manage.\n\n$demo_calculator_list_and_dict\n\n", "heading1": "Function Input List vs Dict", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Similarly, you may return values for multiple output components either as:\n\n1. a list of values, or\n2. a dictionary keyed by the component\n\nLet's first see an example of (1), where we set the values of two output components by returning two values:\n\n```python\nwith gr.Blocks() as demo:\n    food_box = gr.Number(value=10, label=\"Food Count\")\n    status_box = gr.Textbox()\n\n    def eat(food):\n        if food > 0:\n            return food - 1, \"full\"\n        else:\n            return 0, \"hungry\"\n\n    gr.Button(\"Eat\").click(\n        fn=eat,\n        inputs=food_box,\n        outputs=[food_box, status_box]\n    )\n```\n\nAbove, each return statement returns two values corresponding to `food_box` and `status_box`, respectively.\n\n**Note:** if your event listener has a single output component, you should **not** return it as a single-item list. This will not work, since Gradio does not know whether to interpret that outer list as part of your return value. You should instead just return that value directly.\n\nNow, let's see option (2). Instead of returning a list of values corresponding to each output component in order, you can also return a dictionary, with the key corresponding to the output component and the value as the new value. This also allows you to skip updating some output components.\n\n```python\nwith gr.Blocks() as demo:\n    food_box = gr.Number(value=10, label=\"Food Count\")\n    status_box = gr.Textbox()\n\n    def eat(food):\n        if food > 0:\n            return {food_box: food - 1, status_box: \"full\"}\n        else:\n            return {status_box: \"hungry\"}\n\n    gr.Button(\"Eat\").click(\n        fn=eat,\n        inputs=food_box,\n        outputs=[food_box, status_box]\n    )\n```\n\nNotice how when there is no food, we only update the `status_box` element. We skipped updating the `food_box` component.\n\nDictionary returns are helpful when an event listener affects many components on return, or conditionally affects outputs and not others.\n\nKeep in mind that with dictionary returns,", "heading1": "Function Return List vs Dict", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "d_box` component.\n\nDictionary returns are helpful when an event listener affects many components on return, or conditionally affects outputs and not others.\n\nKeep in mind that with dictionary returns, we still need to specify the possible outputs in the event listener.\n\n", "heading1": "Function Return List vs Dict", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "The return value of an event listener function is usually the updated value of the corresponding output Component. Sometimes we want to update the configuration of the Component as well, such as the visibility. In this case, we return a new Component, setting the properties we want to change.\n\n$code_blocks_essay_simple\n$demo_blocks_essay_simple\n\nSee how we can configure the Textbox itself through a new `gr.Textbox()` method. The `value=` argument can still be used to update the value along with Component configuration. Any arguments we do not set will preserve their previous values.\n\n", "heading1": "Updating Component Configurations", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "In some cases, you may want to leave a component's value unchanged. Gradio includes a special function, `gr.skip()`, which can be returned from your function. Returning this function will keep the output component (or components') values as is. Let us illustrate with an example:\n\n$code_skip\n$demo_skip\n\nNote the difference between returning `None` (which generally resets a component's value to an empty state) versus returning `gr.skip()`, which leaves the component value unchanged.\n\nTip: if you have multiple output components, and you want to leave all of their values unchanged, you can just return a single `gr.skip()` instead of returning a tuple of skips, one for each element.\n\n", "heading1": "Not Changing a Component's Value", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "You can also run events consecutively by using the `then` method of an event listener. This will run an event after the previous event has finished running. This is useful for running events that update components in multiple steps.\n\nFor example, in the chatbot example below, we first update the chatbot with the user message immediately, and then update the chatbot with the computer response after a simulated delay.\n\n$code_chatbot_consecutive\n$demo_chatbot_consecutive\n\nThe `.then()` method of an event listener executes the subsequent event regardless of whether the previous event raised any errors. If you'd like to only run subsequent events if the previous event executed successfully, use the `.success()` method, which takes the same arguments as `.then()`. Conversely, if you'd like to only run subsequent events if the previous event failed (i.e., raised an error), use the `.failure()` method. This is particularly useful for error handling workflows, such as displaying error messages or restoring previous states when an operation fails.\n\n", "heading1": "Running Events Consecutively", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Often times, you may want to bind multiple triggers to the same function. For example, you may want to allow a user to click a submit button, or press enter to submit a form. You can do this using the `gr.on` method and passing a list of triggers to the `trigger`.\n\n$code_on_listener_basic\n$demo_on_listener_basic\n\nYou can use decorator syntax as well:\n\n$code_on_listener_decorator\n\nYou can use `gr.on` to create \"live\" events by binding to the `change` event of components that implement it. If you do not specify any triggers, the function will automatically bind to all `change` event of all input components that include a `change` event (for example `gr.Textbox` has a `change` event whereas `gr.Button` does not).\n\n$code_on_listener_live\n$demo_on_listener_live\n\nYou can follow `gr.on` with `.then`, just like any regular event listener. This handy method should save you from having to write a lot of repetitive code!\n\n", "heading1": "Binding Multiple Triggers to a Function", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "If you want to set a Component's value to always be a function of the value of other Components, you can use the following shorthand:\n\n```python\nwith gr.Blocks() as demo:\n  num1 = gr.Number()\n  num2 = gr.Number()\n  product = gr.Number(lambda a, b: a * b, inputs=[num1, num2])\n```\n\nThis functionally the same as:\n```python\nwith gr.Blocks() as demo:\n  num1 = gr.Number()\n  num2 = gr.Number()\n  product = gr.Number()\n\n  gr.on(\n    [num1.change, num2.change, demo.load], \n    lambda a, b: a * b, \n    inputs=[num1, num2], \n    outputs=product\n  )\n```\n", "heading1": "Binding a Component Value Directly to a Function of Other Components", "source_page_url": "https://gradio.app/guides/blocks-and-event-listeners", "source_page_title": "Building With Blocks - Blocks And Event Listeners Guide"}, {"text": "Elements within a `with gr.Row` clause will all be displayed horizontally. For example, to display two Buttons side by side:\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Row():\n        btn1 = gr.Button(\"Button 1\")\n        btn2 = gr.Button(\"Button 2\")\n```\n\nYou can set every element in a Row to have the same height. Configure this with the `equal_height` argument.\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Row(equal_height=True):\n        textbox = gr.Textbox()\n        btn2 = gr.Button(\"Button 2\")\n```\n\nThe widths of elements in a Row can be controlled via a combination of `scale` and `min_width` arguments that are present in every Component.\n\n- `scale` is an integer that defines how an element will take up space in a Row. If scale is set to `0`, the element will not expand to take up space. If scale is set to `1` or greater, the element will expand. Multiple elements in a row will expand proportional to their scale. Below, `btn2` will expand twice as much as `btn1`, while `btn0` will not expand at all:\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Row():\n        btn0 = gr.Button(\"Button 0\", scale=0)\n        btn1 = gr.Button(\"Button 1\", scale=1)\n        btn2 = gr.Button(\"Button 2\", scale=2)\n```\n\n- `min_width` will set the minimum width the element will take. The Row will wrap if there isn't sufficient space to satisfy all `min_width` values.\n\nLearn more about Rows in the [docs](https://gradio.app/docs/row).\n\n", "heading1": "Rows", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Components within a Column will be placed vertically atop each other. Since the vertical layout is the default layout for Blocks apps anyway, to be useful, Columns are usually nested within Rows. For example:\n\n$code_rows_and_columns\n$demo_rows_and_columns\n\nSee how the first column has two Textboxes arranged vertically. The second column has an Image and Button arranged vertically. Notice how the relative widths of the two columns is set by the `scale` parameter. The column with twice the `scale` value takes up twice the width.\n\nLearn more about Columns in the [docs](https://gradio.app/docs/column).\n\nFill Browser Height / Width\n\nTo make an app take the full width of the browser by removing the side padding, use `gr.Blocks(fill_width=True)`. \n\nTo make top level Components expand to take the full height of the browser, use `fill_height` and apply scale to the expanding Components.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks(fill_height=True) as demo:\n    gr.Chatbot(scale=1)\n    gr.Textbox(scale=0)\n```\n\n", "heading1": "Columns and Nesting", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Some components support setting height and width. These parameters accept either a number (interpreted as pixels) or a string. Using a string allows the direct application of any CSS unit to the encapsulating Block element.\n\nBelow is an example illustrating the use of viewport width (vw):\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    im = gr.ImageEditor(width=\"50vw\")\n\ndemo.launch()\n```\n\n", "heading1": "Dimensions", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "You can also create Tabs using the `with gr.Tab('tab_name'):` clause. Any component created inside of a `with gr.Tab('tab_name'):` context appears in that tab. Consecutive Tab clauses are grouped together so that a single tab can be selected at one time, and only the components within that Tab's context are shown.\n\nFor example:\n\n$code_blocks_flipper\n$demo_blocks_flipper\n\nAlso note the `gr.Accordion('label')` in this example. The Accordion is a layout that can be toggled open or closed. Like `Tabs`, it is a layout element that can selectively hide or show content. Any components that are defined inside of a `with gr.Accordion('label'):` will be hidden or shown when the accordion's toggle icon is clicked.\n\nLearn more about [Tabs](https://gradio.app/docs/tab) and [Accordions](https://gradio.app/docs/accordion) in the docs.\n\n", "heading1": "Tabs and Accordions", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "The sidebar is a collapsible panel that renders child components on the left side of the screen and can be expanded or collapsed.\n\nFor example:\n\n$code_blocks_sidebar\n\nLearn more about [Sidebar](https://gradio.app/docs/gradio/sidebar) in the docs.\n\n", "heading1": "Sidebar", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Both Components and Layout elements have a `visible` argument that can set initially and also updated. Setting `gr.Column(visible=...)` on a Column can be used to show or hide a set of Components.\n\n$code_blocks_form\n$demo_blocks_form\n\n", "heading1": "Visibility", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "In some cases, you might want to define components before you actually render them in your UI. For instance, you might want to show an examples section using `gr.Examples` above the corresponding `gr.Textbox` input. Since `gr.Examples` requires as a parameter the input component object, you will need to first define the input component, but then render it later, after you have defined the `gr.Examples` object.\n\nThe solution to this is to define the `gr.Textbox` outside of the `gr.Blocks()` scope and use the component's `.render()` method wherever you'd like it placed in the UI.\n\nHere's a full code example:\n\n```python\ninput_textbox = gr.Textbox()\n\nwith gr.Blocks() as demo:\n    gr.Examples([\"hello\", \"bonjour\", \"merhaba\"], input_textbox)\n    input_textbox.render()\n```\n\nSimilarly, if you have already defined a component in a Gradio app, but wish to unrender it so that you can define in a different part of your application, then you can call the `.unrender()` method. In the following example, the `Textbox` will appear in the third column:\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    with gr.Row():\n        with gr.Column():\n            gr.Markdown(\"Row 1\")\n            textbox = gr.Textbox()\n        with gr.Column():\n            gr.Markdown(\"Row 2\")\n            textbox.unrender()\n        with gr.Column():\n            gr.Markdown(\"Row 3\")\n            textbox.render()\n\ndemo.launch()\n```\n", "heading1": "Defining and Rendering Components Separately", "source_page_url": "https://gradio.app/guides/controlling-layout", "source_page_title": "Building With Blocks - Controlling Layout Guide"}, {"text": "Global state in Gradio apps is very simple: any variable created outside of a function is shared globally between all users.\n\nThis makes managing global state very simple and without the need for external services. For example, in this application, the `visitor_count` variable is shared between all users\n\n```py\nimport gradio as gr\n\nShared between all users\nvisitor_count = 0\n\ndef increment_counter():\n    global visitor_count\n    visitor_count += 1\n    return visitor_count\n\nwith gr.Blocks() as demo:    \n    number = gr.Textbox(label=\"Total Visitors\", value=\"Counting...\")\n    demo.load(increment_counter, inputs=None, outputs=number)\n\ndemo.launch()\n```\n\nThis means that any time you do _not_ want to share a value between users, you should declare it _within_ a function. But what if you need to share values between function calls, e.g. a chat history? In that case, you should use one of the subsequent approaches to manage state.\n\n", "heading1": "Global State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Gradio supports session state, where data persists across multiple submits within a page session. To reiterate, session data is _not_ shared between different users of your model, and does _not_ persist if a user refreshes the page to reload the Gradio app. To store data in a session state, you need to do three things:\n\n1. Create a `gr.State()` object. If there is a default value to this stateful object, pass that into the constructor. Note that `gr.State` objects must be [deepcopy-able](https://docs.python.org/3/library/copy.html), otherwise you will need to use a different approach as described below.\n2. In the event listener, put the `State` object as an input and output as needed.\n3. In the event listener function, add the variable to the input parameters and the return value.\n\nLet's take a look at a simple example. We have a simple checkout app below where you add items to a cart. You can also see the size of the cart.\n\n$code_simple_state\n\nNotice how we do this with state:\n\n1. We store the cart items in a `gr.State()` object, initialized here to be an empty list.\n2. When adding items to the cart, the event listener uses the cart as both input and output - it returns the updated cart with all the items inside. \n3. We can attach a `.change` listener to cart, that uses the state variable as input as well.\n\nYou can think of `gr.State` as an invisible Gradio component that can store any kind of value. Here, `cart` is not visible in the frontend but is used for calculations.\n\nThe `.change` listener for a state variable triggers after any event listener changes the value of a state variable. If the state variable holds a sequence (like a `list`, `set`, or `dict`), a change is triggered if any of the elements inside change. If it holds an object or primitive, a change is triggered if the **hash** of the  value changes. So if you define a custom class and create a `gr.State` variable that is an instance of that class, make sure that the the class includes a sensible `__", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "riggered if the **hash** of the  value changes. So if you define a custom class and create a `gr.State` variable that is an instance of that class, make sure that the the class includes a sensible `__hash__` implementation.\n\nThe value of a session State variable is cleared when the user refreshes the page. The value is stored on in the app backend for 60 minutes after the user closes the tab (this can be configured by the `delete_cache` parameter in `gr.Blocks`).\n\nLearn more about `State` in the [docs](https://gradio.app/docs/gradio/state).\n\n**What about objects that cannot be deepcopied?**\n\nAs mentioned earlier, the value stored in `gr.State` must be [deepcopy-able](https://docs.python.org/3/library/copy.html). If you are working with a complex object that cannot be deepcopied, you can take a different approach to manually read the user's `session_hash` and store a global `dictionary` with instances of your object for each user. Here's how you would do that:\n\n```py\nimport gradio as gr\n\nclass NonDeepCopyable:\n    def __init__(self):\n        from threading import Lock\n        self.counter = 0\n        self.lock = Lock()  Lock objects cannot be deepcopied\n    \n    def increment(self):\n        with self.lock:\n            self.counter += 1\n            return self.counter\n\nGlobal dictionary to store user-specific instances\ninstances = {}\n\ndef initialize_instance(request: gr.Request):\n    instances[request.session_hash] = NonDeepCopyable()\n    return \"Session initialized!\"\n\ndef cleanup_instance(request: gr.Request):\n    if request.session_hash in instances:\n        del instances[request.session_hash]\n\ndef increment_counter(request: gr.Request):\n    if request.session_hash in instances:\n        instance = instances[request.session_hash]\n        return instance.increment()\n    return \"Error: Session not initialized\"\n\nwith gr.Blocks() as demo:\n    output = gr.Textbox(label=\"Status\")\n    counter = gr.Number(label=\"Counter Value\")\n    increment_btn = gr.Button(\"Increment Co", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "   return \"Error: Session not initialized\"\n\nwith gr.Blocks() as demo:\n    output = gr.Textbox(label=\"Status\")\n    counter = gr.Number(label=\"Counter Value\")\n    increment_btn = gr.Button(\"Increment Counter\")\n    increment_btn.click(increment_counter, inputs=None, outputs=counter)\n    \n    Initialize instance when page loads\n    demo.load(initialize_instance, inputs=None, outputs=output)    \n    Clean up instance when page is closed/refreshed\n    demo.unload(cleanup_instance)    \n\ndemo.launch()\n```\n\n", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Gradio also supports browser state, where data persists in the browser's localStorage even after the page is refreshed or closed. This is useful for storing user preferences, settings, API keys, or other data that should persist across sessions. To use local state:\n\n1. Create a `gr.BrowserState` object. You can optionally provide an initial default value and a key to identify the data in the browser's localStorage.\n2. Use it like a regular `gr.State` component in event listeners as inputs and outputs.\n\nHere's a simple example that saves a user's username and password across sessions:\n\n$code_browserstate\n\nNote: The value stored in `gr.BrowserState` does not persist if the Grado app is restarted. To persist it, you can hardcode specific values of `storage_key` and `secret` in the `gr.BrowserState` component and restart the Gradio app on the same server name and server port. However, this should only be done if you are running trusted Gradio apps, as in principle, this can allow one Gradio app to access localStorage data that was created by a different Gradio app.\n", "heading1": "Browser State", "source_page_url": "https://gradio.app/guides/state-in-blocks", "source_page_title": "Building With Blocks - State In Blocks Guide"}, {"text": "Gradio themes are the easiest way to customize the look and feel of your app. You can choose from a variety of themes, or create your own. To do so, pass the `theme=` kwarg to the `Blocks` constructor. For example:\n\n```python\nwith gr.Blocks(theme=gr.themes.Glass()):\n    ...\n```\n\nGradio comes with a set of prebuilt themes which you can load from `gr.themes.*`. You can extend these themes or create your own themes from scratch - see the [Theming guide](/guides/theming-guide) for more details.\n\nFor additional styling ability, you can pass any CSS to your app as a string using the `css=` kwarg. You can also pass a pathlib.Path to a css file or a list of such paths to the `css_paths=` kwarg.\n\n**Warning**: The use of query selectors in custom JS and CSS is _not_ guaranteed to work across Gradio versions that bind to Gradio's own HTML elements as the Gradio HTML DOM may change. We recommend using query selectors sparingly.\n\nThe base class for the Gradio app is `gradio-container`, so here's an example that changes the background color of the Gradio app:\n\n```python\nwith gr.Blocks(css=\".gradio-container {background-color: red}\") as demo:\n    ...\n```\n\nIf you'd like to reference external files in your css, preface the file path (which can be a relative or absolute path) with `\"/gradio_api/file=\"`, for example:\n\n```python\nwith gr.Blocks(css=\".gradio-container {background: url('/gradio_api/file=clouds.jpg')}\") as demo:\n    ...\n```\n\nNote: By default, most files in the host machine are not accessible to users running the Gradio app. As a result, you should make sure that any referenced files (such as `clouds.jpg` here) are either URLs or [allowed paths, as described here](/main/guides/file-access).\n\n\n", "heading1": "Adding custom CSS to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "You can `elem_id` to add an HTML element `id` to any component, and `elem_classes` to add a class or list of classes. This will allow you to select elements more easily with CSS. This approach is also more likely to be stable across Gradio versions as built-in class names or ids may change (however, as mentioned in the warning above, we cannot guarantee complete compatibility between Gradio versions if you use custom CSS as the DOM elements may themselves change).\n\n```python\ncss = \"\"\"\nwarning {background-color: FFCCCB}\n.feedback textarea {font-size: 24px !important}\n\"\"\"\n\nwith gr.Blocks(css=css) as demo:\n    box1 = gr.Textbox(value=\"Good Job\", elem_classes=\"feedback\")\n    box2 = gr.Textbox(value=\"Failure\", elem_id=\"warning\", elem_classes=\"feedback\")\n```\n\nThe CSS `warning` ruleset will only target the second Textbox, while the `.feedback` ruleset will target both. Note that when targeting classes, you might need to put the `!important` selector to override the default Gradio styles.\n\n", "heading1": "The `elem_id` and `elem_classes` Arguments", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "There are 3 ways to add javascript code to your Gradio demo:\n\n1. You can add JavaScript code as a string to the `js` parameter of the `Blocks` or `Interface` initializer. This will run the JavaScript code when the demo is first loaded.\n\nBelow is an example of adding custom js to show an animated welcome message when the demo first loads.\n\n$code_blocks_js_load\n$demo_blocks_js_load\n\n\n2. When using `Blocks` and event listeners, events have a `js` argument that can take a JavaScript function as a string and treat it just like a Python event listener function. You can pass both a JavaScript function and a Python function (in which case the JavaScript function is run first) or only Javascript (and set the Python `fn` to `None`). Take a look at the code below:\n   \n$code_blocks_js_methods\n$demo_blocks_js_methods\n\n3. Lastly, you can add JavaScript code to the `head` param of the `Blocks` initializer. This will add the code to the head of the HTML document. For example, you can add Google Analytics to your demo like so:\n\n\n```python\nhead = f\"\"\"\n<script async src=\"https://www.googletagmanager.com/gtag/js?id={google_analytics_tracking_id}\"></script>\n<script>\n  window.dataLayer = window.dataLayer || [];\n  function gtag(){{dataLayer.push(arguments);}}\n  gtag('js', new Date());\n  gtag('config', '{google_analytics_tracking_id}');\n</script>\n\"\"\"\n\nwith gr.Blocks(head=head) as demo:\n    gr.HTML(\"<h1>My App</h1>\")\n\ndemo.launch()\n```\n\nThe `head` parameter accepts any HTML tags you would normally insert into the `<head>` of a page. For example, you can also include `<meta>` tags to `head` in order to update the social sharing preview for your Gradio app like this:\n\n```py\nimport gradio as gr\n\ncustom_head = \"\"\"\n<!-- HTML Meta Tags -->\n<title>Sample App</title>\n<meta name=\"description\" content=\"An open-source web application showcasing various features and capabilities.\">\n\n<!-- Facebook Meta Tags -->\n<meta property=\"og:url\" content=\"https://example.com\">\n<meta property=\"og:type\" content=\"webs", "heading1": "Adding custom JavaScript to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "n open-source web application showcasing various features and capabilities.\">\n\n<!-- Facebook Meta Tags -->\n<meta property=\"og:url\" content=\"https://example.com\">\n<meta property=\"og:type\" content=\"website\">\n<meta property=\"og:title\" content=\"Sample App\">\n<meta property=\"og:description\" content=\"An open-source web application showcasing various features and capabilities.\">\n<meta property=\"og:image\" content=\"https://cdn.britannica.com/98/152298-050-8E45510A/Cheetah.jpg\">\n\n<!-- Twitter Meta Tags -->\n<meta name=\"twitter:card\" content=\"summary_large_image\">\n<meta name=\"twitter:creator\" content=\"@example_user\">\n<meta name=\"twitter:title\" content=\"Sample App\">\n<meta name=\"twitter:description\" content=\"An open-source web application showcasing various features and capabilities.\">\n<meta name=\"twitter:image\" content=\"https://cdn.britannica.com/98/152298-050-8E45510A/Cheetah.jpg\">\n<meta property=\"twitter:domain\" content=\"example.com\">\n<meta property=\"twitter:url\" content=\"https://example.com\">  \n\"\"\"\n\nwith gr.Blocks(title=\"My App\", head=custom_head) as demo:\n    gr.HTML(\"<h1>My App</h1>\")\n\ndemo.launch()\n```\n\n\n\nNote that injecting custom JS can affect browser behavior and accessibility (e.g. keyboard shortcuts may be lead to unexpected behavior if your Gradio app is embedded in another webpage). You should test your interface across different browsers and be mindful of how scripts may interact with browser defaults. Here's an example where pressing `Shift + s` triggers the `click` event of a specific `Button` component if the browser focus is _not_ on an input component (e.g. `Textbox` component):\n\n```python\nimport gradio as gr\n\nshortcut_js = \"\"\"\n<script>\nfunction shortcuts(e) {\n    var event = document.all ? window.event : e;\n    switch (e.target.tagName.toLowerCase()) {\n        case \"input\":\n        case \"textarea\":\n        break;\n        default:\n        if (e.key.toLowerCase() == \"s\" && e.shiftKey) {\n            document.getElementById(\"my_btn\").click();\n        }\n    }\n}\ndoc", "heading1": "Adding custom JavaScript to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "e \"input\":\n        case \"textarea\":\n        break;\n        default:\n        if (e.key.toLowerCase() == \"s\" && e.shiftKey) {\n            document.getElementById(\"my_btn\").click();\n        }\n    }\n}\ndocument.addEventListener('keypress', shortcuts, false);\n</script>\n\"\"\"\n\nwith gr.Blocks(head=shortcut_js) as demo:\n    action_button = gr.Button(value=\"Name\", elem_id=\"my_btn\")\n    textbox = gr.Textbox()\n    action_button.click(lambda : \"button pressed\", None, textbox)\n    \ndemo.launch()\n```\n\n", "heading1": "Adding custom JavaScript to your demo", "source_page_url": "https://gradio.app/guides/custom-CSS-and-JS", "source_page_title": "Building With Blocks - Custom Css And Js Guide"}, {"text": "In the example below, we will create a variable number of Textboxes. When the user edits the input Textbox, we create a Textbox for each letter in the input. Try it out below:\n\n$code_render_split_simple\n$demo_render_split_simple\n\nSee how we can now create a variable number of Textboxes using our custom logic - in this case, a simple `for` loop. The `@gr.render` decorator enables this with the following steps:\n\n1. Create a function and attach the @gr.render decorator to it.\n2. Add the input components to the `inputs=` argument of @gr.render, and create a corresponding argument in your function for each component. This function will automatically re-run on any change to a component.\n3. Add all components inside the function that you want to render based on the inputs.\n\nNow whenever the inputs change, the function re-runs, and replaces the components created from the previous function run with the latest run. Pretty straightforward! Let's add a little more complexity to this app:\n\n$code_render_split\n$demo_render_split\n\nBy default, `@gr.render` re-runs are triggered by the `.load` listener to the app and the `.change` listener to any input component provided. We can override this by explicitly setting the triggers in the decorator, as we have in this app to only trigger on `input_text.submit` instead. \nIf you are setting custom triggers, and you also want an automatic render at the start of the app, make sure to add `demo.load` to your list of triggers.\n\n", "heading1": "Dynamic Number of Components", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "If you're creating components, you probably want to attach event listeners to them as well. Let's take a look at an example that takes in a variable number of Textbox as input, and merges all the text into a single box.\n\n$code_render_merge_simple\n$demo_render_merge_simple\n\nLet's take a look at what's happening here:\n\n1. The state variable `text_count` is keeping track of the number of Textboxes to create. By clicking on the Add button, we increase `text_count` which triggers the render decorator.\n2. Note that in every single Textbox we create in the render function, we explicitly set a `key=` argument. This key allows us to preserve the value of this Component between re-renders. If you type in a value in a textbox, and then click the Add button, all the Textboxes re-render, but their values aren't cleared because the `key=` maintains the the value of a Component across a render.\n3. We've stored the Textboxes created in a list, and provide this list as input to the merge button event listener. Note that **all event listeners that use Components created inside a render function must also be defined inside that render function**. The event listener can still reference Components outside the render function, as we do here by referencing `merge_btn` and `output` which are both defined outside the render function.\n\nJust as with Components, whenever a function re-renders, the event listeners created from the previous render are cleared and the new event listeners from the latest run are attached. \n\nThis allows us to create highly customizable and complex interactions! \n\n", "heading1": "Dynamic Event Listeners", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "The `key=` argument is used to let Gradio know that the same component is being generated when your render function re-runs. This does two things:\n\n1. The same element in the browser is re-used from the previous render for this Component. This gives browser performance gains - as there's no need to destroy and rebuild a component on a render - and preserves any browser attributes that the Component may have had. If your Component is nested within layout items like `gr.Row`, make sure they are keyed as well because the keys of the parents must also match.\n2. Properties that may be changed by the user or by other event listeners are preserved. By default, only the \"value\" of Component is preserved, but you can specify any list of properties to preserve using the `preserved_by_key=` kwarg.\n\nSee the example below:\n\n$code_render_preserve_key\n$demo_render_preserve_key\n\nYou'll see in this example, when you change the `number_of_boxes` slider, there's a new re-render to update the number of box rows. If you click the \"Change Label\" buttons, they change the `label` and `info` properties of the corresponding textbox. You can also enter text in any textbox to change its value. If you change number of boxes after this, the re-renders \"reset\" the `info`, but the `label` and any entered `value` is still preserved.\n\nNote you can also key any event listener, e.g. `button.click(key=...)` if the same listener is being recreated with the same inputs and outputs across renders. This gives performance benefits, and also prevents errors from occuring if an event was triggered in a previous render, then a re-render occurs, and then the previous event finishes processing. By keying your listener, Gradio knows where to send the data properly. \n\n", "heading1": "Closer Look at `keys=` parameter", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "Let's look at two examples that use all the features above. First, try out the to-do list app below: \n\n$code_todo_list\n$demo_todo_list\n\nNote that almost the entire app is inside a single `gr.render` that reacts to the tasks `gr.State` variable. This variable is a nested list, which presents some complexity. If you design a `gr.render` to react to a list or dict structure, ensure you do the following:\n\n1. Any event listener that modifies a state variable in a manner that should trigger a re-render must set the state variable as an output. This lets Gradio know to check if the variable has changed behind the scenes. \n2. In a `gr.render`, if a variable in a loop is used inside an event listener function, that variable should be \"frozen\" via setting it to itself as a default argument in the function header. See how we have `task=task` in both `mark_done` and `delete`. This freezes the variable to its \"loop-time\" value.\n\nLet's take a look at one last example that uses everything we learned. Below is an audio mixer. Provide multiple audio tracks and mix them together.\n\n$code_audio_mixer\n$demo_audio_mixer\n\nTwo things to note in this app:\n1. Here we provide `key=` to all the components! We need to do this so that if we add another track after setting the values for an existing track, our input values to the existing track do not get reset on re-render.\n2. When there are lots of components of different types and arbitrary counts passed to an event listener, it is easier to use the set and dictionary notation for inputs rather than list notation. Above, we make one large set of all the input `gr.Audio` and `gr.Slider` components when we pass the inputs to the `merge` function. In the function body we query the component values as a dict.\n\nThe `gr.render` expands gradio capabilities extensively - see what you can make out of it! \n", "heading1": "Putting it Together", "source_page_url": "https://gradio.app/guides/dynamic-apps-with-render-decorator", "source_page_title": "Building With Blocks - Dynamic Apps With Render Decorator Guide"}, {"text": "By default, each event listener has its own queue, which handles one request at a time. This can be configured via two arguments:\n\n- `concurrency_limit`: This sets the maximum number of concurrent executions for an event listener. By default, the limit is 1 unless configured otherwise in `Blocks.queue()`. You can also set it to `None` for no limit (i.e., an unlimited number of concurrent executions). For example:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    prompt = gr.Textbox()\n    image = gr.Image()\n    generate_btn = gr.Button(\"Generate Image\")\n    generate_btn.click(image_gen, prompt, image, concurrency_limit=5)\n```\n\nIn the code above, up to 5 requests can be processed simultaneously for this event listener. Additional requests will be queued until a slot becomes available.\n\nIf you want to manage multiple event listeners using a shared queue, you can use the `concurrency_id` argument:\n\n- `concurrency_id`: This allows event listeners to share a queue by assigning them the same ID. For example, if your setup has only 2 GPUs but multiple functions require GPU access, you can create a shared queue for all those functions. Here's how that might look:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    prompt = gr.Textbox()\n    image = gr.Image()\n    generate_btn_1 = gr.Button(\"Generate Image via model 1\")\n    generate_btn_2 = gr.Button(\"Generate Image via model 2\")\n    generate_btn_3 = gr.Button(\"Generate Image via model 3\")\n    generate_btn_1.click(image_gen_1, prompt, image, concurrency_limit=2, concurrency_id=\"gpu_queue\")\n    generate_btn_2.click(image_gen_2, prompt, image, concurrency_id=\"gpu_queue\")\n    generate_btn_3.click(image_gen_3, prompt, image, concurrency_id=\"gpu_queue\")\n```\n\nIn this example, all three event listeners share a queue identified by `\"gpu_queue\"`. The queue can handle up to 2 concurrent requests at a time, as defined by the `concurrency_limit`.\n\nNotes\n\n- To ensure unlimited concurrency for an event listener, se", "heading1": "Configuring the Queue", "source_page_url": "https://gradio.app/guides/queuing", "source_page_title": "Additional Features - Queuing Guide"}, {"text": " identified by `\"gpu_queue\"`. The queue can handle up to 2 concurrent requests at a time, as defined by the `concurrency_limit`.\n\nNotes\n\n- To ensure unlimited concurrency for an event listener, set `concurrency_limit=None`.  This is useful if your function is calling e.g. an external API which handles the rate limiting of requests itself.\n- The default concurrency limit for all queues can be set globally using the `default_concurrency_limit` parameter in `Blocks.queue()`. \n\nThese configurations make it easy to manage the queuing behavior of your Gradio app.\n", "heading1": "Configuring the Queue", "source_page_url": "https://gradio.app/guides/queuing", "source_page_title": "Additional Features - Queuing Guide"}, {"text": "**API endpoint names**\n\nWhen you create a Gradio application, the API endpoint names are automatically generated based on the function names. You can change this by using the `api_name` parameter in `gr.Interface` or `gr.ChatInterface`. If you are using Gradio `Blocks`, you can name each event listener, like this:\n\n```python\nbtn.click(add, [num1, num2], output, api_name=\"addition\")\n```\n\n**Hiding API endpoints**\n\nWhen building a complex Gradio app, you might want to hide certain API endpoints from appearing on the view API page, e.g. if they correspond to functions that simply update the UI. You can set the  `show_api` parameter to `False` in any `Blocks` event listener to achieve this, e.g. \n\n```python\nbtn.click(add, [num1, num2], output, show_api=False)\n```\n\n**Disabling API endpoints**\n\nHiding the API endpoint doesn't disable it. A user can still programmatically call the API endpoint if they know the name. If you want to disable an API endpoint altogether, set `api_name=False`, e.g. \n\n```python\nbtn.click(add, [num1, num2], output, api_name=False)\n```\n\nNote: setting an `api_name=False` also means that downstream apps will not be able to load your Gradio app using `gr.load()` as this function uses the Gradio API under the hood.\n\n**Adding API endpoints**\n\nYou can also add new API routes to your Gradio application that do not correspond to events in your UI.\n\nFor example, in this Gradio application, we add a new route that adds numbers and slices a list:\n\n```py\nimport gradio as gr\nwith gr.Blocks() as demo:\n    with gr.Row():\n        input = gr.Textbox()\n        button = gr.Button(\"Submit\")\n    output = gr.Textbox()\n    def fn(a: int, b: int, c: list[str]) -> tuple[int, str]:\n        return a + b, c[a:b]\n    gr.api(fn, api_name=\"add_and_slice\")\n\n_, url, _ = demo.launch()\n```\n\nThis will create a new route `/add_and_slice` which will show up in the \"view API\" page. It can be programmatically called by the Python or JS Clients (discussed below) like this:\n\n```py\nfrom grad", "heading1": "Configuring the API Page", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "``\n\nThis will create a new route `/add_and_slice` which will show up in the \"view API\" page. It can be programmatically called by the Python or JS Clients (discussed below) like this:\n\n```py\nfrom gradio_client import Client\n\nclient = Client(url)\nresult = client.predict(\n        a=3,\n        b=5,\n        c=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],\n        api_name=\"/add_and_slice\"\n)\nprint(result)\n```\n\n", "heading1": "Configuring the API Page", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "This API page not only lists all of the endpoints that can be used to query the Gradio app, but also shows the usage of both [the Gradio Python client](https://gradio.app/guides/getting-started-with-the-python-client/), and [the Gradio JavaScript client](https://gradio.app/guides/getting-started-with-the-js-client/). \n\nFor each endpoint, Gradio automatically generates a complete code snippet with the parameters and their types, as well as example inputs, allowing you to immediately test an endpoint. Here's an example showing an image file input and `str` output:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-snippet.png)\n\n\n", "heading1": "The Clients", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "Instead of reading through the view API page, you can also use Gradio's built-in API recorder to generate the relevant code snippet. Simply click on the \"API Recorder\" button, use your Gradio app via the UI as you would normally, and then the API Recorder will generate the code using the Clients to recreate your all of your interactions programmatically.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/api-recorder.gif)\n\n", "heading1": "The API Recorder \ud83e\ude84", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "The API page also includes instructions on how to use the Gradio app as an Model Context Protocol (MCP) server, which is a standardized way to expose functions as tools so that they can be used by LLMs. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-mcp.png)\n\nFor the MCP sever, each tool, its description, and its parameters are listed, along with instructions on how to integrate with popular MCP Clients. Read more about Gradio's [MCP integration here](https://www.gradio.app/guides/building-mcp-server-with-gradio).\n\n", "heading1": "MCP Server", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "You can access the complete OpenAPI (formerly Swagger) specification of your Gradio app's API at the endpoint `<your-gradio-app-url>/gradio_api/openapi.json`. The OpenAPI specification is a standardized, language-agnostic interface description for REST APIs that enables both humans and computers to discover and understand the capabilities of your service.\n", "heading1": "OpenAPI Specification", "source_page_url": "https://gradio.app/guides/view-api-page", "source_page_title": "Additional Features - View Api Page Guide"}, {"text": "Gradio can stream audio and video directly from your generator function.\nThis lets your user hear your audio or see your video nearly as soon as it's `yielded` by your function.\nAll you have to do is \n\n1. Set `streaming=True` in your `gr.Audio` or `gr.Video` output component.\n2. Write a python generator that yields the next \"chunk\" of audio or video.\n3. Set `autoplay=True` so that the media starts playing automatically.\n\nFor audio, the next \"chunk\" can be either an `.mp3` or `.wav` file or a `bytes` sequence of audio.\nFor video, the next \"chunk\" has to be either `.mp4` file or a file with `h.264` codec with a `.ts` extension.\nFor smooth playback, make sure chunks are consistent lengths and larger than 1 second.\n\nWe'll finish with some simple examples illustrating these points.\n\nStreaming Audio\n\n```python\nimport gradio as gr\nfrom time import sleep\n\ndef keep_repeating(audio_file):\n    for _ in range(10):\n        sleep(0.5)\n        yield audio_file\n\ngr.Interface(keep_repeating,\n             gr.Audio(sources=[\"microphone\"], type=\"filepath\"),\n             gr.Audio(streaming=True, autoplay=True)\n).launch()\n```\n\nStreaming Video\n\n```python\nimport gradio as gr\nfrom time import sleep\n\ndef keep_repeating(video_file):\n    for _ in range(10):\n        sleep(0.5)\n        yield video_file\n\ngr.Interface(keep_repeating,\n             gr.Video(sources=[\"webcam\"], format=\"mp4\"),\n             gr.Video(streaming=True, autoplay=True)\n).launch()\n```\n\n", "heading1": "Streaming Media", "source_page_url": "https://gradio.app/guides/streaming-outputs", "source_page_title": "Additional Features - Streaming Outputs Guide"}, {"text": "For an end-to-end example of streaming media, see the object detection from video [guide](/main/guides/object-detection-from-video) or the streaming AI-generated audio with [transformers](https://huggingface.co/docs/transformers/index) [guide](/main/guides/streaming-ai-generated-audio).", "heading1": "End-to-End Examples", "source_page_url": "https://gradio.app/guides/streaming-outputs", "source_page_title": "Additional Features - Streaming Outputs Guide"}, {"text": "Gradio demos can be easily shared publicly by setting `share=True` in the `launch()` method. Like this:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n    return \"Hello \" + name + \"!\"\n\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n\ndemo.launch(share=True)  Share your demo with just 1 extra parameter \ud83d\ude80\n```\n\nThis generates a public, shareable link that you can send to anybody! When you send this link, the user on the other side can try out the model in their browser. Because the processing happens on your device (as long as your device stays on), you don't have to worry about any packaging any dependencies.\n\n![sharing](https://github.com/gradio-app/gradio/blob/main/guides/assets/sharing.svg?raw=true)\n\n\nA share link usually looks something like this: **https://07ff8706ab.gradio.live**. Although the link is served through the Gradio Share Servers, these servers are only a proxy for your local server, and do not store any data sent through your app. Share links expire after 1 week. (it is [also possible to set up your own Share Server](https://github.com/huggingface/frp/) on your own cloud server to overcome this restriction.)\n\nTip: Keep in mind that share links are publicly accessible, meaning that anyone can use your model for prediction! Therefore, make sure not to expose any sensitive information through the functions you write, or allow any critical changes to occur on your device. Or you can [add authentication to your Gradio app](authentication) as discussed below.\n\nNote that by default, `share=False`, which means that your server is only running locally. (This is the default, except in Google Colab notebooks, where share links are automatically created). As an alternative to using share links, you can use use [SSH port-forwarding](https://www.ssh.com/ssh/tunneling/example) to share your local server with specific users.\n\n\n", "heading1": "Sharing Demos", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "If you'd like to have a permanent link to your Gradio demo on the internet, use Hugging Face Spaces. [Hugging Face Spaces](http://huggingface.co/spaces/) provides the infrastructure to permanently host your machine learning model for free!\n\nAfter you have [created a free Hugging Face account](https://huggingface.co/join), you have two methods to deploy your Gradio app to Hugging Face Spaces:\n\n1. From terminal: run `gradio deploy` in your app directory. The CLI will gather some basic metadata and then launch your app. To update your space, you can re-run this command or enable the Github Actions option to automatically update the Spaces on `git push`.\n\n2. From your browser: Drag and drop a folder containing your Gradio model and all related files [here](https://huggingface.co/new-space). See [this guide how to host on Hugging Face Spaces](https://huggingface.co/blog/gradio-spaces) for more information, or watch the embedded video:\n\n<video autoplay muted loop>\n  <source src=\"https://github.com/gradio-app/gradio/blob/main/guides/assets/hf_demo.mp4?raw=true\" type=\"video/mp4\" />\n</video>\n\n", "heading1": "Hosting on HF Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "You can add a button to your Gradio app that creates a unique URL you can use to share your app and all components **as they currently are** with others. This is useful for sharing unique and interesting generations from your application , or for saving a snapshot of your app at a particular point in time.\n\nTo add a deep link button to your app, place the `gr.DeepLinkButton` component anywhere in your app.\nFor the URL to be accessible to others, your app must be available at a public URL. So be sure to host your app like Hugging Face Spaces or use the `share=True` parameter when launching your app.\n\nLet's see an example of how this works. Here's a simple Gradio chat ap that uses the `gr.DeepLinkButton` component. After a couple of messages, click the deep link button and paste it into a new browser tab to see the app as it is at that point in time.\n\n$code_deep_link\n$demo_deep_link\n\n\n", "heading1": "Sharing Deep Links", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "Once you have hosted your app on Hugging Face Spaces (or on your own server), you may want to embed the demo on a different website, such as your blog or your portfolio. Embedding an interactive demo allows people to try out the machine learning model that you have built, without needing to download or install anything \u2014 right in their browser! The best part is that you can embed interactive demos even in static websites, such as GitHub pages.\n\nThere are two ways to embed your Gradio demos. You can find quick links to both options directly on the Hugging Face Space page, in the \"Embed this Space\" dropdown option:\n\n![Embed this Space dropdown option](https://github.com/gradio-app/gradio/blob/main/guides/assets/embed_this_space.png?raw=true)\n\nEmbedding with Web Components\n\nWeb components typically offer a better experience to users than IFrames. Web components load lazily, meaning that they won't slow down the loading time of your website, and they automatically adjust their height based on the size of the Gradio app.\n\nTo embed with Web Components:\n\n1. Import the gradio JS library into into your site by adding the script below in your site (replace {GRADIO_VERSION} in the URL with the library version of Gradio you are using).\n\n```html\n<script\n\ttype=\"module\"\n\tsrc=\"https://gradio.s3-us-west-2.amazonaws.com/{GRADIO_VERSION}/gradio.js\"\n></script>\n```\n\n2. Add\n\n```html\n<gradio-app src=\"https://$your_space_host.hf.space\"></gradio-app>\n```\n\nelement where you want to place the app. Set the `src=` attribute to your Space's embed URL, which you can find in the \"Embed this Space\" button. For example:\n\n```html\n<gradio-app\n\tsrc=\"https://abidlabs-pytorch-image-classifier.hf.space\"\n></gradio-app>\n```\n\n<script>\nfetch(\"https://pypi.org/pypi/gradio/json\"\n).then(r => r.json()\n).then(obj => {\n    let v = obj.info.version;\n    content = document.querySelector('.prose');\n    content.innerHTML = content.innerHTML.replaceAll(\"{GRADIO_VERSION}\", v);\n});\n</script>\n\nYou can see examples of h", "heading1": "Embedding Hosted Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "=> {\n    let v = obj.info.version;\n    content = document.querySelector('.prose');\n    content.innerHTML = content.innerHTML.replaceAll(\"{GRADIO_VERSION}\", v);\n});\n</script>\n\nYou can see examples of how web components look <a href=\"https://www.gradio.app\">on the Gradio landing page</a>.\n\nYou can also customize the appearance and behavior of your web component with attributes that you pass into the `<gradio-app>` tag:\n\n- `src`: as we've seen, the `src` attributes links to the URL of the hosted Gradio demo that you would like to embed\n- `space`: an optional shorthand if your Gradio demo is hosted on Hugging Face Space. Accepts a `username/space_name` instead of a full URL. Example: `gradio/Echocardiogram-Segmentation`. If this attribute attribute is provided, then `src` does not need to be provided.\n- `control_page_title`: a boolean designating whether the html title of the page should be set to the title of the Gradio app (by default `\"false\"`)\n- `initial_height`: the initial height of the web component while it is loading the Gradio app, (by default `\"300px\"`). Note that the final height is set based on the size of the Gradio app.\n- `container`: whether to show the border frame and information about where the Space is hosted (by default `\"true\"`)\n- `info`: whether to show just the information about where the Space is hosted underneath the embedded app (by default `\"true\"`)\n- `autoscroll`: whether to autoscroll to the output when prediction has finished (by default `\"false\"`)\n- `eager`: whether to load the Gradio app as soon as the page loads (by default `\"false\"`)\n- `theme_mode`: whether to use the `dark`, `light`, or default `system` theme mode (by default `\"system\"`)\n- `render`: an event that is triggered once the embedded space has finished rendering.\n\nHere's an example of how to use these attributes to create a Gradio app that does not lazy load and has an initial height of 0px.\n\n```html\n<gradio-app\n\tspace=\"gradio/Echocardiogram-Segmentation\"\n\teager=\"true\"\n\tinit", "heading1": "Embedding Hosted Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "ple of how to use these attributes to create a Gradio app that does not lazy load and has an initial height of 0px.\n\n```html\n<gradio-app\n\tspace=\"gradio/Echocardiogram-Segmentation\"\n\teager=\"true\"\n\tinitial_height=\"0px\"\n></gradio-app>\n```\n\nHere's another example of how to use the `render` event. An event listener is used to capture the `render` event and will call the `handleLoadComplete()` function once rendering is complete.\n\n```html\n<script>\n\tfunction handleLoadComplete() {\n\t\tconsole.log(\"Embedded space has finished rendering\");\n\t}\n\n\tconst gradioApp = document.querySelector(\"gradio-app\");\n\tgradioApp.addEventListener(\"render\", handleLoadComplete);\n</script>\n```\n\n_Note: While Gradio's CSS will never impact the embedding page, the embedding page can affect the style of the embedded Gradio app. Make sure that any CSS in the parent page isn't so general that it could also apply to the embedded Gradio app and cause the styling to break. Element selectors such as `header { ... }` and `footer { ... }` will be the most likely to cause issues._\n\nEmbedding with IFrames\n\nTo embed with IFrames instead (if you cannot add javascript to your website, for example), add this element:\n\n```html\n<iframe src=\"https://$your_space_host.hf.space\"></iframe>\n```\n\nAgain, you can find the `src=` attribute to your Space's embed URL, which you can find in the \"Embed this Space\" button.\n\nNote: if you use IFrames, you'll probably want to add a fixed `height` attribute and set `style=\"border:0;\"` to remove the border. In addition, if your app requires permissions such as access to the webcam or the microphone, you'll need to provide that as well using the `allow` attribute.\n\n", "heading1": "Embedding Hosted Spaces", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "You can use almost any Gradio app as an API! In the footer of a Gradio app [like this one](https://huggingface.co/spaces/gradio/hello_world), you'll see a \"Use via API\" link.\n\n![Use via API](https://github.com/gradio-app/gradio/blob/main/guides/assets/use_via_api.png?raw=true)\n\nThis is a page that lists the endpoints that can be used to query the Gradio app, via our supported clients: either [the Python client](https://gradio.app/guides/getting-started-with-the-python-client/), or [the JavaScript client](https://gradio.app/guides/getting-started-with-the-js-client/). For each endpoint, Gradio automatically generates the parameters and their types, as well as example inputs, like this.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api.png)\n\nThe endpoints are automatically created when you launch a Gradio application. If you are using Gradio `Blocks`, you can also name each event listener, such as\n\n```python\nbtn.click(add, [num1, num2], output, api_name=\"addition\")\n```\n\nThis will add and document the endpoint `/addition/` to the automatically generated API page. Read more about the [API page here](./view-api-page).\n\n", "heading1": "API Page", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "When a user makes a prediction to your app, you may need the underlying network request, in order to get the request headers (e.g. for advanced authentication), log the client's IP address, getting the query parameters, or for other reasons. Gradio supports this in a similar manner to FastAPI: simply add a function parameter whose type hint is `gr.Request` and Gradio will pass in the network request as that parameter. Here is an example:\n\n```python\nimport gradio as gr\n\ndef echo(text, request: gr.Request):\n    if request:\n        print(\"Request headers dictionary:\", request.headers)\n        print(\"IP address:\", request.client.host)\n        print(\"Query parameters:\", dict(request.query_params))\n    return text\n\nio = gr.Interface(echo, \"textbox\", \"textbox\").launch()\n```\n\nNote: if your function is called directly instead of through the UI (this happens, for\nexample, when examples are cached, or when the Gradio app is called via API), then `request` will be `None`.\nYou should handle this case explicitly to ensure that your app does not throw any errors. That is why\nwe have the explicit check `if request`.\n\n", "heading1": "Accessing the Network Request Directly", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "In some cases, you might have an existing FastAPI app, and you'd like to add a path for a Gradio demo.\nYou can easily do this with `gradio.mount_gradio_app()`.\n\nHere's a complete example:\n\n$code_custom_path\n\nNote that this approach also allows you run your Gradio apps on custom paths (`http://localhost:8000/gradio` in the example above).\n\n\n", "heading1": "Mounting Within Another FastAPI App", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "Password-protected app\n\nYou may wish to put an authentication page in front of your app to limit who can open your app. With the `auth=` keyword argument in the `launch()` method, you can provide a tuple with a username and password, or a list of acceptable username/password tuples; Here's an example that provides password-based authentication for a single user named \"admin\":\n\n```python\ndemo.launch(auth=(\"admin\", \"pass1234\"))\n```\n\nFor more complex authentication handling, you can even pass a function that takes a username and password as arguments, and returns `True` to allow access, `False` otherwise.\n\nHere's an example of a function that accepts any login where the username and password are the same:\n\n```python\ndef same_auth(username, password):\n    return username == password\ndemo.launch(auth=same_auth)\n```\n\nIf you have multiple users, you may wish to customize the content that is shown depending on the user that is logged in. You can retrieve the logged in user by [accessing the network request directly](accessing-the-network-request-directly) as discussed above, and then reading the `.username` attribute of the request. Here's an example:\n\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n    return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as demo:\n    m = gr.Markdown()\n    demo.load(update_message, None, m)\n\ndemo.launch(auth=[(\"Abubakar\", \"Abubakar\"), (\"Ali\", \"Ali\")])\n```\n\nNote: For authentication to work properly, third party cookies must be enabled in your browser. This is not the case by default for Safari or for Chrome Incognito Mode.\n\nIf users visit the `/logout` page of your Gradio app, they will automatically be logged out and session cookies deleted. This allows you to add logout functionality to your Gradio app as well. Let's update the previous example to include a log out button:\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n    return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as ", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": " Let's update the previous example to include a log out button:\n\n```python\nimport gradio as gr\n\ndef update_message(request: gr.Request):\n    return f\"Welcome, {request.username}\"\n\nwith gr.Blocks() as demo:\n    m = gr.Markdown()\n    logout_button = gr.Button(\"Logout\", link=\"/logout\")\n    demo.load(update_message, None, m)\n\ndemo.launch(auth=[(\"Pete\", \"Pete\"), (\"Dawood\", \"Dawood\")])\n```\n\nNote: Gradio's built-in authentication provides a straightforward and basic layer of access control but does not offer robust security features for applications that require stringent access controls (e.g.  multi-factor authentication, rate limiting, or automatic lockout policies).\n\nOAuth (Login via Hugging Face)\n\nGradio natively supports OAuth login via Hugging Face. In other words, you can easily add a _\"Sign in with Hugging Face\"_ button to your demo, which allows you to get a user's HF username as well as other information from their HF profile. Check out [this Space](https://huggingface.co/spaces/Wauplin/gradio-oauth-demo) for a live demo.\n\nTo enable OAuth, you must set `hf_oauth: true` as a Space metadata in your README.md file. This will register your Space\nas an OAuth application on Hugging Face. Next, you can use `gr.LoginButton` to add a login button to\nyour Gradio app. Once a user is logged in with their HF account, you can retrieve their profile by adding a parameter of type\n`gr.OAuthProfile` to any Gradio function. The user profile will be automatically injected as a parameter value. If you want\nto perform actions on behalf of the user (e.g. list user's private repos, create repo, etc.), you can retrieve the user\ntoken by adding a parameter of type `gr.OAuthToken`. You must define which scopes you will use in your Space metadata\n(see [documentation](https://huggingface.co/docs/hub/spaces-oauthscopes) for more details).\n\nHere is a short example:\n\n$code_login_with_huggingface\n\nWhen the user clicks on the login button, they get redirected in a new page to authorize your ", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "docs/hub/spaces-oauthscopes) for more details).\n\nHere is a short example:\n\n$code_login_with_huggingface\n\nWhen the user clicks on the login button, they get redirected in a new page to authorize your Space.\n\n<center>\n<img src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/oauth_sign_in.png\" style=\"width:300px; max-width:80%\">\n</center>\n\nUsers can revoke access to their profile at any time in their [settings](https://huggingface.co/settings/connected-applications).\n\nAs seen above, OAuth features are available only when your app runs in a Space. However, you often need to test your app\nlocally before deploying it. To test OAuth features locally, your machine must be logged in to Hugging Face. Please run `huggingface-cli login` or set `HF_TOKEN` as environment variable with one of your access token. You can generate a new token in your settings page (https://huggingface.co/settings/tokens). Then, clicking on the `gr.LoginButton` will log in to your local Hugging Face profile, allowing you to debug your app with your Hugging Face account before deploying it to a Space.\n\n**Security Note**: It is important to note that adding a `gr.LoginButton` does not restrict users from using your app, in the same way that adding [username-password authentication](/guides/sharing-your-apppassword-protected-app) does. This means that users of your app who have not logged in with Hugging Face can still access and run events in your Gradio app -- the difference is that the `gr.OAuthProfile` or `gr.OAuthToken` will be `None` in the corresponding functions.\n\n\nOAuth (with external providers)\n\nIt is also possible to authenticate with external OAuth providers (e.g. Google OAuth) in your Gradio apps. To do this, first mount your Gradio app within a FastAPI app ([as discussed above](mounting-within-another-fast-api-app)). Then, you must write an *authentication function*, which gets the user's username from the OAuth provider and returns it. Th", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": " FastAPI app ([as discussed above](mounting-within-another-fast-api-app)). Then, you must write an *authentication function*, which gets the user's username from the OAuth provider and returns it. This function should be passed to the `auth_dependency` parameter in `gr.mount_gradio_app`.\n\nSimilar to [FastAPI dependency functions](https://fastapi.tiangolo.com/tutorial/dependencies/), the function specified by `auth_dependency` will run before any Gradio-related route in your FastAPI app. The function should accept a single parameter: the FastAPI `Request` and return either a string (representing a user's username) or `None`. If a string is returned, the user will be able to access the Gradio-related routes in your FastAPI app.\n\nFirst, let's show a simplistic example to illustrate the `auth_dependency` parameter:\n\n```python\nfrom fastapi import FastAPI, Request\nimport gradio as gr\n\napp = FastAPI()\n\ndef get_user(request: Request):\n    return request.headers.get(\"user\")\n\ndemo = gr.Interface(lambda s: f\"Hello {s}!\", \"textbox\", \"textbox\")\n\napp = gr.mount_gradio_app(app, demo, path=\"/demo\", auth_dependency=get_user)\n\nif __name__ == '__main__':\n    uvicorn.run(app)\n```\n\nIn this example, only requests that include a \"user\" header will be allowed to access the Gradio app. Of course, this does not add much security, since any user can add this header in their request.\n\nHere's a more complete example showing how to add Google OAuth to a Gradio app (assuming you've already created OAuth Credentials on the [Google Developer Console](https://console.cloud.google.com/project)):\n\n```python\nimport os\nfrom authlib.integrations.starlette_client import OAuth, OAuthError\nfrom fastapi import FastAPI, Depends, Request\nfrom starlette.config import Config\nfrom starlette.responses import RedirectResponse\nfrom starlette.middleware.sessions import SessionMiddleware\nimport uvicorn\nimport gradio as gr\n\napp = FastAPI()\n\nReplace these with your own OAuth settings\nGOOGLE_CLIENT_ID = \"...\"\nGOOGLE_C", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "Response\nfrom starlette.middleware.sessions import SessionMiddleware\nimport uvicorn\nimport gradio as gr\n\napp = FastAPI()\n\nReplace these with your own OAuth settings\nGOOGLE_CLIENT_ID = \"...\"\nGOOGLE_CLIENT_SECRET = \"...\"\nSECRET_KEY = \"...\"\n\nconfig_data = {'GOOGLE_CLIENT_ID': GOOGLE_CLIENT_ID, 'GOOGLE_CLIENT_SECRET': GOOGLE_CLIENT_SECRET}\nstarlette_config = Config(environ=config_data)\noauth = OAuth(starlette_config)\noauth.register(\n    name='google',\n    server_metadata_url='https://accounts.google.com/.well-known/openid-configuration',\n    client_kwargs={'scope': 'openid email profile'},\n)\n\nSECRET_KEY = os.environ.get('SECRET_KEY') or \"a_very_secret_key\"\napp.add_middleware(SessionMiddleware, secret_key=SECRET_KEY)\n\nDependency to get the current user\ndef get_user(request: Request):\n    user = request.session.get('user')\n    if user:\n        return user['name']\n    return None\n\n@app.get('/')\ndef public(user: dict = Depends(get_user)):\n    if user:\n        return RedirectResponse(url='/gradio')\n    else:\n        return RedirectResponse(url='/login-demo')\n\n@app.route('/logout')\nasync def logout(request: Request):\n    request.session.pop('user', None)\n    return RedirectResponse(url='/')\n\n@app.route('/login')\nasync def login(request: Request):\n    redirect_uri = request.url_for('auth')\n    If your app is running on https, you should ensure that the\n    `redirect_uri` is https, e.g. uncomment the following lines:\n    \n    from urllib.parse import urlparse, urlunparse\n    redirect_uri = urlunparse(urlparse(str(redirect_uri))._replace(scheme='https'))\n    return await oauth.google.authorize_redirect(request, redirect_uri)\n\n@app.route('/auth')\nasync def auth(request: Request):\n    try:\n        access_token = await oauth.google.authorize_access_token(request)\n    except OAuthError:\n        return RedirectResponse(url='/')\n    request.session['user'] = dict(access_token)[\"userinfo\"]\n    return RedirectResponse(url='/')\n\nwith gr.Blocks() as login_demo:\n    gr.Button(", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "t OAuthError:\n        return RedirectResponse(url='/')\n    request.session['user'] = dict(access_token)[\"userinfo\"]\n    return RedirectResponse(url='/')\n\nwith gr.Blocks() as login_demo:\n    gr.Button(\"Login\", link=\"/login\")\n\napp = gr.mount_gradio_app(app, login_demo, path=\"/login-demo\")\n\ndef greet(request: gr.Request):\n    return f\"Welcome to Gradio, {request.username}\"\n\nwith gr.Blocks() as main_demo:\n    m = gr.Markdown(\"Welcome to Gradio!\")\n    gr.Button(\"Logout\", link=\"/logout\")\n    main_demo.load(greet, None, m)\n\napp = gr.mount_gradio_app(app, main_demo, path=\"/gradio\", auth_dependency=get_user)\n\nif __name__ == '__main__':\n    uvicorn.run(app)\n```\n\nThere are actually two separate Gradio apps in this example! One that simply displays a log in button (this demo is accessible to any user), while the other main demo is only accessible to users that are logged in. You can try this example out on [this Space](https://huggingface.co/spaces/gradio/oauth-example).\n\n\n", "heading1": "Authentication", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "By default, Gradio collects certain analytics to help us better understand the usage of the `gradio` library. This includes the following information:\n\n* What environment the Gradio app is running on (e.g. Colab Notebook, Hugging Face Spaces)\n* What input/output components are being used in the Gradio app\n* Whether the Gradio app is utilizing certain advanced features, such as `auth` or `show_error`\n* The IP address which is used solely to measure the number of unique developers using Gradio\n* The version of Gradio that is running\n\nNo information is collected from _users_ of your Gradio app. If you'd like to disable analytics altogether, you can do so by setting the `analytics_enabled` parameter to `False` in `gr.Blocks`, `gr.Interface`, or `gr.ChatInterface`. Or, you can set the GRADIO_ANALYTICS_ENABLED environment variable to `\"False\"` to apply this to all Gradio apps created across your system.\n\n*Note*: this reflects the analytics policy as of `gradio>=4.32.0`.\n\n", "heading1": "Analytics", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "[Progressive Web Apps (PWAs)](https://developer.mozilla.org/en-US/docs/Web/Progressive_web_apps) are web applications that are regular web pages or websites, but can appear to the user like installable platform-specific applications.\n\nGradio apps can be easily served as PWAs by setting the `pwa=True` parameter in the `launch()` method. Here's an example:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n    return \"Hello \" + name + \"!\"\n\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n\ndemo.launch(pwa=True)  Launch your app as a PWA\n```\n\nThis will generate a PWA that can be installed on your device. Here's how it looks:\n\n![Installing PWA](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/install-pwa.gif)\n\nWhen you specify `favicon_path` in the `launch()` method, the icon will be used as the app's icon. Here's an example:\n\n```python\ndemo.launch(pwa=True, favicon_path=\"./hf-logo.svg\")  Use a custom icon for your PWA\n```\n\n![Custom PWA Icon](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/pwa-favicon.png)\n", "heading1": "Progressive Web App (PWA)", "source_page_url": "https://gradio.app/guides/sharing-your-app", "source_page_title": "Additional Features - Sharing Your App Guide"}, {"text": "- **1. Static files**. You can designate static files or directories using the `gr.set_static_paths` function. Static files  are not be copied to the Gradio cache (see below) and will be served directly from your computer. This can help save disk space and reduce the time your app takes to launch but be mindful of possible security implications as any static files are accessible to all useres of your Gradio app.\n\n- **2. Files in the `allowed_paths` parameter in `launch()`**. This parameter allows you to pass in a list of additional directories or exact filepaths you'd like to allow users to have access to. (By default, this parameter is an empty list).\n\n- **3. Files in Gradio's cache**. After you launch your Gradio app, Gradio copies certain files into a temporary cache and makes these files accessible to users. Let's unpack this in more detail below.\n\n\n", "heading1": "Files Gradio allows users to access", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "First, it's important to understand why Gradio has a cache at all. Gradio copies files to a cache directory before returning them to the frontend. This prevents files from being overwritten by one user while they are still needed by another user of your application. For example, if your prediction function returns a video file, then Gradio will move that video to the cache after your prediction function runs and returns a URL the frontend can use to show the video. Any file in the cache is available via URL to all users of your running application.\n\nTip: You can customize the location of the cache by setting the `GRADIO_TEMP_DIR` environment variable to an absolute path, such as `/home/usr/scripts/project/temp/`. \n\nFiles Gradio moves to the cache\n\nGradio moves three kinds of files into the cache\n\n1. Files specified by the developer before runtime, e.g. cached examples, default values of components, or files passed into parameters such as the `avatar_images` of `gr.Chatbot`\n\n2. File paths returned by a prediction function in your Gradio application, if they ALSO meet one of the conditions below:\n\n* It is in the `allowed_paths` parameter of the `Blocks.launch` method.\n* It is in the current working directory of the python interpreter.\n* It is in the temp directory obtained by `tempfile.gettempdir()`.\n\n**Note:** files in the current working directory whose name starts with a period (`.`) will not be moved to the cache, even if they are returned from a prediction function, since they often contain sensitive information. \n\nIf none of these criteria are met, the prediction function that is returning that file will raise an exception instead of moving the file to cache. Gradio performs this check so that arbitrary files on your machine cannot be accessed.\n\n3. Files uploaded by a user to your Gradio app (e.g. through the `File` or `Image` input components).\n\nTip: If at any time Gradio blocks a file that you would like it to process, add its path to the `allowed_paths` p", "heading1": "The Gradio cache", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "d by a user to your Gradio app (e.g. through the `File` or `Image` input components).\n\nTip: If at any time Gradio blocks a file that you would like it to process, add its path to the `allowed_paths` parameter.\n\n", "heading1": "The Gradio cache", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "While running, Gradio apps will NOT ALLOW users to access:\n\n- **Files that you explicitly block via the `blocked_paths` parameter in `launch()`**. You can pass in a list of additional directories or exact filepaths to the `blocked_paths` parameter in `launch()`. This parameter takes precedence over the files that Gradio exposes by default, or by the `allowed_paths` parameter or the `gr.set_static_paths` function.\n\n- **Any other paths on the host machine**. Users should NOT be able to access other arbitrary paths on the host.\n\n", "heading1": "The files Gradio will not allow others to access", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "Sharing your Gradio application will also allow users to upload files to your computer or server. You can set a maximum file size for uploads to prevent abuse and to preserve disk space. You can do this with the `max_file_size` parameter of `.launch`. For example, the following two code snippets limit file uploads to 5 megabytes per file.\n\n```python\nimport gradio as gr\n\ndemo = gr.Interface(lambda x: x, \"image\", \"image\")\n\ndemo.launch(max_file_size=\"5mb\")\nor\ndemo.launch(max_file_size=5 * gr.FileSize.MB)\n```\n\n", "heading1": "Uploading Files", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "* Set a `max_file_size` for your application.\n* Do not return arbitrary user input from a function that is connected to a file-based output component (`gr.Image`, `gr.File`, etc.). For example, the following interface would allow anyone to move an arbitrary file in your local directory to the cache: `gr.Interface(lambda s: s, \"text\", \"file\")`. This is because the user input is treated as an arbitrary file path. \n* Make `allowed_paths` as small as possible. If a path in `allowed_paths` is a directory, any file within that directory can be accessed. Make sure the entires of `allowed_paths` only contains files related to your application.\n* Run your gradio application from the same directory the application file is located in. This will narrow the scope of files Gradio will be allowed to move into the cache. For example, prefer `python app.py` to `python Users/sources/project/app.py`.\n\n\n", "heading1": "Best Practices", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "Both `gr.set_static_paths` and the `allowed_paths` parameter in launch expect absolute paths. Below is a minimal example to display a local `.png` image file in an HTML block.\n\n```txt\n\u251c\u2500\u2500 assets\n\u2502   \u2514\u2500\u2500 logo.png\n\u2514\u2500\u2500 app.py\n```\nFor the example directory structure, `logo.png` and any other files in the `assets` folder can be accessed from your Gradio app in `app.py` as follows:\n\n```python\nfrom pathlib import Path\n\nimport gradio as gr\n\ngr.set_static_paths(paths=[Path.cwd().absolute()/\"assets\"])\n\nwith gr.Blocks() as demo:\n    gr.HTML(\"<img src='/gradio_api/file=assets/logo.png'>\")\n\ndemo.launch()\n```\n", "heading1": "Example: Accessing local files", "source_page_url": "https://gradio.app/guides/file-access", "source_page_title": "Additional Features - File Access Guide"}, {"text": "When a user closes their browser tab, Gradio will automatically delete any `gr.State` variables associated with that user session after 60 minutes. If the user connects again within those 60 minutes, no state will be deleted.\n\nYou can control the deletion behavior further with the following two parameters of `gr.State`:\n\n1. `delete_callback` - An arbitrary function that will be called when the variable is deleted. This function must take the state value as input. This function is useful for deleting variables from GPU memory.\n2. `time_to_live` - The number of seconds the state should be stored for after it is created or updated. This will delete variables before the session is closed, so it's useful for clearing state for potentially long running sessions.\n\n", "heading1": "Automatic deletion of `gr.State`", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "Your Gradio application will save uploaded and generated files to a special directory called the cache directory. Gradio uses a hashing scheme to ensure that duplicate files are not saved to the cache but over time the size of the cache will grow (especially if your app goes viral \ud83d\ude09).\n\nGradio can periodically clean up the cache for you if you specify the `delete_cache` parameter of `gr.Blocks()`, `gr.Interface()`, or `gr.ChatInterface()`. \nThis parameter is a tuple of the form `[frequency, age]` both expressed in number of seconds.\nEvery `frequency` seconds, the temporary files created by this Blocks instance will be deleted if more than `age` seconds have passed since the file was created. \nFor example, setting this to (86400, 86400) will delete temporary files every day if they are older than a day old.\nAdditionally, the cache will be deleted entirely when the server restarts.\n\n", "heading1": "Automatic cache cleanup via `delete_cache`", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "Additionally, Gradio now includes a `Blocks.unload()` event, allowing you to run arbitrary cleanup functions when users disconnect (this does not have a 60 minute delay).\nUnlike other gradio events, this event does not accept inputs or outptus.\nYou can think of the `unload` event as the opposite of the `load` event.\n\n", "heading1": "The `unload` event", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "The following demo uses all of these features. When a user visits the page, a special unique directory is created for that user.\nAs the user interacts with the app, images are saved to disk in that special directory.\nWhen the user closes the page, the images created in that session are deleted via the `unload` event.\nThe state and files in the cache are cleaned up automatically as well.\n\n$code_state_cleanup\n$demo_state_cleanup", "heading1": "Putting it all together", "source_page_url": "https://gradio.app/guides/resource-cleanup", "source_page_title": "Additional Features - Resource Cleanup Guide"}, {"text": "Let's create a demo where a user can choose a filter to apply to their webcam stream. Users can choose from an edge-detection filter, a cartoon filter, or simply flipping the stream vertically.\n\n$code_streaming_filter\n$demo_streaming_filter\n\nYou will notice that if you change the filter value it will immediately take effect in the output stream. That is an important difference of stream events in comparison to other Gradio events. The input values of the stream can be changed while the stream is being processed. \n\nTip: We set the \"streaming\" parameter of the image output component to be \"True\". Doing so lets the server automatically convert our output images into base64 format, a format that is efficient for streaming.\n\n", "heading1": "A Realistic Image Demo", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "For some image streaming demos, like the one above, we don't need to display separate input and output components. Our app would look cleaner if we could just display the modified output stream.\n\nWe can do so by just specifying the input image component as the output of the stream event.\n\n$code_streaming_filter_unified\n$demo_streaming_filter_unified\n\n", "heading1": "Unified Image Demos", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "Your streaming function should be stateless. It should take the current input and return its corresponding output. However, there are cases where you may want to keep track of past inputs or outputs. For example, you may want to keep a buffer of the previous `k` inputs to improve the accuracy of your transcription demo. You can do this with Gradio's `gr.State()` component.\n\nLet's showcase this with a sample demo:\n\n```python\ndef transcribe_handler(current_audio, state, transcript):\n    next_text = transcribe(current_audio, history=state)\n    state.append(current_audio)\n    state = state[-3:]\n    return state, transcript + next_text\n\nwith gr.Blocks() as demo:\n    with gr.Row():\n        with gr.Column():\n            mic = gr.Audio(sources=\"microphone\")\n            state = gr.State(value=[])\n        with gr.Column():\n            transcript = gr.Textbox(label=\"Transcript\")\n    mic.stream(transcribe_handler, [mic, state, transcript], [state, transcript],\n               time_limit=10, stream_every=1)\n\n\ndemo.launch()\n```\n\n", "heading1": "Keeping track of past inputs or outputs", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "For an end-to-end example of streaming from the webcam, see the object detection from webcam [guide](/main/guides/object-detection-from-webcam-with-webrtc).", "heading1": "End-to-End Examples", "source_page_url": "https://gradio.app/guides/streaming-inputs", "source_page_title": "Additional Features - Streaming Inputs Guide"}, {"text": "1. `GRADIO_SERVER_PORT`\n\n- **Description**: Specifies the port on which the Gradio app will run.\n- **Default**: `7860`\n- **Example**:\n  ```bash\n  export GRADIO_SERVER_PORT=8000\n  ```\n\n2. `GRADIO_SERVER_NAME`\n\n- **Description**: Defines the host name for the Gradio server. To make Gradio accessible from any IP address, set this to `\"0.0.0.0\"`\n- **Default**: `\"127.0.0.1\"` \n- **Example**:\n  ```bash\n  export GRADIO_SERVER_NAME=\"0.0.0.0\"\n  ```\n\n3. `GRADIO_NUM_PORTS`\n\n- **Description**: Defines the number of ports to try when starting the Gradio server.\n- **Default**: `100`\n- **Example**:\n  ```bash\n  export GRADIO_NUM_PORTS=200\n  ```\n\n4. `GRADIO_ANALYTICS_ENABLED`\n\n- **Description**: Whether Gradio should provide \n- **Default**: `\"True\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_ANALYTICS_ENABLED=\"True\"\n  ```\n\n5. `GRADIO_DEBUG`\n\n- **Description**: Enables or disables debug mode in Gradio. If debug mode is enabled, the main thread does not terminate allowing error messages to be printed in environments such as Google Colab.\n- **Default**: `0`\n- **Example**:\n  ```sh\n  export GRADIO_DEBUG=1\n  ```\n\n6. `GRADIO_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag inputs/outputs in the Gradio interface. See [the Guide on flagging](/guides/using-flagging) for more details.\n- **Default**: `\"manual\"`\n- **Options**: `\"never\"`, `\"manual\"`, `\"auto\"`\n- **Example**:\n  ```sh\n  export GRADIO_FLAGGING_MODE=\"never\"\n  ```\n\n7. `GRADIO_TEMP_DIR`\n\n- **Description**: Specifies the directory where temporary files created by Gradio are stored.\n- **Default**: System default temporary directory\n- **Example**:\n  ```sh\n  export GRADIO_TEMP_DIR=\"/path/to/temp\"\n  ```\n\n8. `GRADIO_ROOT_PATH`\n\n- **Description**: Sets the root path for the Gradio application. Useful if running Gradio [behind a reverse proxy](/guides/running-gradio-on-your-web-server-with-nginx).\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_ROOT_PATH=", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "r the Gradio application. Useful if running Gradio [behind a reverse proxy](/guides/running-gradio-on-your-web-server-with-nginx).\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_ROOT_PATH=\"/myapp\"\n  ```\n\n9. `GRADIO_SHARE`\n\n- **Description**: Enables or disables sharing the Gradio app.\n- **Default**: `\"False\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_SHARE=\"True\"\n  ```\n\n10. `GRADIO_ALLOWED_PATHS`\n\n- **Description**: Sets a list of complete filepaths or parent directories that gradio is allowed to serve. Must be absolute paths. Warning: if you provide directories, any files in these directories or their subdirectories are accessible to all users of your app. Multiple items can be specified by separating items with commas.\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_ALLOWED_PATHS=\"/mnt/sda1,/mnt/sda2\"\n  ```\n\n11. `GRADIO_BLOCKED_PATHS`\n\n- **Description**: Sets a list of complete filepaths or parent directories that gradio is not allowed to serve (i.e. users of your app are not allowed to access). Must be absolute paths. Warning: takes precedence over `allowed_paths` and all other directories exposed by Gradio by default. Multiple items can be specified by separating items with commas.\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_BLOCKED_PATHS=\"/users/x/gradio_app/admin,/users/x/gradio_app/keys\"\n  ```\n\n12. `FORWARDED_ALLOW_IPS`\n\n- **Description**: This is not a Gradio-specific environment variable, but rather one used in server configurations, specifically `uvicorn` which is used by Gradio internally. This environment variable is useful when deploying applications behind a reverse proxy. It defines a list of IP addresses that are trusted to forward traffic to your application. When set, the application will trust the `X-Forwarded-For` header from these IP addresses to determine the original IP address of the user making the request. This means that if you use the `gr.Request` [objec", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": " the application will trust the `X-Forwarded-For` header from these IP addresses to determine the original IP address of the user making the request. This means that if you use the `gr.Request` [object's](https://www.gradio.app/docs/gradio/request) `client.host` property, it will correctly get the user's IP address instead of the IP address of the reverse proxy server. Note that only trusted IP addresses (i.e. the IP addresses of your reverse proxy servers) should be added, as any server with these IP addresses can modify the `X-Forwarded-For` header and spoof the client's IP address.\n- **Default**: `\"127.0.0.1\"`\n- **Example**:\n  ```sh\n  export FORWARDED_ALLOW_IPS=\"127.0.0.1,192.168.1.100\"\n  ```\n\n13. `GRADIO_CACHE_EXAMPLES`\n\n- **Description**: Whether or not to cache examples by default in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()` when no explicit argument is passed for the `cache_examples` parameter. You can set this environment variable to either the string \"true\" or \"false\".\n- **Default**: `\"false\"`\n- **Example**:\n  ```sh\n  export GRADIO_CACHE_EXAMPLES=\"true\"\n  ```\n\n\n14. `GRADIO_CACHE_MODE`\n\n- **Description**: How to cache examples. Only applies if `cache_examples` is set to `True` either via enviornment variable or by an explicit parameter, AND no no explicit argument is passed for the `cache_mode` parameter in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()`. Can be set to either the strings \"lazy\" or \"eager.\" If \"lazy\", examples are cached after their first use for all users of the app. If \"eager\", all examples are cached at app launch.\n\n- **Default**: `\"eager\"`\n- **Example**:\n  ```sh\n  export GRADIO_CACHE_MODE=\"lazy\"\n  ```\n\n\n15. `GRADIO_EXAMPLES_CACHE`\n\n- **Description**:  If you set `cache_examples=True` in `gr.Interface()`, `gr.ChatInterface()` or in `gr.Examples()`, Gradio will run your prediction function and save the results to disk. By default, this is in the `.gradio/cached_examples//` subdirectory within your", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "e()`, `gr.ChatInterface()` or in `gr.Examples()`, Gradio will run your prediction function and save the results to disk. By default, this is in the `.gradio/cached_examples//` subdirectory within your app's working directory. You can customize the location of cached example files created by Gradio by setting the environment variable `GRADIO_EXAMPLES_CACHE` to an absolute path or a path relative to your working directory.\n- **Default**: `\".gradio/cached_examples/\"`\n- **Example**:\n  ```sh\n  export GRADIO_EXAMPLES_CACHE=\"custom_cached_examples/\"\n  ```\n\n\n16. `GRADIO_SSR_MODE`\n\n- **Description**: Controls whether server-side rendering (SSR) is enabled. When enabled, the initial HTML is rendered on the server rather than the client, which can improve initial page load performance and SEO.\n\n- **Default**: `\"False\"` (except on Hugging Face Spaces, where this environment variable sets it to `True`)\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_SSR_MODE=\"True\"\n  ```\n\n17. `GRADIO_NODE_SERVER_NAME`\n\n- **Description**: Defines the host name for the Gradio node server. (Only applies if `ssr_mode` is set to `True`.)\n- **Default**: `GRADIO_SERVER_NAME` if it is set, otherwise `\"127.0.0.1\"`\n- **Example**:\n  ```sh\n  export GRADIO_NODE_SERVER_NAME=\"0.0.0.0\"\n  ```\n\n18. `GRADIO_NODE_NUM_PORTS`\n\n- **Description**: Defines the number of ports to try when starting the Gradio node server. (Only applies if `ssr_mode` is set to `True`.)\n- **Default**: `100`\n- **Example**:\n  ```sh\n  export GRADIO_NODE_NUM_PORTS=200\n  ```\n\n19. `GRADIO_RESET_EXAMPLES_CACHE`\n\n- **Description**: If set to \"True\", Gradio will delete and recreate the examples cache directory when the app starts instead of reusing the cached example if they already exist. \n- **Default**: `\"False\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_RESET_EXAMPLES_CACHE=\"True\"\n  ```\n\n20. `GRADIO_CHAT_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "e\"`\n- **Options**: `\"True\"`, `\"False\"`\n- **Example**:\n  ```sh\n  export GRADIO_RESET_EXAMPLES_CACHE=\"True\"\n  ```\n\n20. `GRADIO_CHAT_FLAGGING_MODE`\n\n- **Description**: Controls whether users can flag messages in `gr.ChatInterface` applications. Similar to `GRADIO_FLAGGING_MODE` but specifically for chat interfaces.\n- **Default**: `\"never\"`\n- **Options**: `\"never\"`, `\"manual\"`\n- **Example**:\n  ```sh\n  export GRADIO_CHAT_FLAGGING_MODE=\"manual\"\n  ```\n\n21. `GRADIO_WATCH_DIRS`\n\n- **Description**: Specifies directories to watch for file changes when running Gradio in development mode. When files in these directories change, the Gradio app will automatically reload. Multiple directories can be specified by separating them with commas. This is primarily used by the `gradio` CLI command for development workflows.\n- **Default**: `\"\"`\n- **Example**:\n  ```sh\n  export GRADIO_WATCH_DIRS=\"/path/to/src,/path/to/templates\"\n  ```\n\n22. `GRADIO_VIBE_MODE`\n\n- **Description**: Enables the Vibe editor mode, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. When enabled, anyone who can access the Gradio endpoint can modify files and run arbitrary code on the host machine. Use with extreme caution in production environments.\n- **Default**: `\"\"`\n- **Options**: Any non-empty string enables the mode\n- **Example**:\n  ```sh\n  export GRADIO_VIBE_MODE=\"1\"\n  ```\n\n\n\n", "heading1": "Key Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "To set environment variables in your terminal, use the `export` command followed by the variable name and its value. For example:\n\n```sh\nexport GRADIO_SERVER_PORT=8000\n```\n\nIf you're using a `.env` file to manage your environment variables, you can add them like this:\n\n```sh\nGRADIO_SERVER_PORT=8000\nGRADIO_SERVER_NAME=\"localhost\"\n```\n\nThen, use a tool like `dotenv` to load these variables when running your application.\n\n\n\n", "heading1": "How to Set Environment Variables", "source_page_url": "https://gradio.app/guides/environment-variables", "source_page_title": "Additional Features - Environment Variables Guide"}, {"text": "You can initialize the `I18n` class with multiple language dictionaries to add custom translations:\n\n```python\nimport gradio as gr\n\nCreate an I18n instance with translations for multiple languages\ni18n = gr.I18n(\n    en={\"greeting\": \"Hello, welcome to my app!\", \"submit\": \"Submit\"},\n    es={\"greeting\": \"\u00a1Hola, bienvenido a mi aplicaci\u00f3n!\", \"submit\": \"Enviar\"},\n    fr={\"greeting\": \"Bonjour, bienvenue dans mon application!\", \"submit\": \"Soumettre\"}\n)\n\nwith gr.Blocks() as demo:\n    Use the i18n method to translate the greeting\n    gr.Markdown(i18n(\"greeting\"))\n    with gr.Row():\n        input_text = gr.Textbox(label=\"Input\")\n        output_text = gr.Textbox(label=\"Output\")\n    \n    submit_btn = gr.Button(i18n(\"submit\"))\n\nPass the i18n instance to the launch method\ndemo.launch(i18n=i18n)\n```\n\n", "heading1": "Setting Up Translations", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "When you use the `i18n` instance with a translation key, Gradio will show the corresponding translation to users based on their browser's language settings or the language they've selected in your app.\n\nIf a translation isn't available for the user's locale, the system will fall back to English (if available) or display the key itself.\n\n", "heading1": "How It Works", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "Locale codes should follow the BCP 47 format (e.g., 'en', 'en-US', 'zh-CN'). The `I18n` class will warn you if you use an invalid locale code.\n\n", "heading1": "Valid Locale Codes", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "The following component properties typically support internationalization:\n\n- `description`\n- `info`\n- `title`\n- `placeholder`\n- `value`\n- `label`\n\nNote that support may vary depending on the component, and some properties might have exceptions where internationalization is not applicable. You can check this by referring to the typehint for the parameter and if it contains `I18nData`, then it supports internationalization.", "heading1": "Supported Component Properties", "source_page_url": "https://gradio.app/guides/internationalization", "source_page_title": "Additional Features - Internationalization Guide"}, {"text": "Client side functions are ideal for updating component properties (like visibility, placeholders, interactive state, or styling). \n\nHere's a basic example:\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    with gr.Row() as row:\n        btn = gr.Button(\"Hide this row\")\n    \n    This function runs in the browser without a server roundtrip\n    btn.click(\n        lambda: gr.Row(visible=False), \n        None, \n        row, \n        js=True\n    )\n\ndemo.launch()\n```\n\n\n", "heading1": "When to Use Client Side Functions", "source_page_url": "https://gradio.app/guides/client-side-functions", "source_page_title": "Additional Features - Client Side Functions Guide"}, {"text": "Client side functions have some important restrictions:\n* They can only update component properties (not values)\n* They cannot take any inputs\n\nHere are some functions that will work with `js=True`:\n\n```py\nSimple property updates\nlambda: gr.Textbox(lines=4)\n\nMultiple component updates\nlambda: [gr.Textbox(lines=4), gr.Button(interactive=False)]\n\nUsing gr.update() for property changes\nlambda: gr.update(visible=True, interactive=False)\n```\n\nWe are working to increase the space of functions that can be transpiled to JavaScript so that they can be run in the browser. [Follow the Groovy library for more info](https://github.com/abidlabs/groovy-transpiler).\n\n\n", "heading1": "Limitations", "source_page_url": "https://gradio.app/guides/client-side-functions", "source_page_title": "Additional Features - Client Side Functions Guide"}, {"text": "Here's a more complete example showing how client side functions can improve the user experience:\n\n$code_todo_list_js\n\n\n", "heading1": "Complete Example", "source_page_url": "https://gradio.app/guides/client-side-functions", "source_page_title": "Additional Features - Client Side Functions Guide"}, {"text": "When you set `js=True`, Gradio:\n\n1. Transpiles your Python function to JavaScript\n\n2. Runs the function directly in the browser\n\n3. Still sends the request to the server (for consistency and to handle any side effects)\n\nThis provides immediate visual feedback while ensuring your application state remains consistent.\n", "heading1": "Behind the Scenes", "source_page_url": "https://gradio.app/guides/client-side-functions", "source_page_title": "Additional Features - Client Side Functions Guide"}, {"text": "As of version 5.36.0, Gradio now comes with a built-in MCP server that can upload files to a running Gradio application. In the `View API` page of the server, you should see the following code snippet if any of the tools require file inputs:\n\n<img src=\"https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/MCPConnectionDocs.png\">\n\nThe command to start the MCP server takes two arguments:\n\n- The URL (or Hugging Face space id) of the gradio application to upload the files to. In this case, `http://127.0.0.1:7860`.\n- The local directory on your computer with which the server is allowed to upload files from (`<UPLOAD_DIRECTORY>`). For security, please make this directory as narrow as possible to prevent unintended file uploads.\n\nAs stated in the image, you need to install [uv](https://docs.astral.sh/uv/getting-started/installation/) (a python package manager that can run python scripts) before connecting from your MCP client. \n\nIf you have gradio installed locally and you don't want to install uv, you can replace the `uvx` command with the path to gradio binary. It should look like this:\n\n```json\n\"upload-files\": {\n    \"command\": \"<absoluate-path-to-gradio>\",\n    \"args\": [\n    \"upload-mcp\",\n    \"http://localhost:7860/\",\n    \"/Users/freddyboulton/Pictures\"\n    ]\n}\n```\n\nAfter connecting to the upload server, your LLM agent will know when to upload files for you automatically!\n\n<img src=\"https://huggingface.co/datasets/freddyaboulton/bucket/resolve/main/Ghibliafy.png\">\n\n", "heading1": "Using the File Upload MCP Server", "source_page_url": "https://gradio.app/guides/file-upload-mcp", "source_page_title": "Mcp - File Upload Mcp Guide"}, {"text": "In this guide, we've covered how you can connect to the Upload File MCP Server so that your agent can upload files before using Gradio MCP servers. Remember to set the `<UPLOAD_DIRECTORY>` as small as possible to prevent unintended file uploads!\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/file-upload-mcp", "source_page_title": "Mcp - File Upload Mcp Guide"}, {"text": "An MCP (Model Control Protocol) server is a standardized way to expose tools so that they can be used by  LLMs. A tool can provide an LLM functionality that it does not have natively, such as the ability to generate images or calculate the prime factors of a number. \n\n", "heading1": "What is an MCP Server?", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "LLMs are famously not great at counting the number of letters in a word (e.g. the number of \"r\"-s in \"strawberry\"). But what if we equip them with a tool to help? Let's start by writing a simple Gradio app that counts the number of letters in a word or phrase:\n\n$code_letter_counter\n\nNotice that we have: (1) included a detailed docstring for our function, and (2) set `mcp_server=True` in `.launch()`. This is all that's needed for your Gradio app to serve as an MCP server! Now, when you run this app, it will:\n\n1. Start the regular Gradio web interface\n2. Start the MCP server\n3. Print the MCP server URL in the console\n\nThe MCP server will be accessible at:\n```\nhttp://your-server:port/gradio_api/mcp/sse\n```\n\nGradio automatically converts the `letter_counter` function into an MCP tool that can be used by LLMs. The docstring of the function and the type hints of arguments will be used to generate the description of the tool and its parameters. The name of the function will be used as the name of your tool. Any initial values you provide to your input components (e.g. \"strawberry\" and \"r\" in the `gr.Textbox` components above) will be used as the default values if your LLM doesn't specify a value for that particular input parameter.\n\nNow, all you need to do is add this URL endpoint to your MCP Client (e.g. Claude Desktop, Cursor, or Cline), which typically means pasting this config in the settings:\n\n```\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"http://your-server:port/gradio_api/mcp/sse\"\n    }\n  }\n}\n```\n\n(By the way, you can find the exact config to copy-paste by going to the \"View API\" link in the footer of your Gradio app, and then clicking on \"MCP\").\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/view-api-mcp.png)\n\n", "heading1": "Example: Counting Letters in a Word", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "1. **Tool Conversion**: Each API endpoint in your Gradio app is automatically converted into an MCP tool with a corresponding name, description, and input schema. To view the tools and schemas, visit http://your-server:port/gradio_api/mcp/schema or go to the \"View API\" link in the footer of your Gradio app, and then click on \"MCP\".\n\n\n2. **Environment variable support**. There are two ways to enable the MCP server functionality:\n\n*  Using the `mcp_server` parameter, as shown above:\n   ```python\n   demo.launch(mcp_server=True)\n   ```\n\n* Using environment variables:\n   ```bash\n   export GRADIO_MCP_SERVER=True\n   ```\n\n3. **File Handling**: The Gradio MCP server automatically handles file data conversions, including:\n   - Processing image files and returning them in the correct format\n   - Managing temporary file storage\n\n    By default, the Gradio MCP server accepts input images and files as full URLs (\"http://...\" or \"https:/...\"). For convenience, an additional STDIO-based MCP server is also generated, which can be used to upload files to any remote Gradio app and which returns a URL that can be used for subsequent tool calls.\n\n4. **Hosted MCP Servers on \udb40\udc20\ud83e\udd17 Spaces**: You can publish your Gradio application for free on Hugging Face Spaces, which will allow you to have a free hosted MCP server. Here's an example of such a Space: https://huggingface.co/spaces/abidlabs/mcp-tools. Notice that you can add this config to your MCP Client to start using the tools from this Space immediately:\n\n```\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"https://abidlabs-mcp-tools.hf.space/gradio_api/mcp/sse\"\n    }\n  }\n}\n```\n\n<video src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/mcp_guide1.mp4\" style=\"width:100%\" controls preload> </video>\n\n\n", "heading1": "Key features of the Gradio <> MCP Integration", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "If there's an existing Space that you'd like to use an MCP server, you'll need to do three things:\n\n1. First, [duplicate the Space](https://huggingface.co/docs/hub/en/spaces-more-ways-to-createduplicating-a-space) if it is not your own Space. This will allow you to make changes to the app. If the Space requires a GPU, set the hardware of the duplicated Space to be same as the original Space. You can make it either a public Space or a private Space, since it is possible to use either as an MCP server, as described below.\n2. Then, add docstrings to the functions that you'd like the LLM to be able to call as a tool. The docstring should be in the same format as the example code above.\n3. Finally, add `mcp_server=True` in `.launch()`.\n\nThat's it!\n\n", "heading1": "Converting an Existing Space", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "You can use either a public Space or a private Space as an MCP server. If you'd like to use a private Space as an MCP server (or a ZeroGPU Space with your own quota), then you will need to provide your [Hugging Face token](https://huggingface.co/settings/token) when you make your request. To do this, simply add it as a header in your config like this:\n\n```\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"https://abidlabs-mcp-tools.hf.space/gradio_api/mcp/sse\",\n      \"headers\": {\n        \"Authorization\": \"Bearer <YOUR-HUGGING-FACE-TOKEN>\"\n      }\n    }\n  }\n}\n```\n\n", "heading1": "Private Spaces", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "You may wish to authenticate users more precisely or let them provide other kinds of credentials or tokens in order to provide a custom experience for different users. \n\nGradio allows you to access the underlying `starlette.Request` that has made the tool call, which means that you can access headers, originating IP address, or any other information that is part of the network request. To do this, simply add a parameter in your function of the type `gr.Request`, and Gradio will automatically inject the request object as the parameter.\n\nHere's an example:\n\n```py\nimport gradio as gr\n\ndef echo_headers(x, request: gr.Request):\n    return str(dict(request.headers))\n\ngr.Interface(echo_headers, \"textbox\", \"textbox\").launch(mcp_server=True)\n```\n\nThis MCP server will simply ignore the user's input and echo back all of the headers from a user's request. One can build more complex apps using the same idea. See the [docs on `gr.Request`](https://www.gradio.app/main/docs/gradio/request) for more information (note that only the core Starlette attributes of the `gr.Request` object will be present, attributes such as Gradio's `.session_hash` will not be present).\n\nUsing the gr.Header class\n\nA common pattern in MCP server development is to use authentication headers to call services on behalf of your users. Instead of using a `gr.Request` object like in the example above, you can use a `gr.Header` argument. Gradio will automatically extract that header from the incoming request (if it exists) and pass it to your function.\n\nIn the example below, the `X-API-Token` header is extracted from the incoming request and passed in as the `x_api_token` argument to `make_api_request_on_behalf_of_user`.\n\nThe benefit of using `gr.Header` is that the MCP connection docs will automatically display the headers you need to supply when connecting to the server! See the image below:\n\n```python\nimport gradio as gr\n\ndef make_api_request_on_behalf_of_user(prompt: str, x_api_token: gr.Header):\n    \"\"\"M", "heading1": "Authentication and Credentials", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "the headers you need to supply when connecting to the server! See the image below:\n\n```python\nimport gradio as gr\n\ndef make_api_request_on_behalf_of_user(prompt: str, x_api_token: gr.Header):\n    \"\"\"Make a request to everyone's favorite API.\n    Args:\n        prompt: The prompt to send to the API.\n    Returns:\n        The response from the API.\n    Raises:\n        AssertionError: If the API token is not valid.\n    \"\"\"\n    return \"Hello from the API\" if not x_api_token else \"Hello from the API with token!\"\n\n\ndemo = gr.Interface(\n    make_api_request_on_behalf_of_user,\n    [\n        gr.Textbox(label=\"Prompt\"),\n    ],\n    gr.Textbox(label=\"Response\"),\n)\n\ndemo.launch(mcp_server=True)\n```\n\n![MCP Header Connection Page](https://github.com/user-attachments/assets/e264eedf-a91a-476b-880d-5be0d5934134)\n\nSending Progress Updates\n\nThe Gradio MCP server automatically sends progress updates to your MCP Client based on the queue in the Gradio application. If you'd like to send custom progress updates, you can do so using the same mechanism as you would use to display progress updates in the UI of your Gradio app: by using the `gr.Progress` class!\n\nHere's an example of how to do this:\n\n$code_mcp_progress\n\n[Here are the docs](https://www.gradio.app/docs/gradio/progress) for the `gr.Progress` class, which can also automatically track `tqdm` calls.\n\n\n", "heading1": "Authentication and Credentials", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "Gradio automatically sets the tool name based on the name of your function, and the description from the docstring of your function. But you may want to change how the description appears to your LLM. You can do this by using the `api_description` parameter in `Interface`, `ChatInterface`, or any event listener. This parameter takes three different kinds of values:\n\n* `None` (default): the tool description is automatically created from the docstring of the function (or its parent's docstring if it does not have a docstring but inherits from a method that does.)\n* `False`: no tool description appears to the LLM.\n* `str`: an arbitrary string to use as the tool description.\n\nIn addition to modifying the tool descriptions, you can also toggle which tools appear to the LLM. You can do this by setting the `show_api` parameter, which is by default `True`. Setting it to `False` hides the endpoint from the API docs and from the MCP server. If you expose multiple tools, users of your app will also be able to toggle which tools they'd like to add to their MCP server by checking boxes in the \"view MCP or API\" panel.\n\nHere's an example that shows the `api_description` and `show_api` parameters in actions:\n\n$code_mcp_tools\n\n", "heading1": "Modifying Tool Descriptions", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "So far, all of our MCP tools have corresponded to event listeners in the UI. This works well for functions that directly update the UI, but may not work if you wish to expose a \"pure logic\" function that should return raw data (e.g. a JSON object) without directly causing a UI update.\n\nIn order to expose such an MCP tool, you can create a pure Gradio API endpoint using `gr.api` (see [full docs here](https://www.gradio.app/main/docs/gradio/api)). Here's an example of creating an MCP tool that slices a list:\n\n$code_mcp_tool_only\n\nNote that if you use this approach, your function signature must be fully typed, including the return value, as these signature are used to determine the typing information for the MCP tool.\n\n\n", "heading1": "Adding MCP-Only Tools", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "In some cases, you may decide not to use Gradio's built-in integration and instead manually create an FastMCP Server that calls a Gradio app. This approach is useful when you want to:\n\n- Store state / identify users between calls instead of treating every tool call completely independently\n- Start the Gradio app MCP server when a tool is called (if you are running multiple Gradio apps locally and want to save memory / GPU)\n\nThis is very doable thanks to the [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) and the [MCP Python SDK](https://github.com/modelcontextprotocol/python-sdk)'s `FastMCP` class. Here's an example of creating a custom MCP server that connects to various Gradio apps hosted on [HuggingFace Spaces](https://huggingface.co/spaces) using the `stdio` protocol:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nfrom gradio_client import Client\nimport sys\nimport io\nimport json \n\nmcp = FastMCP(\"gradio-spaces\")\n\nclients = {}\n\ndef get_client(space_id: str) -> Client:\n    \"\"\"Get or create a Gradio client for the specified space.\"\"\"\n    if space_id not in clients:\n        clients[space_id] = Client(space_id)\n    return clients[space_id]\n\n\n@mcp.tool()\nasync def generate_image(prompt: str, space_id: str = \"ysharma/SanaSprint\") -> str:\n    \"\"\"Generate an image using Flux.\n    \n    Args:\n        prompt: Text prompt describing the image to generate\n        space_id: HuggingFace Space ID to use \n    \"\"\"\n    client = get_client(space_id)\n    result = client.predict(\n            prompt=prompt,\n            model_size=\"1.6B\",\n            seed=0,\n            randomize_seed=True,\n            width=1024,\n            height=1024,\n            guidance_scale=4.5,\n            num_inference_steps=2,\n            api_name=\"/infer\"\n    )\n    return result\n\n\n@mcp.tool()\nasync def run_dia_tts(prompt: str, space_id: str = \"ysharma/Dia-1.6B\") -> str:\n    \"\"\"Text-to-Speech Synthesis.\n    \n    Args:\n        prompt: Text prompt describing the co", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "return result\n\n\n@mcp.tool()\nasync def run_dia_tts(prompt: str, space_id: str = \"ysharma/Dia-1.6B\") -> str:\n    \"\"\"Text-to-Speech Synthesis.\n    \n    Args:\n        prompt: Text prompt describing the conversation between speakers S1, S2\n        space_id: HuggingFace Space ID to use \n    \"\"\"\n    client = get_client(space_id)\n    result = client.predict(\n            text_input=f\"\"\"{prompt}\"\"\",\n            audio_prompt_input=None, \n            max_new_tokens=3072,\n            cfg_scale=3,\n            temperature=1.3,\n            top_p=0.95,\n            cfg_filter_top_k=30,\n            speed_factor=0.94,\n            api_name=\"/generate_audio\"\n    )\n    return result\n\n\nif __name__ == \"__main__\":\n    import sys\n    import io\n    sys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8')\n    \n    mcp.run(transport='stdio')\n```\n\nThis server exposes two tools:\n1. `run_dia_tts` - Generates a conversation for the given transcript in the form of `[S1]first-sentence. [S2]second-sentence. [S1]...`\n2. `generate_image` - Generates images using a fast text-to-image model\n\nTo use this MCP Server with Claude Desktop (as MCP Client):\n\n1. Save the code to a file (e.g., `gradio_mcp_server.py`)\n2. Install the required dependencies: `pip install mcp gradio-client`\n3. Configure Claude Desktop to use your server by editing the configuration file at `~/Library/Application Support/Claude/claude_desktop_config.json` (macOS) or `%APPDATA%\\Claude\\claude_desktop_config.json` (Windows):\n\n```json\n{\n    \"mcpServers\": {\n        \"gradio-spaces\": {\n            \"command\": \"python\",\n            \"args\": [\n                \"/absolute/path/to/gradio_mcp_server.py\"\n            ]\n        }\n    }\n}\n```\n\n4. Restart Claude Desktop\n\nNow, when you ask Claude about generating an image or transcribing audio, it can use your Gradio-powered tools to accomplish these tasks.\n\n\n", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "use your Gradio-powered tools to accomplish these tasks.\n\n\n", "heading1": "Gradio with FastMCP", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "The MCP protocol is still in its infancy and you might see issues connecting to an MCP Server that you've built. We generally recommend using the [MCP Inspector Tool](https://github.com/modelcontextprotocol/inspector) to try connecting and debugging your MCP Server.\n\nHere are some things that may help:\n\n**1. Ensure that you've provided type hints and valid docstrings for your functions**\n\nAs mentioned earlier, Gradio reads the docstrings for your functions and the type hints of input arguments to generate the description of the tool and parameters. A valid function and docstring looks like this (note the \"Args:\" block with indented parameter names underneath):\n\n```py\ndef image_orientation(image: Image.Image) -> str:\n    \"\"\"\n    Returns whether image is portrait or landscape.\n\n    Args:\n        image (Image.Image): The image to check.\n    \"\"\"\n    return \"Portrait\" if image.height > image.width else \"Landscape\"\n```\n\nNote: You can preview the schema that is created for your MCP server by visiting the `http://your-server:port/gradio_api/mcp/schema` URL.\n\n**2. Try accepting input arguments as `str`**\n\nSome MCP Clients do not recognize parameters that are numeric or other complex types, but all of the MCP Clients that we've tested accept `str` input parameters. When in doubt, change your input parameter to be a `str` and then cast to a specific type in the function, as in this example:\n\n```py\ndef prime_factors(n: str):\n    \"\"\"\n    Compute the prime factorization of a positive integer.\n\n    Args:\n        n (str): The integer to factorize. Must be greater than 1.\n    \"\"\"\n    n_int = int(n)\n    if n_int <= 1:\n        raise ValueError(\"Input must be an integer greater than 1.\")\n\n    factors = []\n    while n_int % 2 == 0:\n        factors.append(2)\n        n_int //= 2\n\n    divisor = 3\n    while divisor * divisor <= n_int:\n        while n_int % divisor == 0:\n            factors.append(divisor)\n            n_int //= divisor\n        divisor += 2\n\n    if n_int > 1:\n        factors.", "heading1": "Troubleshooting your MCP Servers", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "= 3\n    while divisor * divisor <= n_int:\n        while n_int % divisor == 0:\n            factors.append(divisor)\n            n_int //= divisor\n        divisor += 2\n\n    if n_int > 1:\n        factors.append(n_int)\n\n    return factors\n```\n\n**3. Ensure that your MCP Client Supports SSE**\n\nSome MCP Clients, notably [Claude Desktop](https://claude.ai/download), do not yet support SSE-based MCP Servers. In those cases, you can use a tool such as [mcp-remote](https://github.com/geelen/mcp-remote). First install [Node.js](https://nodejs.org/en/download/). Then, add the following to your own MCP Client config:\n\n```\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"command\": \"npx\",\n      \"args\": [\n        \"mcp-remote\",\n        \"http://your-server:port/gradio_api/mcp/sse\"\n      ]\n    }\n  }\n}\n```\n\n**4. Restart your MCP Client and MCP Server**\n\nSome MCP Clients require you to restart them every time you update the MCP configuration. Other times, if the connection between the MCP Client and servers breaks, you might need to restart the MCP server. If all else fails, try restarting both your MCP Client and MCP Servers!\n\n", "heading1": "Troubleshooting your MCP Servers", "source_page_url": "https://gradio.app/guides/building-mcp-server-with-gradio", "source_page_title": "Mcp - Building Mcp Server With Gradio Guide"}, {"text": "If you're using LLMs in your workflow, adding this server will augment them with just the right context on gradio - which makes your experience a lot faster and smoother. \n\n<video src=\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/mcp-docs.mp4\" style=\"width:100%\" controls preload> </video>\n\nThe server is running on Spaces and was launched entirely using Gradio, you can see all the code [here](https://huggingface.co/spaces/gradio/docs-mcp). For more on building an mcp server with gradio, see the [previous guide](./building-an-mcp-client-with-gradio). \n\n", "heading1": "Why an MCP Server?", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "For clients that support SSE (e.g. Cursor, Windsurf, Cline), simply add the following configuration to your MCP config:\n\n```json\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/sse\"\n    }\n  }\n}\n```\n\nWe've included step-by-step instructions for Cursor below, but you can consult the docs for Windsurf [here](https://docs.windsurf.com/windsurf/mcp), and Cline [here](https://docs.cline.bot/mcp-servers/configuring-mcp-servers) which are similar to set up. \n\n\n\nCursor \n\n1. Make sure you're using the latest version of Cursor, and go to Cursor > Settings > Cursor Settings > MCP \n2. Click on '+ Add new global MCP server' \n3. Copy paste this json into the file that opens and then save it. \n```json\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"url\": \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/sse\"\n    }\n  }\n}\n```\n4. That's it! You should see the tools load and the status go green in the settings page. You may have to click the refresh icon or wait a few seconds. \n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/cursor-mcp.png)\n\nClaude Desktop\n\n1. Since Claude Desktop only supports stdio, you will need to [install Node.js](https://nodejs.org/en/download/) to get this to work. \n2. Make sure you're using the latest version of Claude Desktop, and go to Claude > Settings > Developer > Edit Config \n3. Open the file with your favorite editor and copy paste this json, then save the file. \n```json\n{\n  \"mcpServers\": {\n    \"gradio\": {\n      \"command\": \"npx\",\n      \"args\": [\n        \"mcp-remote\",\n        \"https://gradio-docs-mcp.hf.space/gradio_api/mcp/sse\",\n        \"--transport\",\n        \"sse-only\"\n      ]\n    }\n  }\n}\n```\n4. Quit and re-open Claude Desktop, and you should be good to go. You should see it loaded in the Search and Tools icon or on the developer settings page. \n \n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/claude-deskt", "heading1": "Installing in the Clients", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "You should see it loaded in the Search and Tools icon or on the developer settings page. \n \n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/claude-desktop-mcp.gif)\n\n", "heading1": "Installing in the Clients", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "There are currently only two tools in the server: `gradio_docs_mcp_load_gradio_docs` and `gradio_docs_mcp_search_gradio_docs`. \n\n1. `gradio_docs_mcp_load_gradio_docs`: This tool takes no arguments and will load an /llms.txt style summary of Gradio's latest, full documentation. Very useful context the LLM can parse before answering questions or generating code. \n\n2. `gradio_docs_mcp_search_gradio_docs`: This tool takes a query as an argument and will run embedding search on Gradio's docs, guides, and demos to return the most useful context for the LLM to parse.", "heading1": "Tools", "source_page_url": "https://gradio.app/guides/using-docs-mcp", "source_page_title": "Mcp - Using Docs Mcp Guide"}, {"text": "The Model Context Protocol (MCP) standardizes how applications provide context to LLMs. It allows Claude to interact with external tools, like image generators, file systems, or APIs, etc.\n\n", "heading1": "What is MCP?", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "- Python 3.10+\n- An Anthropic API key\n- Basic understanding of Python programming\n\n", "heading1": "Prerequisites", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "First, install the required packages:\n\n```bash\npip install gradio anthropic mcp\n```\n\nCreate a `.env` file in your project directory and add your Anthropic API key:\n\n```\nANTHROPIC_API_KEY=your_api_key_here\n```\n\n", "heading1": "Setup", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "The server provides tools that Claude can use. In this example, we'll create a server that generates images through [a HuggingFace space](https://huggingface.co/spaces/ysharma/SanaSprint).\n\nCreate a file named `gradio_mcp_server.py`:\n\n```python\nfrom mcp.server.fastmcp import FastMCP\nimport json\nimport sys\nimport io\nimport time\nfrom gradio_client import Client\n\nsys.stdout = io.TextIOWrapper(sys.stdout.buffer, encoding='utf-8', errors='replace')\nsys.stderr = io.TextIOWrapper(sys.stderr.buffer, encoding='utf-8', errors='replace')\n\nmcp = FastMCP(\"huggingface_spaces_image_display\")\n\n@mcp.tool()\nasync def generate_image(prompt: str, width: int = 512, height: int = 512) -> str:\n    \"\"\"Generate an image using SanaSprint model.\n    \n    Args:\n        prompt: Text prompt describing the image to generate\n        width: Image width (default: 512)\n        height: Image height (default: 512)\n    \"\"\"\n    client = Client(\"https://ysharma-sanasprint.hf.space/\")\n    \n    try:\n        result = client.predict(\n            prompt,\n            \"0.6B\",\n            0,\n            True,\n            width,\n            height,\n            4.0,\n            2,\n            api_name=\"/infer\"\n        )\n        \n        if isinstance(result, list) and len(result) >= 1:\n            image_data = result[0]\n            if isinstance(image_data, dict) and \"url\" in image_data:\n                return json.dumps({\n                    \"type\": \"image\",\n                    \"url\": image_data[\"url\"],\n                    \"message\": f\"Generated image for prompt: {prompt}\"\n                })\n        \n        return json.dumps({\n            \"type\": \"error\",\n            \"message\": \"Failed to generate image\"\n        })\n        \n    except Exception as e:\n        return json.dumps({\n            \"type\": \"error\",\n            \"message\": f\"Error generating image: {str(e)}\"\n        })\n\nif __name__ == \"__main__\":\n    mcp.run(transport='stdio')\n```\n\nWhat this server does:\n\n1. It creates an MCP server that exposes a `gene", "heading1": "Part 1: Building the MCP Server", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "  \"message\": f\"Error generating image: {str(e)}\"\n        })\n\nif __name__ == \"__main__\":\n    mcp.run(transport='stdio')\n```\n\nWhat this server does:\n\n1. It creates an MCP server that exposes a `generate_image` tool\n2. The tool connects to the SanaSprint model hosted on HuggingFace Spaces\n3. It handles the asynchronous nature of image generation by polling for results\n4. When an image is ready, it returns the URL in a structured JSON format\n\n", "heading1": "Part 1: Building the MCP Server", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now let's create a Gradio chat interface as MCP Client that connects Claude to our MCP server.\n\nCreate a file named `app.py`:\n\n```python\nimport asyncio\nimport os\nimport json\nfrom typing import List, Dict, Any, Union\nfrom contextlib import AsyncExitStack\n\nimport gradio as gr\nfrom gradio.components.chatbot import ChatMessage\nfrom mcp import ClientSession, StdioServerParameters\nfrom mcp.client.stdio import stdio_client\nfrom anthropic import Anthropic\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nloop = asyncio.new_event_loop()\nasyncio.set_event_loop(loop)\n\nclass MCPClientWrapper:\n    def __init__(self):\n        self.session = None\n        self.exit_stack = None\n        self.anthropic = Anthropic()\n        self.tools = []\n    \n    def connect(self, server_path: str) -> str:\n        return loop.run_until_complete(self._connect(server_path))\n    \n    async def _connect(self, server_path: str) -> str:\n        if self.exit_stack:\n            await self.exit_stack.aclose()\n        \n        self.exit_stack = AsyncExitStack()\n        \n        is_python = server_path.endswith('.py')\n        command = \"python\" if is_python else \"node\"\n        \n        server_params = StdioServerParameters(\n            command=command,\n            args=[server_path],\n            env={\"PYTHONIOENCODING\": \"utf-8\", \"PYTHONUNBUFFERED\": \"1\"}\n        )\n        \n        stdio_transport = await self.exit_stack.enter_async_context(stdio_client(server_params))\n        self.stdio, self.write = stdio_transport\n        \n        self.session = await self.exit_stack.enter_async_context(ClientSession(self.stdio, self.write))\n        await self.session.initialize()\n        \n        response = await self.session.list_tools()\n        self.tools = [{ \n            \"name\": tool.name,\n            \"description\": tool.description,\n            \"input_schema\": tool.inputSchema\n        } for tool in response.tools]\n        \n        tool_names = [tool[\"name\"] for tool in self.tools]\n        return f\"Connected to MCP server.", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "iption,\n            \"input_schema\": tool.inputSchema\n        } for tool in response.tools]\n        \n        tool_names = [tool[\"name\"] for tool in self.tools]\n        return f\"Connected to MCP server. Available tools: {', '.join(tool_names)}\"\n    \n    def process_message(self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]) -> tuple:\n        if not self.session:\n            return history + [\n                {\"role\": \"user\", \"content\": message}, \n                {\"role\": \"assistant\", \"content\": \"Please connect to an MCP server first.\"}\n            ], gr.Textbox(value=\"\")\n        \n        new_messages = loop.run_until_complete(self._process_query(message, history))\n        return history + [{\"role\": \"user\", \"content\": message}] + new_messages, gr.Textbox(value=\"\")\n    \n    async def _process_query(self, message: str, history: List[Union[Dict[str, Any], ChatMessage]]):\n        claude_messages = []\n        for msg in history:\n            if isinstance(msg, ChatMessage):\n                role, content = msg.role, msg.content\n            else:\n                role, content = msg.get(\"role\"), msg.get(\"content\")\n            \n            if role in [\"user\", \"assistant\", \"system\"]:\n                claude_messages.append({\"role\": role, \"content\": content})\n        \n        claude_messages.append({\"role\": \"user\", \"content\": message})\n        \n        response = self.anthropic.messages.create(\n            model=\"claude-3-5-sonnet-20241022\",\n            max_tokens=1000,\n            messages=claude_messages,\n            tools=self.tools\n        )\n\n        result_messages = []\n        \n        for content in response.content:\n            if content.type == 'text':\n                result_messages.append({\n                    \"role\": \"assistant\", \n                    \"content\": content.text\n                })\n                \n            elif content.type == 'tool_use':\n                tool_name = content.name\n                tool_args = content.input\n               ", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "ntent\": content.text\n                })\n                \n            elif content.type == 'tool_use':\n                tool_name = content.name\n                tool_args = content.input\n                \n                result_messages.append({\n                    \"role\": \"assistant\",\n                    \"content\": f\"I'll use the {tool_name} tool to help answer your question.\",\n                    \"metadata\": {\n                        \"title\": f\"Using tool: {tool_name}\",\n                        \"log\": f\"Parameters: {json.dumps(tool_args, ensure_ascii=True)}\",\n                        \"status\": \"pending\",\n                        \"id\": f\"tool_call_{tool_name}\"\n                    }\n                })\n                \n                result_messages.append({\n                    \"role\": \"assistant\",\n                    \"content\": \"```json\\n\" + json.dumps(tool_args, indent=2, ensure_ascii=True) + \"\\n```\",\n                    \"metadata\": {\n                        \"parent_id\": f\"tool_call_{tool_name}\",\n                        \"id\": f\"params_{tool_name}\",\n                        \"title\": \"Tool Parameters\"\n                    }\n                })\n                \n                result = await self.session.call_tool(tool_name, tool_args)\n                \n                if result_messages and \"metadata\" in result_messages[-2]:\n                    result_messages[-2][\"metadata\"][\"status\"] = \"done\"\n                \n                result_messages.append({\n                    \"role\": \"assistant\",\n                    \"content\": \"Here are the results from the tool:\",\n                    \"metadata\": {\n                        \"title\": f\"Tool Result for {tool_name}\",\n                        \"status\": \"done\",\n                        \"id\": f\"result_{tool_name}\"\n                    }\n                })\n                \n                result_content = result.content\n                if isinstance(result_content, list):\n                    result_content = \"\\n\".join(str(item) for item in re", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "        })\n                \n                result_content = result.content\n                if isinstance(result_content, list):\n                    result_content = \"\\n\".join(str(item) for item in result_content)\n                \n                try:\n                    result_json = json.loads(result_content)\n                    if isinstance(result_json, dict) and \"type\" in result_json:\n                        if result_json[\"type\"] == \"image\" and \"url\" in result_json:\n                            result_messages.append({\n                                \"role\": \"assistant\",\n                                \"content\": {\"path\": result_json[\"url\"], \"alt_text\": result_json.get(\"message\", \"Generated image\")},\n                                \"metadata\": {\n                                    \"parent_id\": f\"result_{tool_name}\",\n                                    \"id\": f\"image_{tool_name}\",\n                                    \"title\": \"Generated Image\"\n                                }\n                            })\n                        else:\n                            result_messages.append({\n                                \"role\": \"assistant\",\n                                \"content\": \"```\\n\" + result_content + \"\\n```\",\n                                \"metadata\": {\n                                    \"parent_id\": f\"result_{tool_name}\",\n                                    \"id\": f\"raw_result_{tool_name}\",\n                                    \"title\": \"Raw Output\"\n                                }\n                            })\n                except:\n                    result_messages.append({\n                        \"role\": \"assistant\",\n                        \"content\": \"```\\n\" + result_content + \"\\n```\",\n                        \"metadata\": {\n                            \"parent_id\": f\"result_{tool_name}\",\n                            \"id\": f\"raw_result_{tool_name}\",\n                            \"title\": \"Raw Output\"\n                        }\n                    })\n   ", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": " \"parent_id\": f\"result_{tool_name}\",\n                            \"id\": f\"raw_result_{tool_name}\",\n                            \"title\": \"Raw Output\"\n                        }\n                    })\n                \n                claude_messages.append({\"role\": \"user\", \"content\": f\"Tool result for {tool_name}: {result_content}\"})\n                next_response = self.anthropic.messages.create(\n                    model=\"claude-3-5-sonnet-20241022\",\n                    max_tokens=1000,\n                    messages=claude_messages,\n                )\n                \n                if next_response.content and next_response.content[0].type == 'text':\n                    result_messages.append({\n                        \"role\": \"assistant\",\n                        \"content\": next_response.content[0].text\n                    })\n\n        return result_messages\n\nclient = MCPClientWrapper()\n\ndef gradio_interface():\n    with gr.Blocks(title=\"MCP Weather Client\") as demo:\n        gr.Markdown(\"MCP Weather Assistant\")\n        gr.Markdown(\"Connect to your MCP weather server and chat with the assistant\")\n        \n        with gr.Row(equal_height=True):\n            with gr.Column(scale=4):\n                server_path = gr.Textbox(\n                    label=\"Server Script Path\",\n                    placeholder=\"Enter path to server script (e.g., weather.py)\",\n                    value=\"gradio_mcp_server.py\"\n                )\n            with gr.Column(scale=1):\n                connect_btn = gr.Button(\"Connect\")\n        \n        status = gr.Textbox(label=\"Connection Status\", interactive=False)\n        \n        chatbot = gr.Chatbot(\n            value=[], \n            height=500,\n            type=\"messages\",\n            show_copy_button=True,\n            avatar_images=(\"\ud83d\udc64\", \"\ud83e\udd16\")\n        )\n        \n        with gr.Row(equal_height=True):\n            msg = gr.Textbox(\n                label=\"Your Question\",\n                placeholder=\"Ask about weather or alerts (e.g., What's the weath", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": ")\n        \n        with gr.Row(equal_height=True):\n            msg = gr.Textbox(\n                label=\"Your Question\",\n                placeholder=\"Ask about weather or alerts (e.g., What's the weather in New York?)\",\n                scale=4\n            )\n            clear_btn = gr.Button(\"Clear Chat\", scale=1)\n        \n        connect_btn.click(client.connect, inputs=server_path, outputs=status)\n        msg.submit(client.process_message, [msg, chatbot], [chatbot, msg])\n        clear_btn.click(lambda: [], None, chatbot)\n        \n    return demo\n\nif __name__ == \"__main__\":\n    if not os.getenv(\"ANTHROPIC_API_KEY\"):\n        print(\"Warning: ANTHROPIC_API_KEY not found in environment. Please set it in your .env file.\")\n    \n    interface = gradio_interface()\n    interface.launch(debug=True)\n```\n\nWhat this MCP Client does:\n\n- Creates a friendly Gradio chat interface for user interaction\n- Connects to the MCP server you specify\n- Handles conversation history and message formatting\n- Makes call to Claude API with tool definitions\n- Processes tool usage requests from Claude\n- Displays images and other tool outputs in the chat\n- Sends tool results back to Claude for interpretation\n\n", "heading1": "Part 2: Building the MCP Client with Gradio", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "To run your MCP application:\n\n- Start a terminal window and run the MCP Client:\n   ```bash\n   python app.py\n   ```\n- Open the Gradio interface at the URL shown (typically http://127.0.0.1:7860)\n- In the Gradio interface, you'll see a field for the MCP Server path. It should default to `gradio_mcp_server.py`.\n- Click \"Connect\" to establish the connection to the MCP server.\n- You should see a message indicating the server connection was successful.\n\n", "heading1": "Running the Application", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now you can chat with Claude and it will be able to generate images based on your descriptions.\n\nTry prompts like:\n- \"Can you generate an image of a mountain landscape at sunset?\"\n- \"Create an image of a cool tabby cat\"\n- \"Generate a picture of a panda wearing sunglasses\"\n\nClaude will recognize these as image generation requests and automatically use the `generate_image` tool from your MCP server.\n\n\n", "heading1": "Example Usage", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Here's the high-level flow of what happens during a chat session:\n\n1. Your prompt enters the Gradio interface\n2. The client forwards your prompt to Claude\n3. Claude analyzes the prompt and decides to use the `generate_image` tool\n4. The client sends the tool call to the MCP server\n5. The server calls the external image generation API\n6. The image URL is returned to the client\n7. The client sends the image URL back to Claude\n8. Claude provides a response that references the generated image\n9. The Gradio chat interface displays both Claude's response and the image\n\n\n", "heading1": "How it Works", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Now that you have a working MCP system, here are some ideas to extend it:\n\n- Add more tools to your server\n- Improve error handling \n- Add private Huggingface Spaces with authentication for secure tool access\n- Create custom tools that connect to your own APIs or services\n- Implement streaming responses for better user experience\n\n", "heading1": "Next Steps", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "Congratulations! You've successfully built an MCP Client and Server that allows Claude to generate images based on text prompts. This is just the beginning of what you can do with Gradio and MCP. This guide enables you to build complex AI applications that can use Claude or any other powerful LLM to interact with virtually any external tool or service.\n\nRead our other Guide on using [Gradio apps as MCP Servers](./building-mcp-server-with-gradio).\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/building-an-mcp-client-with-gradio", "source_page_title": "Mcp - Building An Mcp Client With Gradio Guide"}, {"text": "This guide explains how you can run background tasks from your gradio app.\nBackground tasks are operations that you'd like to perform outside the request-response\nlifecycle of your app either once or on a periodic schedule.\nExamples of background tasks include periodically synchronizing data to an external database or\nsending a report of model predictions via email.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "We will be creating a simple \"Google-forms-style\" application to gather feedback from users of the gradio library.\nWe will use a local sqlite database to store our data, but we will periodically synchronize the state of the database\nwith a [HuggingFace Dataset](https://huggingface.co/datasets) so that our user reviews are always backed up.\nThe synchronization will happen in a background task running every 60 seconds.\n\nAt the end of the demo, you'll have a fully working application like this one:\n\n<gradio-app space=\"freddyaboulton/gradio-google-forms\"> </gradio-app>\n\n", "heading1": "Overview", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "Our application will store the name of the reviewer, their rating of gradio on a scale of 1 to 5, as well as\nany comments they want to share about the library. Let's write some code that creates a database table to\nstore this data. We'll also write some functions to insert a review into that table and fetch the latest 10 reviews.\n\nWe're going to use the `sqlite3` library to connect to our sqlite database but gradio will work with any library.\n\nThe code will look like this:\n\n```python\nDB_FILE = \"./reviews.db\"\ndb = sqlite3.connect(DB_FILE)\n\nCreate table if it doesn't already exist\ntry:\n    db.execute(\"SELECT * FROM reviews\").fetchall()\n    db.close()\nexcept sqlite3.OperationalError:\n    db.execute(\n        '''\n        CREATE TABLE reviews (id INTEGER PRIMARY KEY AUTOINCREMENT NOT NULL,\n                              created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP NOT NULL,\n                              name TEXT, review INTEGER, comments TEXT)\n        ''')\n    db.commit()\n    db.close()\n\ndef get_latest_reviews(db: sqlite3.Connection):\n    reviews = db.execute(\"SELECT * FROM reviews ORDER BY id DESC limit 10\").fetchall()\n    total_reviews = db.execute(\"Select COUNT(id) from reviews\").fetchone()[0]\n    reviews = pd.DataFrame(reviews, columns=[\"id\", \"date_created\", \"name\", \"review\", \"comments\"])\n    return reviews, total_reviews\n\n\ndef add_review(name: str, review: int, comments: str):\n    db = sqlite3.connect(DB_FILE)\n    cursor = db.cursor()\n    cursor.execute(\"INSERT INTO reviews(name, review, comments) VALUES(?,?,?)\", [name, review, comments])\n    db.commit()\n    reviews, total_reviews = get_latest_reviews(db)\n    db.close()\n    return reviews, total_reviews\n```\n\nLet's also write a function to load the latest reviews when the gradio application loads:\n\n```python\ndef load_data():\n    db = sqlite3.connect(DB_FILE)\n    reviews, total_reviews = get_latest_reviews(db)\n    db.close()\n    return reviews, total_reviews\n```\n\n", "heading1": "Step 1 - Write your database logic \ud83d\udcbe", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "Now that we have our database logic defined, we can use gradio create a dynamic web page to ask our users for feedback!\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Row():\n        with gr.Column():\n            name = gr.Textbox(label=\"Name\", placeholder=\"What is your name?\")\n            review = gr.Radio(label=\"How satisfied are you with using gradio?\", choices=[1, 2, 3, 4, 5])\n            comments = gr.Textbox(label=\"Comments\", lines=10, placeholder=\"Do you have any feedback on gradio?\")\n            submit = gr.Button(value=\"Submit Feedback\")\n        with gr.Column():\n            data = gr.Dataframe(label=\"Most recently created 10 rows\")\n            count = gr.Number(label=\"Total number of reviews\")\n    submit.click(add_review, [name, review, comments], [data, count])\n    demo.load(load_data, None, [data, count])\n```\n\n", "heading1": "Step 2 - Create a gradio app \u26a1", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "We could call `demo.launch()` after step 2 and have a fully functioning application. However,\nour data would be stored locally on our machine. If the sqlite file were accidentally deleted, we'd lose all of our reviews!\nLet's back up our data to a dataset on the HuggingFace hub.\n\nCreate a dataset [here](https://huggingface.co/datasets) before proceeding.\n\nNow at the **top** of our script, we'll use the [huggingface hub client library](https://huggingface.co/docs/huggingface_hub/index)\nto connect to our dataset and pull the latest backup.\n\n```python\nTOKEN = os.environ.get('HUB_TOKEN')\nrepo = huggingface_hub.Repository(\n    local_dir=\"data\",\n    repo_type=\"dataset\",\n    clone_from=\"<name-of-your-dataset>\",\n    use_auth_token=TOKEN\n)\nrepo.git_pull()\n\nshutil.copyfile(\"./data/reviews.db\", DB_FILE)\n```\n\nNote that you'll have to get an access token from the \"Settings\" tab of your HuggingFace for the above code to work.\nIn the script, the token is securely accessed via an environment variable.\n\n![access_token](https://github.com/gradio-app/gradio/blob/main/guides/assets/access_token.png?raw=true)\n\nNow we will create a background task to synch our local database to the dataset hub every 60 seconds.\nWe will use the [AdvancedPythonScheduler](https://apscheduler.readthedocs.io/en/3.x/) to handle the scheduling.\nHowever, this is not the only task scheduling library available. Feel free to use whatever you are comfortable with.\n\nThe function to back up our data will look like this:\n\n```python\nfrom apscheduler.schedulers.background import BackgroundScheduler\n\ndef backup_db():\n    shutil.copyfile(DB_FILE, \"./data/reviews.db\")\n    db = sqlite3.connect(DB_FILE)\n    reviews = db.execute(\"SELECT * FROM reviews\").fetchall()\n    pd.DataFrame(reviews).to_csv(\"./data/reviews.csv\", index=False)\n    print(\"updating db\")\n    repo.push_to_hub(blocking=False, commit_message=f\"Updating data at {datetime.datetime.now()}\")\n\n\nscheduler = BackgroundScheduler()\nscheduler.add_job(func=backup_db, trigge", "heading1": "Step 3 - Synchronize with HuggingFace Datasets \ud83e\udd17", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "    print(\"updating db\")\n    repo.push_to_hub(blocking=False, commit_message=f\"Updating data at {datetime.datetime.now()}\")\n\n\nscheduler = BackgroundScheduler()\nscheduler.add_job(func=backup_db, trigger=\"interval\", seconds=60)\nscheduler.start()\n```\n\n", "heading1": "Step 3 - Synchronize with HuggingFace Datasets \ud83e\udd17", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "You can use the HuggingFace [Spaces](https://huggingface.co/spaces) platform to deploy this application for free \u2728\n\nIf you haven't used Spaces before, follow the previous guide [here](/using_hugging_face_integrations).\nYou will have to use the `HUB_TOKEN` environment variable as a secret in the Guides.\n\n", "heading1": "Step 4 (Bonus) - Deployment to HuggingFace Spaces", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "Congratulations! You know how to run background tasks from your gradio app on a schedule \u23f2\ufe0f.\n\nCheckout the application running on Spaces [here](https://huggingface.co/spaces/freddyaboulton/gradio-google-forms).\nThe complete code is [here](https://huggingface.co/spaces/freddyaboulton/gradio-google-forms/blob/main/app.py)\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/running-background-tasks", "source_page_title": "Other Tutorials - Running Background Tasks Guide"}, {"text": "It seems that cryptocurrencies, [NFTs](https://www.nytimes.com/interactive/2022/03/18/technology/nft-guide.html), and the web3 movement are all the rage these days! Digital assets are being listed on marketplaces for astounding amounts of money, and just about every celebrity is debuting their own NFT collection. While your crypto assets [may be taxable, such as in Canada](https://www.canada.ca/en/revenue-agency/programs/about-canada-revenue-agency-cra/compliance/digital-currency/cryptocurrency-guide.html), today we'll explore some fun and tax-free ways to generate your own assortment of procedurally generated [CryptoPunks](https://www.larvalabs.com/cryptopunks).\n\nGenerative Adversarial Networks, often known just as _GANs_, are a specific class of deep-learning models that are designed to learn from an input dataset to create (_generate!_) new material that is convincingly similar to elements of the original training set. Famously, the website [thispersondoesnotexist.com](https://thispersondoesnotexist.com/) went viral with lifelike, yet synthetic, images of people generated with a model called StyleGAN2. GANs have gained traction in the machine learning world, and are now being used to generate all sorts of images, text, and even [music](https://salu133445.github.io/musegan/)!\n\nToday we'll briefly look at the high-level intuition behind GANs, and then we'll build a small demo around a pre-trained GAN to see what all the fuss is about. Here's a [peek](https://nimaboscarino-cryptopunks.hf.space) at what we're going to be putting together.\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). To use the pretrained model, also install `torch` and `torchvision`.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Originally proposed in [Goodfellow et al. 2014](https://arxiv.org/abs/1406.2661), GANs are made up of neural networks which compete with the intention of outsmarting each other. One network, known as the _generator_, is responsible for generating images. The other network, the _discriminator_, receives an image at a time from the generator along with a **real** image from the training data set. The discriminator then has to guess: which image is the fake?\n\nThe generator is constantly training to create images which are trickier for the discriminator to identify, while the discriminator raises the bar for the generator every time it correctly detects a fake. As the networks engage in this competitive (_adversarial!_) relationship, the images that get generated improve to the point where they become indistinguishable to human eyes!\n\nFor a more in-depth look at GANs, you can take a look at [this excellent post on Analytics Vidhya](https://www.analyticsvidhya.com/blog/2021/06/a-detailed-explanation-of-gan-with-implementation-using-tensorflow-and-keras/) or this [PyTorch tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html). For now, though, we'll dive into a demo!\n\n", "heading1": "GANs: a very brief introduction", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "To generate new images with a GAN, you only need the generator model. There are many different architectures that the generator could use, but for this demo we'll use a pretrained GAN generator model with the following architecture:\n\n```python\nfrom torch import nn\n\nclass Generator(nn.Module):\n    Refer to the link below for explanations about nc, nz, and ngf\n    https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.htmlinputs\n    def __init__(self, nc=4, nz=100, ngf=64):\n        super(Generator, self).__init__()\n        self.network = nn.Sequential(\n            nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),\n            nn.BatchNorm2d(ngf * 4),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),\n            nn.BatchNorm2d(ngf * 2),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),\n            nn.BatchNorm2d(ngf),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n            nn.Tanh(),\n        )\n\n    def forward(self, input):\n        output = self.network(input)\n        return output\n```\n\nWe're taking the generator from [this repo by @teddykoker](https://github.com/teddykoker/cryptopunks-gan/blob/main/train.pyL90), where you can also see the original discriminator model structure.\n\nAfter instantiating the model, we'll load in the weights from the Hugging Face Hub, stored at [nateraw/cryptopunks-gan](https://huggingface.co/nateraw/cryptopunks-gan):\n\n```python\nfrom huggingface_hub import hf_hub_download\nimport torch\n\nmodel = Generator()\nweights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')\nmodel.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) Use 'cuda' if you have a GPU available\n```\n\n", "heading1": "Step 1 \u2014 Create the Generator model", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "The `predict` function is the key to making Gradio work! Whatever inputs we choose through the Gradio interface will get passed through our `predict` function, which should operate on the inputs and generate outputs that we can display with Gradio output components. For GANs it's common to pass random noise into our model as the input, so we'll generate a tensor of random numbers and pass that through the model. We can then use `torchvision`'s `save_image` function to save the output of the model as a `png` file, and return the file name:\n\n```python\nfrom torchvision.utils import save_image\n\ndef predict(seed):\n    num_punks = 4\n    torch.manual_seed(seed)\n    z = torch.randn(num_punks, 100, 1, 1)\n    punks = model(z)\n    save_image(punks, \"punks.png\", normalize=True)\n    return 'punks.png'\n```\n\nWe're giving our `predict` function a `seed` parameter, so that we can fix the random tensor generation with a seed. We'll then be able to reproduce punks if we want to see them again by passing in the same seed.\n\n_Note!_ Our model needs an input tensor of dimensions 100x1x1 to do a single inference, or (BatchSize)x100x1x1 for generating a batch of images. In this demo we'll start by generating 4 punks at a time.\n\n", "heading1": "Step 2 \u2014 Defining a `predict` function", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "At this point you can even run the code you have with `predict(<SOME_NUMBER>)`, and you'll find your freshly generated punks in your file system at `./punks.png`. To make a truly interactive demo, though, we'll build out a simple interface with Gradio. Our goals here are to:\n\n- Set a slider input so users can choose the \"seed\" value\n- Use an image component for our output to showcase the generated punks\n- Use our `predict()` to take the seed and generate the images\n\nWith `gr.Interface()`, we can define all of that with a single function call:\n\n```python\nimport gradio as gr\n\ngr.Interface(\n    predict,\n    inputs=[\n        gr.Slider(0, 1000, label='Seed', default=42),\n    ],\n    outputs=\"image\",\n).launch()\n```\n\n\n", "heading1": "Step 3 \u2014 Creating a Gradio interface", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Generating 4 punks at a time is a good start, but maybe we'd like to control how many we want to make each time. Adding more inputs to our Gradio interface is as simple as adding another item to the `inputs` list that we pass to `gr.Interface`:\n\n```python\ngr.Interface(\n    predict,\n    inputs=[\n        gr.Slider(0, 1000, label='Seed', default=42),\n        gr.Slider(4, 64, label='Number of Punks', step=1, default=10), Adding another slider!\n    ],\n    outputs=\"image\",\n).launch()\n```\n\nThe new input will be passed to our `predict()` function, so we have to make some changes to that function to accept a new parameter:\n\n```python\ndef predict(seed, num_punks):\n    torch.manual_seed(seed)\n    z = torch.randn(num_punks, 100, 1, 1)\n    punks = model(z)\n    save_image(punks, \"punks.png\", normalize=True)\n    return 'punks.png'\n```\n\nWhen you relaunch your interface, you should see a second slider that'll let you control the number of punks!\n\n", "heading1": "Step 4 \u2014 Even more punks!", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Your Gradio app is pretty much good to go, but you can add a few extra things to really make it ready for the spotlight \u2728\n\nWe can add some examples that users can easily try out by adding this to the `gr.Interface`:\n\n```python\ngr.Interface(\n    ...\n    keep everything as it is, and then add\n    examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],\n).launch(cache_examples=True) cache_examples is optional\n```\n\nThe `examples` parameter takes a list of lists, where each item in the sublists is ordered in the same order that we've listed the `inputs`. So in our case, `[seed, num_punks]`. Give it a try!\n\nYou can also try adding a `title`, `description`, and `article` to the `gr.Interface`. Each of those parameters accepts a string, so try it out and see what happens \ud83d\udc40 `article` will also accept HTML, as [explored in a previous guide](/guides/key-features/descriptive-content)!\n\nWhen you're all done, you may end up with something like [this](https://nimaboscarino-cryptopunks.hf.space).\n\nFor reference, here is our full code:\n\n```python\nimport torch\nfrom torch import nn\nfrom huggingface_hub import hf_hub_download\nfrom torchvision.utils import save_image\nimport gradio as gr\n\nclass Generator(nn.Module):\n    Refer to the link below for explanations about nc, nz, and ngf\n    https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.htmlinputs\n    def __init__(self, nc=4, nz=100, ngf=64):\n        super(Generator, self).__init__()\n        self.network = nn.Sequential(\n            nn.ConvTranspose2d(nz, ngf * 4, 3, 1, 0, bias=False),\n            nn.BatchNorm2d(ngf * 4),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 4, ngf * 2, 3, 2, 1, bias=False),\n            nn.BatchNorm2d(ngf * 2),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 0, bias=False),\n            nn.BatchNorm2d(ngf),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n            nn.Tanh(),\n        )\n\n    def forward(self,", "heading1": "Step 5 - Polishing it up", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": " 4, 2, 0, bias=False),\n            nn.BatchNorm2d(ngf),\n            nn.ReLU(True),\n            nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),\n            nn.Tanh(),\n        )\n\n    def forward(self, input):\n        output = self.network(input)\n        return output\n\nmodel = Generator()\nweights_path = hf_hub_download('nateraw/cryptopunks-gan', 'generator.pth')\nmodel.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))) Use 'cuda' if you have a GPU available\n\ndef predict(seed, num_punks):\n    torch.manual_seed(seed)\n    z = torch.randn(num_punks, 100, 1, 1)\n    punks = model(z)\n    save_image(punks, \"punks.png\", normalize=True)\n    return 'punks.png'\n\ngr.Interface(\n    predict,\n    inputs=[\n        gr.Slider(0, 1000, label='Seed', default=42),\n        gr.Slider(4, 64, label='Number of Punks', step=1, default=10),\n    ],\n    outputs=\"image\",\n    examples=[[123, 15], [42, 29], [456, 8], [1337, 35]],\n).launch(cache_examples=True)\n```\n\n---\n\nCongratulations! You've built out your very own GAN-powered CryptoPunks generator, with a fancy Gradio interface that makes it easy for anyone to use. Now you can [scour the Hub for more GANs](https://huggingface.co/models?other=gan) (or train your own) and continue making even more awesome demos \ud83e\udd17\n", "heading1": "Step 5 - Polishing it up", "source_page_url": "https://gradio.app/guides/create-your-own-friends-with-a-gan", "source_page_title": "Other Tutorials - Create Your Own Friends With A Gan Guide"}, {"text": "Image classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from autonomous vehicles to medical imaging.\n\nSuch models are perfect to use with Gradio's _image_ input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in Python, and it will look like the demo on the bottom of the page.\n\nLet's get started!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). We will be using a pretrained image classification model, so you should also have `torch` installed.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "First, we will need an image classification model. For this tutorial, we will use a pretrained Resnet-18 model, as it is easily downloadable from [PyTorch Hub](https://pytorch.org/hub/pytorch_vision_resnet/). You can use a different pretrained model or train your own.\n\n```python\nimport torch\n\nmodel = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()\n```\n\nBecause we will be using the model for inference, we have called the `.eval()` method.\n\n", "heading1": "Step 1 \u2014 Setting up the Image Classification Model", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "Next, we will need to define a function that takes in the _user input_, which in this case is an image, and returns the prediction. The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities. We will load the class names from this [text file](https://git.io/JJkYN).\n\nIn the case of our pretrained model, it will look like this:\n\n```python\nimport requests\nfrom PIL import Image\nfrom torchvision import transforms\n\nDownload human-readable labels for ImageNet.\nresponse = requests.get(\"https://git.io/JJkYN\")\nlabels = response.text.split(\"\\n\")\n\ndef predict(inp):\n  inp = transforms.ToTensor()(inp).unsqueeze(0)\n  with torch.no_grad():\n    prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)\n    confidences = {labels[i]: float(prediction[i]) for i in range(1000)}\n  return confidences\n```\n\nLet's break this down. The function takes one parameter:\n\n- `inp`: the input image as a `PIL` image\n\nThen, the function converts the image to a PIL Image and then eventually a PyTorch `tensor`, passes it through the model, and returns:\n\n- `confidences`: the predictions, as a dictionary whose keys are class labels and whose values are confidence probabilities\n\n", "heading1": "Step 2 \u2014 Defining a `predict` function", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "Now that we have our predictive function set up, we can create a Gradio Interface around it.\n\nIn this case, the input component is a drag-and-drop image component. To create this input, we use `Image(type=\"pil\")` which creates the component and handles the preprocessing to convert that to a `PIL` image.\n\nThe output component will be a `Label`, which displays the top labels in a nice form. Since we don't want to show all 1,000 class labels, we will customize it to show only the top 3 images by constructing it as `Label(num_top_classes=3)`.\n\nFinally, we'll add one more parameter, the `examples`, which allows us to prepopulate our interfaces with a few predefined examples. The code for Gradio looks like this:\n\n```python\nimport gradio as gr\n\ngr.Interface(fn=predict,\n             inputs=gr.Image(type=\"pil\"),\n             outputs=gr.Label(num_top_classes=3),\n             examples=[\"lion.jpg\", \"cheetah.jpg\"]).launch()\n```\n\nThis produces the following interface, which you can try right here in your browser (try uploading your own examples!):\n\n<gradio-app space=\"gradio/pytorch-image-classifier\">\n\n\n---\n\nAnd you're done! That's all the code you need to build a web demo for an image classifier. If you'd like to share with others, try setting `share=True` when you `launch()` the Interface!\n", "heading1": "Step 3 \u2014 Creating a Gradio Interface", "source_page_url": "https://gradio.app/guides/image-classification-in-pytorch", "source_page_title": "Other Tutorials - Image Classification In Pytorch Guide"}, {"text": "This guide explains how you can use Gradio to plot geographical data on a map using the `gradio.Plot` component. The Gradio `Plot` component works with Matplotlib, Bokeh and Plotly. Plotly is what we will be working with in this guide. Plotly allows developers to easily create all sorts of maps with their geographical data. Take a look [here](https://plotly.com/python/maps/) for some examples.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "We will be using the New York City Airbnb dataset, which is hosted on kaggle [here](https://www.kaggle.com/datasets/dgomonov/new-york-city-airbnb-open-data). I've uploaded it to the Hugging Face Hub as a dataset [here](https://huggingface.co/datasets/gradio/NYC-Airbnb-Open-Data) for easier use and download. Using this data we will plot Airbnb locations on a map output and allow filtering based on price and location. Below is the demo that we will be building. \u26a1\ufe0f\n\n$demo_map_airbnb\n\n", "heading1": "Overview", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Let's start by loading the Airbnb NYC data from the Hugging Face Hub.\n\n```python\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"gradio/NYC-Airbnb-Open-Data\", split=\"train\")\ndf = dataset.to_pandas()\n\ndef filter_map(min_price, max_price, boroughs):\n    new_df = df[(df['neighbourhood_group'].isin(boroughs)) &\n            (df['price'] > min_price) & (df['price'] < max_price)]\n    names = new_df[\"name\"].tolist()\n    prices = new_df[\"price\"].tolist()\n    text_list = [(names[i], prices[i]) for i in range(0, len(names))]\n```\n\nIn the code above, we first load the csv data into a pandas dataframe. Let's begin by defining a function that we will use as the prediction function for the gradio app. This function will accept the minimum price and maximum price range as well as the list of boroughs to filter the resulting map. We can use the passed in values (`min_price`, `max_price`, and list of `boroughs`) to filter the dataframe and create `new_df`. Next we will create `text_list` of the names and prices of each Airbnb to use as labels on the map.\n\n", "heading1": "Step 1 - Loading CSV data \ud83d\udcbe", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Plotly makes it easy to work with maps. Let's take a look below how we can create a map figure.\n\n```python\nimport plotly.graph_objects as go\n\nfig = go.Figure(go.Scattermapbox(\n            customdata=text_list,\n            lat=new_df['latitude'].tolist(),\n            lon=new_df['longitude'].tolist(),\n            mode='markers',\n            marker=go.scattermapbox.Marker(\n                size=6\n            ),\n            hoverinfo=\"text\",\n            hovertemplate='<b>Name</b>: %{customdata[0]}<br><b>Price</b>: $%{customdata[1]}'\n        ))\n\nfig.update_layout(\n    mapbox_style=\"open-street-map\",\n    hovermode='closest',\n    mapbox=dict(\n        bearing=0,\n        center=go.layout.mapbox.Center(\n            lat=40.67,\n            lon=-73.90\n        ),\n        pitch=0,\n        zoom=9\n    ),\n)\n```\n\nAbove, we create a scatter plot on mapbox by passing it our list of latitudes and longitudes to plot markers. We also pass in our custom data of names and prices for additional info to appear on every marker we hover over. Next we use `update_layout` to specify other map settings such as zoom, and centering.\n\nMore info [here](https://plotly.com/python/scattermapbox/) on scatter plots using Mapbox and Plotly.\n\n", "heading1": "Step 2 - Map Figure \ud83c\udf10", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "We will use two `gr.Number` components and a `gr.CheckboxGroup` to allow users of our app to specify price ranges and borough locations. We will then use the `gr.Plot` component as an output for our Plotly + Mapbox map we created earlier.\n\n```python\nwith gr.Blocks() as demo:\n    with gr.Column():\n        with gr.Row():\n            min_price = gr.Number(value=250, label=\"Minimum Price\")\n            max_price = gr.Number(value=1000, label=\"Maximum Price\")\n        boroughs = gr.CheckboxGroup(choices=[\"Queens\", \"Brooklyn\", \"Manhattan\", \"Bronx\", \"Staten Island\"], value=[\"Queens\", \"Brooklyn\"], label=\"Select Boroughs:\")\n        btn = gr.Button(value=\"Update Filter\")\n        map = gr.Plot()\n    demo.load(filter_map, [min_price, max_price, boroughs], map)\n    btn.click(filter_map, [min_price, max_price, boroughs], map)\n```\n\nWe layout these components using the `gr.Column` and `gr.Row` and we'll also add event triggers for when the demo first loads and when our \"Update Filter\" button is clicked in order to trigger the map to update with our new filters.\n\nThis is what the full demo code looks like:\n\n$code_map_airbnb\n\n", "heading1": "Step 3 - Gradio App \u26a1\ufe0f", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "If you run the code above, your app will start running locally.\nYou can even get a temporary shareable link by passing the `share=True` parameter to `launch`.\n\nBut what if you want to a permanent deployment solution?\nLet's deploy our Gradio app to the free HuggingFace Spaces platform.\n\nIf you haven't used Spaces before, follow the previous guide [here](/using_hugging_face_integrations).\n\n", "heading1": "Step 4 - Deployment \ud83e\udd17", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "And you're all done! That's all the code you need to build a map demo.\n\nHere's a link to the demo [Map demo](https://huggingface.co/spaces/gradio/map_airbnb) and [complete code](https://huggingface.co/spaces/gradio/map_airbnb/blob/main/run.py) (on Hugging Face Spaces)\n", "heading1": "Conclusion \ud83c\udf89", "source_page_url": "https://gradio.app/guides/plot-component-for-maps", "source_page_title": "Other Tutorials - Plot Component For Maps Guide"}, {"text": "Named-entity recognition (NER), also known as token classification or text tagging, is the task of taking a sentence and classifying every word (or \"token\") into different categories, such as names of people or names of locations, or different parts of speech.\n\nFor example, given the sentence:\n\n> Does Chicago have any Pakistani restaurants?\n\nA named-entity recognition algorithm may identify:\n\n- \"Chicago\" as a **location**\n- \"Pakistani\" as an **ethnicity**\n\nand so on.\n\nUsing `gradio` (specifically the `HighlightedText` component), you can easily build a web demo of your NER model and share that with the rest of your team.\n\nHere is an example of a demo that you'll be able to build:\n\n$demo_ner_pipeline\n\nThis tutorial will show how to take a pretrained NER model and deploy it with a Gradio interface. We will show two different ways to use the `HighlightedText` component -- depending on your NER model, either of these two ways may be easier to learn!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). You will also need a pretrained named-entity recognition model. You can use your own, while in this tutorial, we will use one from the `transformers` library.\n\nApproach 1: List of Entity Dictionaries\n\nMany named-entity recognition models output a list of dictionaries. Each dictionary consists of an _entity_, a \"start\" index, and an \"end\" index. This is, for example, how NER models in the `transformers` library operate:\n\n```py\nfrom transformers import pipeline\nner_pipeline = pipeline(\"ner\")\nner_pipeline(\"Does Chicago have any Pakistani restaurants\")\n```\n\nOutput:\n\n```bash\n[{'entity': 'I-LOC',\n  'score': 0.9988978,\n  'index': 2,\n  'word': 'Chicago',\n  'start': 5,\n  'end': 12},\n {'entity': 'I-MISC',\n  'score': 0.9958592,\n  'index': 5,\n  'word': 'Pakistani',\n  'start': 22,\n  'end': 31}]\n```\n\nIf you have such a model, it is very easy to hook it up to Gradio's `HighlightedText` component. All you need to do is pass in this ", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/named-entity-recognition", "source_page_title": "Other Tutorials - Named Entity Recognition Guide"}, {"text": "index': 5,\n  'word': 'Pakistani',\n  'start': 22,\n  'end': 31}]\n```\n\nIf you have such a model, it is very easy to hook it up to Gradio's `HighlightedText` component. All you need to do is pass in this **list of entities**, along with the **original text** to the model, together as dictionary, with the keys being `\"entities\"` and `\"text\"` respectively.\n\nHere is a complete example:\n\n$code_ner_pipeline\n$demo_ner_pipeline\n\nApproach 2: List of Tuples\n\nAn alternative way to pass data into the `HighlightedText` component is a list of tuples. The first element of each tuple should be the word or words that are being classified into a particular entity. The second element should be the entity label (or `None` if they should be unlabeled). The `HighlightedText` component automatically strings together the words and labels to display the entities.\n\nIn some cases, this can be easier than the first approach. Here is a demo showing this approach using Spacy's parts-of-speech tagger:\n\n$code_text_analysis\n$demo_text_analysis\n\n---\n\nAnd you're done! That's all you need to know to build a web-based GUI for your NER model.\n\nFun tip: you can share your NER demo instantly with others simply by setting `share=True` in `launch()`.\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/named-entity-recognition", "source_page_title": "Other Tutorials - Named Entity Recognition Guide"}, {"text": "The Hugging Face Hub is a central platform that has hundreds of thousands of [models](https://huggingface.co/models), [datasets](https://huggingface.co/datasets) and [demos](https://huggingface.co/spaces) (also known as Spaces). \n\nGradio has multiple features that make it extremely easy to leverage existing models and Spaces on the Hub. This guide walks through these features.\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Hugging Face has a service called [Serverless Inference Endpoints](https://huggingface.co/docs/api-inference/index), which allows you to send HTTP requests to models on the Hub. The API includes a generous free tier, and you can switch to [dedicated Inference Endpoints](https://huggingface.co/inference-endpoints/dedicated) when you want to use it in production. Gradio integrates directly with Serverless Inference Endpoints so that you can create a demo simply by specifying a model's name (e.g. `Helsinki-NLP/opus-mt-en-es`), like this:\n\n```python\nimport gradio as gr\n\ndemo = gr.load(\"Helsinki-NLP/opus-mt-en-es\", src=\"models\")\n\ndemo.launch()\n```\n\nFor any Hugging Face model supported in Inference Endpoints, Gradio automatically infers the expected input and output and make the underlying server calls, so you don't have to worry about defining the prediction function. \n\nNotice that we just put specify the model name and state that the `src` should be `models` (Hugging Face's Model Hub). There is no need to install any dependencies (except `gradio`) since you are not loading the model on your computer.\n\nYou might notice that the first inference takes a little bit longer. This happens since the Inference Endpoints is loading the model in the server. You get some benefits afterward:\n\n- The inference will be much faster.\n- The server caches your requests.\n- You get built-in automatic scaling.\n\n", "heading1": "Demos with the Hugging Face Inference Endpoints", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "[Hugging Face Spaces](https://hf.co/spaces) allows anyone to host their Gradio demos freely, and uploading your Gradio demos take a couple of minutes. You can head to [hf.co/new-space](https://huggingface.co/new-space), select the Gradio SDK, create an `app.py` file, and voila! You have a demo you can share with anyone else. To learn more, read [this guide how to host on Hugging Face Spaces using the website](https://huggingface.co/blog/gradio-spaces).\n\nAlternatively, you can create a Space programmatically, making use of the [huggingface_hub client library](https://huggingface.co/docs/huggingface_hub/index) library. Here's an example:\n\n```python\nfrom huggingface_hub import (\n    create_repo,\n    get_full_repo_name,\n    upload_file,\n)\ncreate_repo(name=target_space_name, token=hf_token, repo_type=\"space\", space_sdk=\"gradio\")\nrepo_name = get_full_repo_name(model_id=target_space_name, token=hf_token)\nfile_url = upload_file(\n    path_or_fileobj=\"file.txt\",\n    path_in_repo=\"app.py\",\n    repo_id=repo_name,\n    repo_type=\"space\",\n    token=hf_token,\n)\n```\n\nHere, `create_repo` creates a gradio repo with the target name under a specific account using that account's Write Token. `repo_name` gets the full repo name of the related repo. Finally `upload_file` uploads a file inside the repo with the name `app.py`.\n\n\n", "heading1": "Hosting your Gradio demos on Spaces", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "You can also use and remix existing Gradio demos on Hugging Face Spaces. For example, you could take two existing Gradio demos on Spaces and put them as separate tabs and create a new demo. You can run this new demo locally, or upload it to Spaces, allowing endless possibilities to remix and create new demos!\n\nHere's an example that does exactly that:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n  with gr.Tab(\"Translate to Spanish\"):\n    gr.load(\"gradio/en2es\", src=\"spaces\")\n  with gr.Tab(\"Translate to French\"):\n    gr.load(\"abidlabs/en2fr\", src=\"spaces\")\n\ndemo.launch()\n```\n\nNotice that we use `gr.load()`, the same method we used to load models using Inference Endpoints. However, here we specify that the `src` is `spaces` (Hugging Face Spaces). \n\nNote: loading a Space in this way may result in slight differences from the original Space. In particular, any attributes that apply to the entire Blocks, such as the theme or custom CSS/JS, will not be loaded. You can copy these properties from the Space you are loading into your own `Blocks` object. \n\n", "heading1": "Loading demos from Spaces", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Hugging Face's popular `transformers` library has a very easy-to-use abstraction, [`pipeline()`](https://huggingface.co/docs/transformers/v4.16.2/en/main_classes/pipelinestransformers.pipeline) that handles most of the complex code to offer a simple API for common tasks. By specifying the task and an (optional) model, you can build a demo around an existing model with few lines of Python:\n\n```python\nimport gradio as gr\n\nfrom transformers import pipeline\n\npipe = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-es\")\n\ndef predict(text):\n  return pipe(text)[0][\"translation_text\"]\n\ndemo = gr.Interface(\n  fn=predict,\n  inputs='text',\n  outputs='text',\n)\n\ndemo.launch()\n```\n\nBut `gradio` actually makes it even easier to convert a `pipeline` to a demo, simply by using the `gradio.Interface.from_pipeline` methods, which skips the need to specify the input and output components:\n\n```python\nfrom transformers import pipeline\nimport gradio as gr\n\npipe = pipeline(\"translation\", model=\"Helsinki-NLP/opus-mt-en-es\")\n\ndemo = gr.Interface.from_pipeline(pipe)\ndemo.launch()\n```\n\nThe previous code produces the following interface, which you can try right here in your browser:\n\n<gradio-app space=\"gradio/en2es\"></gradio-app>\n\n\n", "heading1": "Demos with the `Pipeline` in `transformers`", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "That's it! Let's recap the various ways Gradio and Hugging Face work together:\n\n1. You can build a demo around Inference Endpoints without having to load the model, by using `gr.load()`.\n2. You host your Gradio demo on Hugging Face Spaces, either using the GUI or entirely in Python.\n3. You can load demos from Hugging Face Spaces to remix and create new Gradio demos using `gr.load()`.\n4. You can convert a `transformers` pipeline into a Gradio demo using `from_pipeline()`.\n\n\ud83e\udd17\n", "heading1": "Recap", "source_page_url": "https://gradio.app/guides/using-hugging-face-integrations", "source_page_title": "Other Tutorials - Using Hugging Face Integrations Guide"}, {"text": "Let's go through a simple example to understand how to containerize a Gradio app using Docker.\n\nStep 1: Create Your Gradio App\n\nFirst, we need a simple Gradio app. Let's create a Python file named `app.py` with the following content:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n    return f\"Hello {name}!\"\n\niface = gr.Interface(fn=greet, inputs=\"text\", outputs=\"text\").launch()\n```\n\nThis app creates a simple interface that greets the user by name.\n\nStep 2: Create a Dockerfile\n\nNext, we'll create a Dockerfile to specify how our app should be built and run in a Docker container. Create a file named `Dockerfile` in the same directory as your app with the following content:\n\n```dockerfile\nFROM python:3.10-slim\n\nWORKDIR /usr/src/app\nCOPY . .\nRUN pip install --no-cache-dir gradio\nEXPOSE 7860\nENV GRADIO_SERVER_NAME=\"0.0.0.0\"\n\nCMD [\"python\", \"app.py\"]\n```\n\nThis Dockerfile performs the following steps:\n- Starts from a Python 3.10 slim image.\n- Sets the working directory and copies the app into the container.\n- Installs Gradio (you should install all other requirements as well).\n- Exposes port 7860 (Gradio's default port).\n- Sets the `GRADIO_SERVER_NAME` environment variable to ensure Gradio listens on all network interfaces.\n- Specifies the command to run the app.\n\nStep 3: Build and Run Your Docker Container\n\nWith the Dockerfile in place, you can build and run your container:\n\n```bash\ndocker build -t gradio-app .\ndocker run -p 7860:7860 gradio-app\n```\n\nYour Gradio app should now be accessible at `http://localhost:7860`.\n\n", "heading1": "How to Dockerize a Gradio App", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-docker", "source_page_title": "Other Tutorials - Deploying Gradio With Docker Guide"}, {"text": "When running Gradio applications in Docker, there are a few important things to keep in mind:\n\nRunning the Gradio app on `\"0.0.0.0\"` and exposing port 7860\n\nIn the Docker environment, setting `GRADIO_SERVER_NAME=\"0.0.0.0\"` as an environment variable (or directly in your Gradio app's `launch()` function) is crucial for allowing connections from outside the container. And the `EXPOSE 7860` directive in the Dockerfile tells Docker to expose Gradio's default port on the container to enable external access to the Gradio app. \n\nEnable Stickiness for Multiple Replicas\n\nWhen deploying Gradio apps with multiple replicas, such as on AWS ECS, it's important to enable stickiness with `sessionAffinity: ClientIP`. This ensures that all requests from the same user are routed to the same instance. This is important because Gradio's communication protocol requires multiple separate connections from the frontend to the backend in order for events to be processed correctly. (If you use Terraform, you'll want to add a [stickiness block](https://registry.terraform.io/providers/hashicorp/aws/3.14.1/docs/resources/lb_target_groupstickiness) into your target group definition.)\n\nDeploying Behind a Proxy\n\nIf you're deploying your Gradio app behind a proxy, like Nginx, it's essential to configure the proxy correctly. Gradio provides a [Guide that walks through the necessary steps](https://www.gradio.app/guides/running-gradio-on-your-web-server-with-nginx). This setup ensures your app is accessible and performs well in production environments.\n\n", "heading1": "Important Considerations", "source_page_url": "https://gradio.app/guides/deploying-gradio-with-docker", "source_page_title": "Other Tutorials - Deploying Gradio With Docker Guide"}, {"text": "Let\u2019s start with a simple example of integrating a C++ program into a Gradio app. Suppose we have the following C++ program that adds two numbers:\n\n```cpp\n// add.cpp\ninclude <iostream>\n\nint main() {\n    double a, b;\n    std::cin >> a >> b;\n    std::cout << a + b << std::endl;\n    return 0;\n}\n```\n\nThis program reads two numbers from standard input, adds them, and outputs the result.\n\nWe can build a Gradio interface around this C++ program using Python's `subprocess` module. Here\u2019s the corresponding Python code:\n\n```python\nimport gradio as gr\nimport subprocess\n\ndef add_numbers(a, b):\n    process = subprocess.Popen(\n        ['./add'], \n        stdin=subprocess.PIPE, \n        stdout=subprocess.PIPE, \n        stderr=subprocess.PIPE\n    )\n    output, error = process.communicate(input=f\"{a} {b}\\n\".encode())\n    \n    if error:\n        return f\"Error: {error.decode()}\"\n    return float(output.decode().strip())\n\ndemo = gr.Interface(\n    fn=add_numbers, \n    inputs=[gr.Number(label=\"Number 1\"), gr.Number(label=\"Number 2\")], \n    outputs=gr.Textbox(label=\"Result\")\n)\n\ndemo.launch()\n```\n\nHere, `subprocess.Popen` is used to execute the compiled C++ program (`add`), pass the input values, and capture the output. You can compile the C++ program by running:\n\n```bash\ng++ -o add add.cpp\n```\n\nThis example shows how easy it is to call C++ from Python using `subprocess` and build a Gradio interface around it.\n\n", "heading1": "Using Gradio with C++", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Now, let\u2019s move to another example: calling a Rust program to apply a sepia filter to an image. The Rust code could look something like this:\n\n```rust\n// sepia.rs\nextern crate image;\n\nuse image::{GenericImageView, ImageBuffer, Rgba};\n\nfn sepia_filter(input: &str, output: &str) {\n    let img = image::open(input).unwrap();\n    let (width, height) = img.dimensions();\n    let mut img_buf = ImageBuffer::new(width, height);\n\n    for (x, y, pixel) in img.pixels() {\n        let (r, g, b, a) = (pixel[0] as f32, pixel[1] as f32, pixel[2] as f32, pixel[3]);\n        let tr = (0.393 * r + 0.769 * g + 0.189 * b).min(255.0);\n        let tg = (0.349 * r + 0.686 * g + 0.168 * b).min(255.0);\n        let tb = (0.272 * r + 0.534 * g + 0.131 * b).min(255.0);\n        img_buf.put_pixel(x, y, Rgba([tr as u8, tg as u8, tb as u8, a]));\n    }\n\n    img_buf.save(output).unwrap();\n}\n\nfn main() {\n    let args: Vec<String> = std::env::args().collect();\n    if args.len() != 3 {\n        eprintln!(\"Usage: sepia <input_file> <output_file>\");\n        return;\n    }\n    sepia_filter(&args[1], &args[2]);\n}\n```\n\nThis Rust program applies a sepia filter to an image. It takes two command-line arguments: the input image path and the output image path. You can compile this program using:\n\n```bash\ncargo build --release\n```\n\nNow, we can call this Rust program from Python and use Gradio to build the interface:\n\n```python\nimport gradio as gr\nimport subprocess\n\ndef apply_sepia(input_path):\n    output_path = \"output.png\"\n    \n    process = subprocess.Popen(\n        ['./target/release/sepia', input_path, output_path], \n        stdout=subprocess.PIPE, \n        stderr=subprocess.PIPE\n    )\n    process.wait()\n    \n    return output_path\n\ndemo = gr.Interface(\n    fn=apply_sepia, \n    inputs=gr.Image(type=\"filepath\", label=\"Input Image\"), \n    outputs=gr.Image(label=\"Sepia Image\")\n)\n\ndemo.launch()\n```\n\nHere, when a user uploads an image and clicks submit, Gradio calls the Rust binary (`sepia`) to process the image, and re", "heading1": "Using Gradio with Rust", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "nput Image\"), \n    outputs=gr.Image(label=\"Sepia Image\")\n)\n\ndemo.launch()\n```\n\nHere, when a user uploads an image and clicks submit, Gradio calls the Rust binary (`sepia`) to process the image, and returns the sepia-filtered output to Gradio.\n\nThis setup showcases how you can integrate performance-critical or specialized code written in Rust into a Gradio interface.\n\n", "heading1": "Using Gradio with Rust", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Integrating Gradio with R is particularly straightforward thanks to the `reticulate` package, which allows you to run Python code directly in R. Let\u2019s walk through an example of using Gradio in R. \n\n**Installation**\n\nFirst, you need to install the `reticulate` package in R:\n\n```r\ninstall.packages(\"reticulate\")\n```\n\n\nOnce installed, you can use the package to run Gradio directly from within an R script.\n\n\n```r\nlibrary(reticulate)\n\npy_install(\"gradio\", pip = TRUE)\n\ngr <- import(\"gradio\") import gradio as gr\n```\n\n**Building a Gradio Application**\n\nWith gradio installed and imported, we now have access to gradio's app building methods. Let's build a simple app for an R function that returns a greeting\n\n```r\ngreeting <- \\(name) paste(\"Hello\", name)\n\napp <- gr$Interface(\n  fn = greeting,\n  inputs = gr$Text(label = \"Name\"),\n  outputs = gr$Text(label = \"Greeting\"),\n  title = \"Hello! &128515 &128075\"\n)\n\napp$launch(server_name = \"localhost\", \n           server_port = as.integer(3000))\n```\n\nCredit to [@IfeanyiIdiaye](https://github.com/Ifeanyi55) for contributing this section. You can see more examples [here](https://github.com/Ifeanyi55/Gradio-in-R/tree/main/Code), including using Gradio Blocks to build a machine learning application in R.\n", "heading1": "Using Gradio with R (via `reticulate`)", "source_page_url": "https://gradio.app/guides/using-gradio-in-other-programming-languages", "source_page_title": "Other Tutorials - Using Gradio In Other Programming Languages Guide"}, {"text": "Image classification is a central task in computer vision. Building better classifiers to classify what object is present in a picture is an active area of research, as it has applications stretching from facial recognition to manufacturing quality control.\n\nState-of-the-art image classifiers are based on the _transformers_ architectures, originally popularized for NLP tasks. Such architectures are typically called vision transformers (ViT). Such models are perfect to use with Gradio's _image_ input component, so in this tutorial we will build a web demo to classify images using Gradio. We will be able to build the whole web application in a **single line of Python**, and it will look like the demo on the bottom of the page.\n\nLet's get started!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "First, we will need an image classification model. For this tutorial, we will use a model from the [Hugging Face Model Hub](https://huggingface.co/models?pipeline_tag=image-classification). The Hub contains thousands of models covering dozens of different machine learning tasks.\n\nExpand the Tasks category on the left sidebar and select \"Image Classification\" as our task of interest. You will then see all of the models on the Hub that are designed to classify images.\n\nAt the time of writing, the most popular one is `google/vit-base-patch16-224`, which has been trained on ImageNet images at a resolution of 224x224 pixels. We will use this model for our demo.\n\n", "heading1": "Step 1 \u2014 Choosing a Vision Image Classification Model", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "When using a model from the Hugging Face Hub, we do not need to define the input or output components for the demo. Similarly, we do not need to be concerned with the details of preprocessing or postprocessing.\nAll of these are automatically inferred from the model tags.\n\nBesides the import statement, it only takes a single line of Python to load and launch the demo.\n\nWe use the `gr.Interface.load()` method and pass in the path to the model including the `huggingface/` to designate that it is from the Hugging Face Hub.\n\n```python\nimport gradio as gr\n\ngr.Interface.load(\n             \"huggingface/google/vit-base-patch16-224\",\n             examples=[\"alligator.jpg\", \"laptop.jpg\"]).launch()\n```\n\nNotice that we have added one more parameter, the `examples`, which allows us to prepopulate our interfaces with a few predefined examples.\n\nThis produces the following interface, which you can try right here in your browser. When you input an image, it is automatically preprocessed and sent to the Hugging Face Hub API, where it is passed through the model and returned as a human-interpretable prediction. Try uploading your own image!\n\n<gradio-app space=\"gradio/vision-transformer\">\n\n---\n\nAnd you're done! In one line of code, you have built a web demo for an image classifier. If you'd like to share with others, try setting `share=True` when you `launch()` the Interface!\n", "heading1": "Step 2 \u2014 Loading the Vision Transformer Model with Gradio", "source_page_url": "https://gradio.app/guides/image-classification-with-vision-transformers", "source_page_title": "Other Tutorials - Image Classification With Vision Transformers Guide"}, {"text": "To use Gradio with BigQuery, you will need to obtain your BigQuery credentials and use them with the [BigQuery Python client](https://pypi.org/project/google-cloud-bigquery/). If you already have BigQuery credentials (as a `.json` file), you can skip this section. If not, you can do this for free in just a couple of minutes.\n\n1. First, log in to your Google Cloud account and go to the Google Cloud Console (https://console.cloud.google.com/)\n\n2. In the Cloud Console, click on the hamburger menu in the top-left corner and select \"APIs & Services\" from the menu. If you do not have an existing project, you will need to create one.\n\n3. Then, click the \"+ Enabled APIs & services\" button, which allows you to enable specific services for your project. Search for \"BigQuery API\", click on it, and click the \"Enable\" button. If you see the \"Manage\" button, then the BigQuery is already enabled, and you're all set.\n\n4. In the APIs & Services menu, click on the \"Credentials\" tab and then click on the \"Create credentials\" button.\n\n5. In the \"Create credentials\" dialog, select \"Service account key\" as the type of credentials to create, and give it a name. Also grant the service account permissions by giving it a role such as \"BigQuery User\", which will allow you to run queries.\n\n6. After selecting the service account, select the \"JSON\" key type and then click on the \"Create\" button. This will download the JSON key file containing your credentials to your computer. It will look something like this:\n\n```json\n{\n\t\"type\": \"service_account\",\n\t\"project_id\": \"your project\",\n\t\"private_key_id\": \"your private key id\",\n\t\"private_key\": \"private key\",\n\t\"client_email\": \"email\",\n\t\"client_id\": \"client id\",\n\t\"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n\t\"token_uri\": \"https://accounts.google.com/o/oauth2/token\",\n\t\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n\t\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/email_id\"\n}\n```\n\n", "heading1": "Setting up your BigQuery Credentials", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-bigquery-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Bigquery Data Guide"}, {"text": "Once you have the credentials, you will need to use the BigQuery Python client to authenticate using your credentials. To do this, you will need to install the BigQuery Python client by running the following command in the terminal:\n\n```bash\npip install google-cloud-bigquery[pandas]\n```\n\nYou'll notice that we've installed the pandas add-on, which will be helpful for processing the BigQuery dataset as a pandas dataframe. Once the client is installed, you can authenticate using your credentials by running the following code:\n\n```py\nfrom google.cloud import bigquery\n\nclient = bigquery.Client.from_service_account_json(\"path/to/key.json\")\n```\n\nWith your credentials authenticated, you can now use the BigQuery Python client to interact with your BigQuery datasets.\n\nHere is an example of a function which queries the `covid19_nyt.us_counties` dataset in BigQuery to show the top 20 counties with the most confirmed cases as of the current day:\n\n```py\nimport numpy as np\n\nQUERY = (\n    'SELECT * FROM `bigquery-public-data.covid19_nyt.us_counties` '\n    'ORDER BY date DESC,confirmed_cases DESC '\n    'LIMIT 20')\n\ndef run_query():\n    query_job = client.query(QUERY)\n    query_result = query_job.result()\n    df = query_result.to_dataframe()\n    Select a subset of columns\n    df = df[[\"confirmed_cases\", \"deaths\", \"county\", \"state_name\"]]\n    Convert numeric columns to standard numpy types\n    df = df.astype({\"deaths\": np.int64, \"confirmed_cases\": np.int64})\n    return df\n```\n\n", "heading1": "Using the BigQuery Client", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-bigquery-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Bigquery Data Guide"}, {"text": "Once you have a function to query the data, you can use the `gr.DataFrame` component from the Gradio library to display the results in a tabular format. This is a useful way to inspect the data and make sure that it has been queried correctly.\n\nHere is an example of how to use the `gr.DataFrame` component to display the results. By passing in the `run_query` function to `gr.DataFrame`, we instruct Gradio to run the function as soon as the page loads and show the results. In addition, you also pass in the keyword `every` to tell the dashboard to refresh every hour (60\\*60 seconds).\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.DataFrame(run_query, every=gr.Timer(60*60))\n\ndemo.launch()\n```\n\nPerhaps you'd like to add a visualization to our dashboard. You can use the `gr.ScatterPlot()` component to visualize the data in a scatter plot. This allows you to see the relationship between different variables such as case count and case deaths in the dataset and can be useful for exploring the data and gaining insights. Again, we can do this in real-time\nby passing in the `every` parameter.\n\nHere is a complete example showing how to use the `gr.ScatterPlot` to visualize in addition to displaying data with the `gr.DataFrame`\n\n```py\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"\ud83d\udc89 Covid Dashboard (Updated Hourly)\")\n    with gr.Row():\n        gr.DataFrame(run_query, every=gr.Timer(60*60))\n        gr.ScatterPlot(run_query, every=gr.Timer(60*60), x=\"confirmed_cases\",\n                        y=\"deaths\", tooltip=\"county\", width=500, height=500)\n\ndemo.queue().launch()  Run the demo with queuing enabled\n```\n", "heading1": "Building the Real-Time Dashboard", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-bigquery-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Bigquery Data Guide"}, {"text": "Data visualization is a crucial aspect of data analysis and machine learning. The Gradio `DataFrame` component is a popular way to display tabular data within a web application. \n\nBut what if you want to stylize the table of data? What if you want to add background colors, partially highlight cells, or change the display precision of numbers? This Guide is for you!\n\n\n\nLet's dive in!\n\n**Prerequisites**: We'll be using the `gradio.Blocks` class in our examples.\nYou can [read the Guide to Blocks first](https://gradio.app/blocks-and-event-listeners) if you are not already familiar with it. Also please make sure you are using the **latest version** version of Gradio: `pip install --upgrade gradio`.\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "The Gradio `DataFrame` component now supports values of the type `Styler` from the `pandas` class. This allows us to reuse the rich existing API and documentation of the `Styler` class instead of inventing a new style format on our own. Here's a complete example of how it looks:\n\n```python\nimport pandas as pd \nimport gradio as gr\n\nCreating a sample dataframe\ndf = pd.DataFrame({\n    \"A\" : [14, 4, 5, 4, 1], \n    \"B\" : [5, 2, 54, 3, 2], \n    \"C\" : [20, 20, 7, 3, 8], \n    \"D\" : [14, 3, 6, 2, 6], \n    \"E\" : [23, 45, 64, 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n    gr.DataFrame(styler)\n    \ndemo.launch()\n```\n\nThe Styler class can be used to apply conditional formatting and styling to dataframes, making them more visually appealing and interpretable. You can highlight certain values, apply gradients, or even use custom CSS to style the DataFrame. The Styler object is applied to a DataFrame and it returns a new object with the relevant styling properties, which can then be previewed directly, or rendered dynamically in a Gradio interface.\n\nTo read more about the Styler object, read the official `pandas` documentation at: https://pandas.pydata.org/docs/user_guide/style.html\n\nBelow, we'll explore a few examples:\n\nHighlighting Cells\n\nOk, so let's revisit the previous example. We start by creating a `pd.DataFrame` object and then highlight the highest value in each row with a light green color:\n\n```python\nimport pandas as pd \n\nCreating a sample dataframe\ndf = pd.DataFrame({\n    \"A\" : [14, 4, 5, 4, 1], \n    \"B\" : [5, 2, 54, 3, 2], \n    \"C\" : [20, 20, 7, 3, 8], \n    \"D\" : [14, 3, 6, 2, 6], \n    \"E\" : [23, 45, 64, 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n```\n\nNow, we simply pass this object into the Gradio `DataFra", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": ", 32, 23]\n}) \n\nApplying style to highlight the maximum value in each row\nstyler = df.style.highlight_max(color = 'lightgreen', axis = 0)\n```\n\nNow, we simply pass this object into the Gradio `DataFrame` and we can visualize our colorful table of data in 4 lines of python:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Dataframe(styler)\n    \ndemo.launch()\n```\n\nHere's how it looks:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-highlight.png)\n\nFont Colors\n\nApart from highlighting cells, you might want to color specific text within the cells. Here's how you can change text colors for certain columns:\n\n```python\nimport pandas as pd \nimport gradio as gr\n\nCreating a sample dataframe\ndf = pd.DataFrame({\n    \"A\" : [14, 4, 5, 4, 1], \n    \"B\" : [5, 2, 54, 3, 2], \n    \"C\" : [20, 20, 7, 3, 8], \n    \"D\" : [14, 3, 6, 2, 6], \n    \"E\" : [23, 45, 64, 32, 23]\n}) \n\nFunction to apply text color\ndef highlight_cols(x): \n    df = x.copy() \n    df.loc[:, :] = 'color: purple'\n    df[['B', 'C', 'E']] = 'color: green'\n    return df \n\nApplying the style function\ns = df.style.apply(highlight_cols, axis = None)\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n    gr.DataFrame(s)\n    \ndemo.launch()\n```\n\nIn this script, we define a custom function highlight_cols that changes the text color to purple for all cells, but overrides this for columns B, C, and E with green. Here's how it looks:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-color.png)\n\nDisplay Precision \n\nSometimes, the data you are dealing with might have long floating numbers, and you may want to display only a fixed number of decimals for simplicity. The pandas Styler object allows you to format the precision of numbers displayed. Here's how you can do this:\n\n```python\nimport pandas as pd\nimport gradio as gr\n\nCreating a sample dataframe with floating numbers\ndf = pd.DataFrame({\n    \"A\" : [14.12345, 4.", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "on of numbers displayed. Here's how you can do this:\n\n```python\nimport pandas as pd\nimport gradio as gr\n\nCreating a sample dataframe with floating numbers\ndf = pd.DataFrame({\n    \"A\" : [14.12345, 4.23456, 5.34567, 4.45678, 1.56789], \n    \"B\" : [5.67891, 2.78912, 54.89123, 3.91234, 2.12345], \n    ... other columns\n}) \n\nSetting the precision of numbers to 2 decimal places\ns = df.style.format(\"{:.2f}\")\n\nDisplaying the styled dataframe in Gradio\nwith gr.Blocks() as demo:\n    gr.DataFrame(s)\n    \ndemo.launch()\n```\n\nIn this script, the format method of the Styler object is used to set the precision of numbers to two decimal places. Much cleaner now:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/df-precision.png)\n\n\n\n", "heading1": "The Pandas `Styler`", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "So far, we've been restricting ourselves to styling that is supported by the Pandas `Styler` class. But what if you want to create custom styles like partially highlighting cells based on their values:\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/gradio-guides/dataframe_custom_styling.png)\n\n\nThis isn't possible with `Styler`, but you can do this by creating your own **`styling`** array, which is a 2D array the same size and shape as your data. Each element in this list should be a CSS style string (e.g. `\"background-color: green\"`) that applies to the `<td>` element containing the cell value (or an empty string if no custom CSS should be applied). Similarly, you can create a **`display_value`** array which controls the value that is displayed in each cell (which can be different the underlying value which is the one that is used for searching/sorting).\n\nHere's the complete code for how to can use custom styling with `gr.Dataframe` as in the screenshot above:\n\n$code_dataframe_custom_styling\n\n\n", "heading1": "Custom Styling", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "One thing to keep in mind is that the gradio `DataFrame` component only accepts custom styling objects when it is non-interactive (i.e. in \"static\" mode). If the `DataFrame` component is interactive, then the styling information is ignored and instead the raw table values are shown instead. \n\nThe `DataFrame` component is by default non-interactive, unless it is used as an input to an event. In which case, you can force the component to be non-interactive by setting the `interactive` prop like this:\n\n```python\nc = gr.DataFrame(styler, interactive=False)\n```\n\n", "heading1": "Note about Interactivity", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "This is just a taste of what's possible using the `gradio.DataFrame` component with the `Styler` class from `pandas`. Try it out and let us know what you think!", "heading1": "Conclusion \ud83c\udf89", "source_page_url": "https://gradio.app/guides/styling-the-gradio-dataframe", "source_page_title": "Other Tutorials - Styling The Gradio Dataframe Guide"}, {"text": "First of all, we need some data to visualize. Following this [excellent guide](https://supabase.com/blog/loading-data-supabase-python), we'll create fake commerce data and put it in Supabase.\n\n1\\. Start by creating a new project in Supabase. Once you're logged in, click the \"New Project\" button\n\n2\\. Give your project a name and database password. You can also choose a pricing plan (for our purposes, the Free Tier is sufficient!)\n\n3\\. You'll be presented with your API keys while the database spins up (can take up to 2 minutes).\n\n4\\. Click on \"Table Editor\" (the table icon) in the left pane to create a new table. We'll create a single table called `Product`, with the following schema:\n\n<center>\n<table>\n<tr><td>product_id</td><td>int8</td></tr>\n<tr><td>inventory_count</td><td>int8</td></tr>\n<tr><td>price</td><td>float8</td></tr>\n<tr><td>product_name</td><td>varchar</td></tr>\n</table>\n</center>\n\n5\\. Click Save to save the table schema.\n\nOur table is now ready!\n\n", "heading1": "Create a table in Supabase", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-supabase-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Supabase Data Guide"}, {"text": "The next step is to write data to a Supabase dataset. We will use the Supabase Python library to do this.\n\n6\\. Install `supabase` by running the following command in your terminal:\n\n```bash\npip install supabase\n```\n\n7\\. Get your project URL and API key. Click the Settings (gear icon) on the left pane and click 'API'. The URL is listed in the Project URL box, while the API key is listed in Project API keys (with the tags `service_role`, `secret`)\n\n8\\. Now, run the following Python script to write some fake data to the table (note you have to put the values of `SUPABASE_URL` and `SUPABASE_SECRET_KEY` from step 7):\n\n```python\nimport supabase\n\nInitialize the Supabase client\nclient = supabase.create_client('SUPABASE_URL', 'SUPABASE_SECRET_KEY')\n\nDefine the data to write\nimport random\n\nmain_list = []\nfor i in range(10):\n    value = {'product_id': i,\n             'product_name': f\"Item {i}\",\n             'inventory_count': random.randint(1, 100),\n             'price': random.random()*100\n            }\n    main_list.append(value)\n\nWrite the data to the table\ndata = client.table('Product').insert(main_list).execute()\n```\n\nReturn to your Supabase dashboard and refresh the page, you should now see 10 rows populated in the `Product` table!\n\n", "heading1": "Write data to Supabase", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-supabase-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Supabase Data Guide"}, {"text": "Finally, we will read the data from the Supabase dataset using the same `supabase` Python library and create a realtime dashboard using `gradio`.\n\nNote: We repeat certain steps in this section (like creating the Supabase client) in case you did not go through the previous sections. As described in Step 7, you will need the project URL and API Key for your database.\n\n9\\. Write a function that loads the data from the `Product` table and returns it as a pandas Dataframe:\n\n```python\nimport supabase\nimport pandas as pd\n\nclient = supabase.create_client('SUPABASE_URL', 'SUPABASE_SECRET_KEY')\n\ndef read_data():\n    response = client.table('Product').select(\"*\").execute()\n    df = pd.DataFrame(response.data)\n    return df\n```\n\n10\\. Create a small Gradio Dashboard with 2 Barplots that plots the prices and inventories of all of the items every minute and updates in real-time:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as dashboard:\n    with gr.Row():\n        gr.BarPlot(read_data, x=\"product_id\", y=\"price\", title=\"Prices\", every=gr.Timer(60))\n        gr.BarPlot(read_data, x=\"product_id\", y=\"inventory_count\", title=\"Inventory\", every=gr.Timer(60))\n\ndashboard.queue().launch()\n```\n\nNotice that by passing in a function to `gr.BarPlot()`, we have the BarPlot query the database as soon as the web app loads (and then again every 60 seconds because of the `every` parameter). Your final dashboard should look something like this:\n\n<gradio-app space=\"gradio/supabase\"></gradio-app>\n\n", "heading1": "Visualize the Data in a Real-Time Gradio Dashboard", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-supabase-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Supabase Data Guide"}, {"text": "That's it! In this tutorial, you learned how to write data to a Supabase dataset, and then read that data and plot the results as bar plots. If you update the data in the Supabase database, you'll notice that the Gradio dashboard will update within a minute.\n\nTry adding more plots and visualizations to this example (or with a different dataset) to build a more complex dashboard!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/creating-a-dashboard-from-supabase-data", "source_page_title": "Other Tutorials - Creating A Dashboard From Supabase Data Guide"}, {"text": "A virtual environment in Python is a self-contained directory that holds a Python installation for a particular version of Python, along with a number of additional packages. This environment is isolated from the main Python installation and other virtual environments. Each environment can have its own independent set of installed Python packages, which allows you to maintain different versions of libraries for different projects without conflicts.\n\n\nUsing virtual environments ensures that you can work on multiple Python projects on the same machine without any conflicts. This is particularly useful when different projects require different versions of the same library. It also simplifies dependency management and enhances reproducibility, as you can easily share the requirements of your project with others.\n\n\n", "heading1": "Virtual Environments", "source_page_url": "https://gradio.app/guides/installing-gradio-in-a-virtual-environment", "source_page_title": "Other Tutorials - Installing Gradio In A Virtual Environment Guide"}, {"text": "To install Gradio on a Windows system in a virtual environment, follow these steps:\n\n1. **Install Python**: Ensure you have Python 3.10 or higher installed. You can download it from [python.org](https://www.python.org/). You can verify the installation by running `python --version` or `python3 --version` in Command Prompt.\n\n\n2. **Create a Virtual Environment**:\n   Open Command Prompt and navigate to your project directory. Then create a virtual environment using the following command:\n\n   ```bash\n   python -m venv gradio-env\n   ```\n\n   This command creates a new directory `gradio-env` in your project folder, containing a fresh Python installation.\n\n3. **Activate the Virtual Environment**:\n   To activate the virtual environment, run:\n\n   ```bash\n   .\\gradio-env\\Scripts\\activate\n   ```\n\n   Your command prompt should now indicate that you are working inside `gradio-env`. Note: you can choose a different name than `gradio-env` for your virtual environment in this step.\n\n\n4. **Install Gradio**:\n   Now, you can install Gradio using pip:\n\n   ```bash\n   pip install gradio\n   ```\n\n5. **Verification**:\n   To verify the installation, run `python` and then type:\n\n   ```python\n   import gradio as gr\n   print(gr.__version__)\n   ```\n\n   This will display the installed version of Gradio.\n\n", "heading1": "Installing Gradio on Windows", "source_page_url": "https://gradio.app/guides/installing-gradio-in-a-virtual-environment", "source_page_title": "Other Tutorials - Installing Gradio In A Virtual Environment Guide"}, {"text": "The installation steps on MacOS and Linux are similar to Windows but with some differences in commands.\n\n1. **Install Python**:\n   Python usually comes pre-installed on MacOS and most Linux distributions. You can verify the installation by running `python --version` in the terminal (note that depending on how Python is installed, you might have to use `python3` instead of `python` throughout these steps). \n   \n   Ensure you have Python 3.10 or higher installed. If you do not have it installed, you can download it from [python.org](https://www.python.org/). \n\n2. **Create a Virtual Environment**:\n   Open Terminal and navigate to your project directory. Then create a virtual environment using:\n\n   ```bash\n   python -m venv gradio-env\n   ```\n\n   Note: you can choose a different name than `gradio-env` for your virtual environment in this step.\n\n3. **Activate the Virtual Environment**:\n   To activate the virtual environment on MacOS/Linux, use:\n\n   ```bash\n   source gradio-env/bin/activate\n   ```\n\n4. **Install Gradio**:\n   With the virtual environment activated, install Gradio using pip:\n\n   ```bash\n   pip install gradio\n   ```\n\n5. **Verification**:\n   To verify the installation, run `python` and then type:\n\n   ```python\n   import gradio as gr\n   print(gr.__version__)\n   ```\n\n   This will display the installed version of Gradio.\n\nBy following these steps, you can successfully install Gradio in a virtual environment on your operating system, ensuring a clean and managed workspace for your Python projects.", "heading1": "Installing Gradio on MacOS/Linux", "source_page_url": "https://gradio.app/guides/installing-gradio-in-a-virtual-environment", "source_page_title": "Other Tutorials - Installing Gradio In A Virtual Environment Guide"}, {"text": "When you are building a Gradio demo, particularly out of Blocks, you may find it cumbersome to keep re-running your code to test your changes.\n\nTo make it faster and more convenient to write your code, we've made it easier to \"reload\" your Gradio apps instantly when you are developing in a **Python IDE** (like VS Code, Sublime Text, PyCharm, or so on) or generally running your Python code from the terminal. We've also developed an analogous \"magic command\" that allows you to re-run cells faster if you use **Jupyter Notebooks** (or any similar environment like Colab).\n\nThis short Guide will cover both of these methods, so no matter how you write Python, you'll leave knowing how to build Gradio apps faster.\n\n", "heading1": "Why Hot Reloading?", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "If you are building Gradio Blocks using a Python IDE, your file of code (let's name it `run.py`) might look something like this:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"Greetings from Gradio!\")\n    inp = gr.Textbox(placeholder=\"What is your name?\")\n    out = gr.Textbox()\n\n    inp.change(fn=lambda x: f\"Welcome, {x}!\",\n               inputs=inp,\n               outputs=out)\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\nThe problem is that anytime that you want to make a change to your layout, events, or components, you have to close and rerun your app by writing `python run.py`.\n\nInstead of doing this, you can run your code in **reload mode** by changing 1 word: `python` to `gradio`:\n\nIn the terminal, run `gradio run.py`. That's it!\n\nNow, you'll see that after you'll see something like this:\n\n```bash\nWatching: '/Users/freddy/sources/gradio/gradio', '/Users/freddy/sources/gradio/demo/'\n\nRunning on local URL:  http://127.0.0.1:7860\n```\n\nThe important part here is the line that says `Watching...` What's happening here is that Gradio will be observing the directory where `run.py` file lives, and if the file changes, it will automatically rerun the file for you. So you can focus on writing your code, and your Gradio demo will refresh automatically \ud83e\udd73\n\nTip: the `gradio` command does not detect the parameters passed to the `launch()` methods because the `launch()` method is never called in reload mode. For example, setting `auth`, or `show_error` in `launch()` will not be reflected in the app.\n\nThere is one important thing to keep in mind when using the reload mode: Gradio specifically looks for a Gradio Blocks/Interface demo called `demo` in your code. If you have named your demo something else, you will need to pass in the name of your demo as the 2nd parameter in your code. So if your `run.py` file looked like this:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as my_demo:\n    gr.Markdown(\"Greetings from Gradio!\")\n    inp = gr.", "heading1": "Python IDE Reload \ud83d\udd25", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "emo as the 2nd parameter in your code. So if your `run.py` file looked like this:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as my_demo:\n    gr.Markdown(\"Greetings from Gradio!\")\n    inp = gr.Textbox(placeholder=\"What is your name?\")\n    out = gr.Textbox()\n\n    inp.change(fn=lambda x: f\"Welcome, {x}!\",\n               inputs=inp,\n               outputs=out)\n\nif __name__ == \"__main__\":\n    my_demo.launch()\n```\n\nThen you would launch it in reload mode like this: `gradio run.py --demo-name=my_demo`.\n\nBy default, the Gradio use UTF-8 encoding for scripts. **For reload mode**, If you are using encoding formats other than UTF-8 (such as cp1252), make sure you've done like this:\n\n1. Configure encoding declaration of python script, for example: `-*- coding: cp1252 -*-`\n2. Confirm that your code editor has identified that encoding format. \n3. Run like this: `gradio run.py --encoding cp1252`\n\n\ud83d\udd25 If your application accepts command line arguments, you can pass them in as well. Here's an example:\n\n```python\nimport gradio as gr\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--name\", type=str, default=\"User\")\nargs, unknown = parser.parse_known_args()\n\nwith gr.Blocks() as demo:\n    gr.Markdown(f\"Greetings {args.name}!\")\n    inp = gr.Textbox()\n    out = gr.Textbox()\n\n    inp.change(fn=lambda x: x, inputs=inp, outputs=out)\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\nWhich you could run like this: `gradio run.py --name Gretel`\n\nAs a small aside, this auto-reloading happens if you change your `run.py` source code or the Gradio source code. Meaning that this can be useful if you decide to [contribute to Gradio itself](https://github.com/gradio-app/gradio/blob/main/CONTRIBUTING.md) \u2705\n\n\n", "heading1": "Python IDE Reload \ud83d\udd25", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "By default, reload mode will re-run your entire script for every change you make.\nBut there are some cases where this is not desirable.\nFor example, loading a machine learning model should probably only happen once to save time. There are also some Python libraries that use C or Rust extensions that throw errors when they are reloaded, like `numpy` and `tiktoken`.\n\nIn these situations, you can place code that you do not want to be re-run inside an `if gr.NO_RELOAD:`  codeblock. Here's an example of how you can use it to only load a transformers model once during the development process.\n\nTip: The value of `gr.NO_RELOAD` is `True`. So you don't have to change your script when you are done developing and want to run it in production. Simply run the file with `python` instead of `gradio`.\n\n```python\nimport gradio as gr\n\nif gr.NO_RELOAD:\n\tfrom transformers import pipeline\n\tpipe = pipeline(\"text-classification\", model=\"cardiffnlp/twitter-roberta-base-sentiment-latest\")\n\ndemo = gr.Interface(lambda s: {d[\"label\"]: d[\"score\"] for d in pipe(s)}, gr.Textbox(), gr.Label())\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\n", "heading1": "Controlling the Reload \ud83c\udf9b\ufe0f", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "You can also enable Gradio's **Vibe Mode**, which, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. To enable this, simply run use the `--vibe` flag with Gradio, e.g. `gradio --vibe app.py`.\n\nVibe Mode lets you describe commands using natural language and have an LLM write or edit the code in your Gradio app. The LLM is powered by Hugging Face's [Inference Providers](https://huggingface.co/docs/inference-providers/en/index), so you must be logged into Hugging Face locally to use this. \n\nNote: When Vibe Mode is enabled, anyone who can access the Gradio endpoint can modify files and run arbitrary code on the host machine. Use only for local development.\n\n", "heading1": "Vibe Mode", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "What about if you use Jupyter Notebooks (or Colab Notebooks, etc.) to develop code? We got something for you too!\n\nWe've developed a **magic command** that will create and run a Blocks demo for you. To use this, load the gradio extension at the top of your notebook:\n\n`%load_ext gradio`\n\nThen, in the cell that you are developing your Gradio demo, simply write the magic command **`%%blocks`** at the top, and then write the layout and components like you would normally:\n\n```py\n%%blocks\n\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(f\"Greetings {args.name}!\")\n    inp = gr.Textbox()\n    out = gr.Textbox()\n\n    inp.change(fn=lambda x: x, inputs=inp, outputs=out)\n```\n\nNotice that:\n\n- You do not need to launch your demo \u2014 Gradio does that for you automatically!\n\n- Every time you rerun the cell, Gradio will re-render your app on the same port and using the same underlying web server. This means you'll see your changes _much, much faster_ than if you were rerunning the cell normally.\n\nHere's what it looks like in a jupyter notebook:\n\n![](https://gradio-builds.s3.amazonaws.com/demo-files/jupyter_reload.gif)\n\n\ud83e\ude84 This works in colab notebooks too! [Here's a colab notebook](https://colab.research.google.com/drive/1zAuWoiTIb3O2oitbtVb2_ekv1K6ggtC1?usp=sharing) where you can see the Blocks magic in action. Try making some changes and re-running the cell with the Gradio code!\n\nTip: You may have to use `%%blocks --share` in Colab to get the demo to appear in the cell.\n\nThe Notebook Magic is now the author's preferred way of building Gradio demos. Regardless of how you write Python code, we hope either of these methods will give you a much better development experience using Gradio.\n\n---\n\n", "heading1": "Jupyter Notebook Magic \ud83d\udd2e", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "Now that you know how to develop quickly using Gradio, start building your own!\n\nIf you are looking for inspiration, try exploring demos other people have built with Gradio, [browse public Hugging Face Spaces](http://hf.space/) \ud83e\udd17\n", "heading1": "Next Steps", "source_page_url": "https://gradio.app/guides/developing-faster-with-reload-mode", "source_page_title": "Other Tutorials - Developing Faster With Reload Mode Guide"}, {"text": "Tabular data science is the most widely used domain of machine learning, with problems ranging from customer segmentation to churn prediction. Throughout various stages of the tabular data science workflow, communicating your work to stakeholders or clients can be cumbersome; which prevents data scientists from focusing on what matters, such as data analysis and model building. Data scientists can end up spending hours building a dashboard that takes in dataframe and returning plots, or returning a prediction or plot of clusters in a dataset. In this guide, we'll go through how to use `gradio` to improve your data science workflows. We will also talk about how to use `gradio` and [skops](https://skops.readthedocs.io/en/stable/) to build interfaces with only one line of code!\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-gradio-for-tabular-workflows", "source_page_title": "Other Tutorials - Using Gradio For Tabular Workflows Guide"}, {"text": "We will take a look at how we can create a simple UI that predicts failures based on product information.\n\n```python\nimport gradio as gr\nimport pandas as pd\nimport joblib\nimport datasets\n\n\ninputs = [gr.Dataframe(row_count = (2, \"dynamic\"), col_count=(4,\"dynamic\"), label=\"Input Data\", interactive=1)]\n\noutputs = [gr.Dataframe(row_count = (2, \"dynamic\"), col_count=(1, \"fixed\"), label=\"Predictions\", headers=[\"Failures\"])]\n\nmodel = joblib.load(\"model.pkl\")\n\nwe will give our dataframe as example\ndf = datasets.load_dataset(\"merve/supersoaker-failures\")\ndf = df[\"train\"].to_pandas()\n\ndef infer(input_dataframe):\n  return pd.DataFrame(model.predict(input_dataframe))\n\ngr.Interface(fn = infer, inputs = inputs, outputs = outputs, examples = [[df.head(2)]]).launch()\n```\n\nLet's break down above code.\n\n- `fn`: the inference function that takes input dataframe and returns predictions.\n- `inputs`: the component we take our input with. We define our input as dataframe with 2 rows and 4 columns, which initially will look like an empty dataframe with the aforementioned shape. When the `row_count` is set to `dynamic`, you don't have to rely on the dataset you're inputting to pre-defined component.\n- `outputs`: The dataframe component that stores outputs. This UI can take single or multiple samples to infer, and returns 0 or 1 for each sample in one column, so we give `row_count` as 2 and `col_count` as 1 above. `headers` is a list made of header names for dataframe.\n- `examples`: You can either pass the input by dragging and dropping a CSV file, or a pandas DataFrame through examples, which headers will be automatically taken by the interface.\n\nWe will now create an example for a minimal data visualization dashboard. You can find a more comprehensive version in the related Spaces.\n\n<gradio-app space=\"gradio/tabular-playground\"></gradio-app>\n\n```python\nimport gradio as gr\nimport pandas as pd\nimport datasets\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndf = datasets.load_dataset", "heading1": "Let's Create a Simple Interface!", "source_page_url": "https://gradio.app/guides/using-gradio-for-tabular-workflows", "source_page_title": "Other Tutorials - Using Gradio For Tabular Workflows Guide"}, {"text": "app space=\"gradio/tabular-playground\"></gradio-app>\n\n```python\nimport gradio as gr\nimport pandas as pd\nimport datasets\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndf = datasets.load_dataset(\"merve/supersoaker-failures\")\ndf = df[\"train\"].to_pandas()\ndf.dropna(axis=0, inplace=True)\n\ndef plot(df):\n  plt.scatter(df.measurement_13, df.measurement_15, c = df.loading,alpha=0.5)\n  plt.savefig(\"scatter.png\")\n  df['failure'].value_counts().plot(kind='bar')\n  plt.savefig(\"bar.png\")\n  sns.heatmap(df.select_dtypes(include=\"number\").corr())\n  plt.savefig(\"corr.png\")\n  plots = [\"corr.png\",\"scatter.png\", \"bar.png\"]\n  return plots\n\ninputs = [gr.Dataframe(label=\"Supersoaker Production Data\")]\noutputs = [gr.Gallery(label=\"Profiling Dashboard\", columns=(1,3))]\n\ngr.Interface(plot, inputs=inputs, outputs=outputs, examples=[df.head(100)], title=\"Supersoaker Failures Analysis Dashboard\").launch()\n```\n\n<gradio-app space=\"gradio/gradio-analysis-dashboard-minimal\"></gradio-app>\n\nWe will use the same dataset we used to train our model, but we will make a dashboard to visualize it this time.\n\n- `fn`: The function that will create plots based on data.\n- `inputs`: We use the same `Dataframe` component we used above.\n- `outputs`: The `Gallery` component is used to keep our visualizations.\n- `examples`: We will have the dataset itself as the example.\n\n", "heading1": "Let's Create a Simple Interface!", "source_page_url": "https://gradio.app/guides/using-gradio-for-tabular-workflows", "source_page_title": "Other Tutorials - Using Gradio For Tabular Workflows Guide"}, {"text": "`skops` is a library built on top of `huggingface_hub` and `sklearn`. With the recent `gradio` integration of `skops`, you can build tabular data interfaces with one line of code!\n\n```python\nimport gradio as gr\n\ntitle and description are optional\ntitle = \"Supersoaker Defective Product Prediction\"\ndescription = \"This model predicts Supersoaker production line failures. Drag and drop any slice from dataset or edit values as you wish in below dataframe component.\"\n\ngr.load(\"huggingface/scikit-learn/tabular-playground\", title=title, description=description).launch()\n```\n\n<gradio-app space=\"gradio/gradio-skops-integration\"></gradio-app>\n\n`sklearn` models pushed to Hugging Face Hub using `skops` include a `config.json` file that contains an example input with column names, the task being solved (that can either be `tabular-classification` or `tabular-regression`). From the task type, `gradio` constructs the `Interface` and consumes column names and the example input to build it. You can [refer to skops documentation on hosting models on Hub](https://skops.readthedocs.io/en/latest/auto_examples/plot_hf_hub.htmlsphx-glr-auto-examples-plot-hf-hub-py) to learn how to push your models to Hub using `skops`.\n", "heading1": "Easily load tabular data interfaces with one line of code using skops", "source_page_url": "https://gradio.app/guides/using-gradio-for-tabular-workflows", "source_page_title": "Other Tutorials - Using Gradio For Tabular Workflows Guide"}, {"text": "In this Guide, we'll walk you through:\n\n- Introduction of ONNX, ONNX model zoo, Gradio, and Hugging Face Spaces\n- How to setup a Gradio demo for EfficientNet-Lite4\n- How to contribute your own Gradio demos for the ONNX organization on Hugging Face\n\nHere's an [example](https://onnx-efficientnet-lite4.hf.space/) of an ONNX model.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "Open Neural Network Exchange ([ONNX](https://onnx.ai/)) is an open standard format for representing machine learning models. ONNX is supported by a community of partners who have implemented it in many frameworks and tools. For example, if you have trained a model in TensorFlow or PyTorch, you can convert it to ONNX easily, and from there run it on a variety of devices using an engine/compiler like ONNX Runtime.\n\nThe [ONNX Model Zoo](https://github.com/onnx/models) is a collection of pre-trained, state-of-the-art models in the ONNX format contributed by community members. Accompanying each model are Jupyter notebooks for model training and running inference with the trained model. The notebooks are written in Python and include links to the training dataset as well as references to the original paper that describes the model architecture.\n\n", "heading1": "What is the ONNX Model Zoo?", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "Gradio\n\nGradio lets users demo their machine learning models as a web app all in python code. Gradio wraps a python function into a user interface and the demos can be launched inside jupyter notebooks, colab notebooks, as well as embedded in your own website and hosted on Hugging Face Spaces for free.\n\nGet started [here](https://gradio.app/getting_started)\n\nHugging Face Spaces\n\nHugging Face Spaces is a free hosting option for Gradio demos. Spaces comes with 3 SDK options: Gradio, Streamlit and Static HTML demos. Spaces can be public or private and the workflow is similar to github repos. There are over 2000+ spaces currently on Hugging Face. Learn more about spaces [here](https://huggingface.co/spaces/launch).\n\nHugging Face Models\n\nHugging Face Model Hub also supports ONNX models and ONNX models can be filtered through the [ONNX tag](https://huggingface.co/models?library=onnx&sort=downloads)\n\n", "heading1": "What are Hugging Face Spaces & Gradio?", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "There are a lot of Jupyter notebooks in the ONNX Model Zoo for users to test models. Previously, users needed to download the models themselves and run those notebooks locally for testing. With Hugging Face, the testing process can be much simpler and more user-friendly. Users can easily try certain ONNX Model Zoo model on Hugging Face Spaces and run a quick demo powered by Gradio with ONNX Runtime, all on cloud without downloading anything locally. Note, there are various runtimes for ONNX, e.g., [ONNX Runtime](https://github.com/microsoft/onnxruntime), [MXNet](https://github.com/apache/incubator-mxnet).\n\n", "heading1": "How did Hugging Face help the ONNX Model Zoo?", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "ONNX Runtime is a cross-platform inference and training machine-learning accelerator. It makes live Gradio demos with ONNX Model Zoo model on Hugging Face possible.\n\nONNX Runtime inference can enable faster customer experiences and lower costs, supporting models from deep learning frameworks such as PyTorch and TensorFlow/Keras as well as classical machine learning libraries such as scikit-learn, LightGBM, XGBoost, etc. ONNX Runtime is compatible with different hardware, drivers, and operating systems, and provides optimal performance by leveraging hardware accelerators where applicable alongside graph optimizations and transforms. For more information please see the [official website](https://onnxruntime.ai/).\n\n", "heading1": "What is the role of ONNX Runtime?", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite models. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU. To learn more read the [model card](https://github.com/onnx/models/tree/main/vision/classification/efficientnet-lite4)\n\nHere we walk through setting up a example demo for EfficientNet-Lite4 using Gradio\n\nFirst we import our dependencies and download and load the efficientnet-lite4 model from the onnx model zoo. Then load the labels from the labels_map.txt file. We then setup our preprocessing functions, load the model for inference, and setup the inference function. Finally, the inference function is wrapped into a gradio interface for a user to interact with. See the full code below.\n\n```python\nimport numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport cv2\nimport json\nimport gradio as gr\nfrom huggingface_hub import hf_hub_download\nfrom onnx import hub\nimport onnxruntime as ort\n\nloads ONNX model from ONNX Model Zoo\nmodel = hub.load(\"efficientnet-lite4\")\nloads the labels text file\nlabels = json.load(open(\"labels_map.txt\", \"r\"))\n\nsets image file dimensions to 224x224 by resizing and cropping image from center\ndef pre_process_edgetpu(img, dims):\n    output_height, output_width, _ = dims\n    img = resize_with_aspectratio(img, output_height, output_width, inter_pol=cv2.INTER_LINEAR)\n    img = center_crop(img, output_height, output_width)\n    img = np.asarray(img, dtype='float32')\n    converts jpg pixel value from [0 - 255] to float array [-1.0 - 1.0]\n    img -= [127.0, 127.0, 127.0]\n    img /= [128.0, 128.0, 128.0]\n    return img\n\nresizes the image with a proportional scale\ndef resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):\n    height, width, _ = img.shape\n    new_height = int(100. * out_he", "heading1": "Setting up a Gradio Demo for EfficientNet-Lite4", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "the image with a proportional scale\ndef resize_with_aspectratio(img, out_height, out_width, scale=87.5, inter_pol=cv2.INTER_LINEAR):\n    height, width, _ = img.shape\n    new_height = int(100. * out_height / scale)\n    new_width = int(100. * out_width / scale)\n    if height > width:\n        w = new_width\n        h = int(new_height * height / width)\n    else:\n        h = new_height\n        w = int(new_width * width / height)\n    img = cv2.resize(img, (w, h), interpolation=inter_pol)\n    return img\n\ncrops the image around the center based on given height and width\ndef center_crop(img, out_height, out_width):\n    height, width, _ = img.shape\n    left = int((width - out_width) / 2)\n    right = int((width + out_width) / 2)\n    top = int((height - out_height) / 2)\n    bottom = int((height + out_height) / 2)\n    img = img[top:bottom, left:right]\n    return img\n\n\nsess = ort.InferenceSession(model)\n\ndef inference(img):\n  img = cv2.imread(img)\n  img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n  img = pre_process_edgetpu(img, (224, 224, 3))\n\n  img_batch = np.expand_dims(img, axis=0)\n\n  results = sess.run([\"Softmax:0\"], {\"images:0\": img_batch})[0]\n  result = reversed(results[0].argsort()[-5:])\n  resultdic = {}\n  for r in result:\n      resultdic[labels[str(r)]] = float(results[0][r])\n  return resultdic\n\ntitle = \"EfficientNet-Lite4\"\ndescription = \"EfficientNet-Lite 4 is the largest variant and most accurate of the set of EfficientNet-Lite model. It is an integer-only quantized model that produces the highest accuracy of all of the EfficientNet models. It achieves 80.4% ImageNet top-1 accuracy, while still running in real-time (e.g. 30ms/image) on a Pixel 4 CPU.\"\nexamples = [['catonnx.jpg']]\ngr.Interface(inference, gr.Image(type=\"filepath\"), \"label\", title=title, description=description, examples=examples).launch()\n```\n\n", "heading1": "Setting up a Gradio Demo for EfficientNet-Lite4", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": " examples=examples).launch()\n```\n\n", "heading1": "Setting up a Gradio Demo for EfficientNet-Lite4", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "- Add model to the [onnx model zoo](https://github.com/onnx/models/blob/main/.github/PULL_REQUEST_TEMPLATE.md)\n- Create an account on Hugging Face [here](https://huggingface.co/join).\n- See list of models left to add to ONNX organization, please refer to the table with the [Models list](https://github.com/onnx/modelsmodels)\n- Add Gradio Demo under your username, see this [blog post](https://huggingface.co/blog/gradio-spaces) for setting up Gradio Demo on Hugging Face.\n- Request to join ONNX Organization [here](https://huggingface.co/onnx).\n- Once approved transfer model from your username to ONNX organization\n- Add a badge for model in model table, see examples in [Models list](https://github.com/onnx/modelsmodels)\n", "heading1": "How to contribute Gradio demos on HF spaces using ONNX models", "source_page_url": "https://gradio.app/guides/Gradio-and-ONNX-on-Hugging-Face", "source_page_title": "Other Tutorials - Gradio And Onnx On Hugging Face Guide"}, {"text": "3D models are becoming more popular in machine learning and make for some of the most fun demos to experiment with. Using `gradio`, you can easily build a demo of your 3D image model and share it with anyone. The Gradio 3D Model component accepts 3 file types including: _.obj_, _.glb_, & _.gltf_.\n\nThis guide will show you how to build a demo for your 3D image model in a few lines of code; like the one below. Play around with 3D object by clicking around, dragging and zooming:\n\n<gradio-app space=\"gradio/Model3D\"> </gradio-app>\n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](https://gradio.app/guides/quickstart).\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/how-to-use-3D-model-component", "source_page_title": "Other Tutorials - How To Use 3D Model Component Guide"}, {"text": "Let's take a look at how to create the minimal interface above. The prediction function in this case will just return the original 3D model mesh, but you can change this function to run inference on your machine learning model. We'll take a look at more complex examples below.\n\n```python\nimport gradio as gr\nimport os\n\n\ndef load_mesh(mesh_file_name):\n    return mesh_file_name\n\n\ndemo = gr.Interface(\n    fn=load_mesh,\n    inputs=gr.Model3D(),\n    outputs=gr.Model3D(\n            clear_color=[0.0, 0.0, 0.0, 0.0],  label=\"3D Model\"),\n    examples=[\n        [os.path.join(os.path.dirname(__file__), \"files/Bunny.obj\")],\n        [os.path.join(os.path.dirname(__file__), \"files/Duck.glb\")],\n        [os.path.join(os.path.dirname(__file__), \"files/Fox.gltf\")],\n        [os.path.join(os.path.dirname(__file__), \"files/face.obj\")],\n    ],\n)\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\nLet's break down the code above:\n\n`load_mesh`: This is our 'prediction' function and for simplicity, this function will take in the 3D model mesh and return it.\n\nCreating the Interface:\n\n- `fn`: the prediction function that is used when the user clicks submit. In our case this is the `load_mesh` function.\n- `inputs`: create a model3D input component. The input expects an uploaded file as a {str} filepath.\n- `outputs`: create a model3D output component. The output component also expects a file as a {str} filepath.\n  - `clear_color`: this is the background color of the 3D model canvas. Expects RGBa values.\n  - `label`: the label that appears on the top left of the component.\n- `examples`: list of 3D model files. The 3D model component can accept _.obj_, _.glb_, & _.gltf_ file types.\n- `cache_examples`: saves the predicted output for the examples, to save time on inference.\n\n", "heading1": "Taking a Look at the Code", "source_page_url": "https://gradio.app/guides/how-to-use-3D-model-component", "source_page_title": "Other Tutorials - How To Use 3D Model Component Guide"}, {"text": "Below is a demo that uses the DPT model to predict the depth of an image and then uses 3D Point Cloud to create a 3D object. Take a look at the [app.py](https://huggingface.co/spaces/gradio/dpt-depth-estimation-3d-obj/blob/main/app.py) file for a peek into the code and the model prediction function.\n<gradio-app space=\"gradio/dpt-depth-estimation-3d-obj\"> </gradio-app>\n\n---\n\nAnd you're done! That's all the code you need to build an interface for your Model3D model. Here are some references that you may find useful:\n\n- Gradio's [\"Getting Started\" guide](https://gradio.app/getting_started/)\n- The first [3D Model Demo](https://huggingface.co/spaces/gradio/Model3D) and [complete code](https://huggingface.co/spaces/gradio/Model3D/tree/main) (on Hugging Face Spaces)\n", "heading1": "Exploring a more complex Model3D Demo:", "source_page_url": "https://gradio.app/guides/how-to-use-3D-model-component", "source_page_title": "Other Tutorials - How To Use 3D Model Component Guide"}, {"text": "Building a dashboard from a public Google Sheet is very easy, thanks to the [`pandas` library](https://pandas.pydata.org/):\n\n1\\. Get the URL of the Google Sheets that you want to use. To do this, simply go to the Google Sheets, click on the \"Share\" button in the top-right corner, and then click on the \"Get shareable link\" button. This will give you a URL that looks something like this:\n\n```html\nhttps://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\n```\n\n2\\. Now, let's modify this URL and then use it to read the data from the Google Sheets into a Pandas DataFrame. (In the code below, replace the `URL` variable with the URL of your public Google Sheet):\n\n```python\nimport pandas as pd\n\nURL = \"https://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\"\ncsv_url = URL.replace('/editgid=', '/export?format=csv&gid=')\n\ndef get_data():\n    return pd.read_csv(csv_url)\n```\n\n3\\. The data query is a function, which means that it's easy to display it real-time using the `gr.DataFrame` component, or plot it real-time using the `gr.LinePlot` component (of course, depending on the data, a different plot may be appropriate). To do this, just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) you would like the component to refresh. Here's the Gradio code:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"\ud83d\udcc8 Real-Time Line Plot\")\n    with gr.Row():\n        with gr.Column():\n            gr.DataFrame(get_data, every=gr.Timer(5))\n        with gr.Column():\n            gr.LinePlot(get_data, every=gr.Timer(5), x=\"Date\", y=\"Sales\", y_title=\"Sales ($ millions)\", overlay_point=True, width=500, height=500)\n\ndemo.queue().launch()  Run the demo with queuing enabled\n```\n\nAnd that's it! You have a dashboard that refreshes every 5 seconds, pulling the data from your Google Sheet.\n\n", "heading1": "Public Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "For private Google Sheets, the process requires a little more work, but not that much! The key difference is that now, you must authenticate yourself to authorize access to the private Google Sheets.\n\nAuthentication\n\nTo authenticate yourself, obtain credentials from Google Cloud. Here's [how to set up google cloud credentials](https://developers.google.com/workspace/guides/create-credentials):\n\n1\\. First, log in to your Google Cloud account and go to the Google Cloud Console (https://console.cloud.google.com/)\n\n2\\. In the Cloud Console, click on the hamburger menu in the top-left corner and select \"APIs & Services\" from the menu. If you do not have an existing project, you will need to create one.\n\n3\\. Then, click the \"+ Enabled APIs & services\" button, which allows you to enable specific services for your project. Search for \"Google Sheets API\", click on it, and click the \"Enable\" button. If you see the \"Manage\" button, then Google Sheets is already enabled, and you're all set.\n\n4\\. In the APIs & Services menu, click on the \"Credentials\" tab and then click on the \"Create credentials\" button.\n\n5\\. In the \"Create credentials\" dialog, select \"Service account key\" as the type of credentials to create, and give it a name. **Note down the email of the service account**\n\n6\\. After selecting the service account, select the \"JSON\" key type and then click on the \"Create\" button. This will download the JSON key file containing your credentials to your computer. It will look something like this:\n\n```json\n{\n\t\"type\": \"service_account\",\n\t\"project_id\": \"your project\",\n\t\"private_key_id\": \"your private key id\",\n\t\"private_key\": \"private key\",\n\t\"client_email\": \"email\",\n\t\"client_id\": \"client id\",\n\t\"auth_uri\": \"https://accounts.google.com/o/oauth2/auth\",\n\t\"token_uri\": \"https://accounts.google.com/o/oauth2/token\",\n\t\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n\t\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/email_id\"\n}\n```\n\n", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "google.com/o/oauth2/token\",\n\t\"auth_provider_x509_cert_url\": \"https://www.googleapis.com/oauth2/v1/certs\",\n\t\"client_x509_cert_url\": \"https://www.googleapis.com/robot/v1/metadata/x509/email_id\"\n}\n```\n\nQuerying\n\nOnce you have the credentials `.json` file, you can use the following steps to query your Google Sheet:\n\n1\\. Click on the \"Share\" button in the top-right corner of the Google Sheet. Share the Google Sheets with the email address of the service from Step 5 of authentication subsection (this step is important!). Then click on the \"Get shareable link\" button. This will give you a URL that looks something like this:\n\n```html\nhttps://docs.google.com/spreadsheets/d/1UoKzzRzOCt-FXLLqDKLbryEKEgllGAQUEJ5qtmmQwpU/editgid=0\n```\n\n2\\. Install the [`gspread` library](https://docs.gspread.org/en/v5.7.0/), which makes it easy to work with the [Google Sheets API](https://developers.google.com/sheets/api/guides/concepts) in Python by running in the terminal: `pip install gspread`\n\n3\\. Write a function to load the data from the Google Sheet, like this (replace the `URL` variable with the URL of your private Google Sheet):\n\n```python\nimport gspread\nimport pandas as pd\n\nAuthenticate with Google and get the sheet\nURL = 'https://docs.google.com/spreadsheets/d/1_91Vps76SKOdDQ8cFxZQdgjTJiz23375sAT7vPvaj4k/editgid=0'\n\ngc = gspread.service_account(\"path/to/key.json\")\nsh = gc.open_by_url(URL)\nworksheet = sh.sheet1\n\ndef get_data():\n    values = worksheet.get_all_values()\n    df = pd.DataFrame(values[1:], columns=values[0])\n    return df\n\n```\n\n4\\. The data query is a function, which means that it's easy to display it real-time using the `gr.DataFrame` component, or plot it real-time using the `gr.LinePlot` component (of course, depending on the data, a different plot may be appropriate). To do this, we just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) we would like the component to refresh. Here's the Gradio cod", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": ". To do this, we just pass the function into the respective components, and set the `every` parameter based on how frequently (in seconds) we would like the component to refresh. Here's the Gradio code:\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as demo:\n    gr.Markdown(\"\ud83d\udcc8 Real-Time Line Plot\")\n    with gr.Row():\n        with gr.Column():\n            gr.DataFrame(get_data, every=gr.Timer(5))\n        with gr.Column():\n            gr.LinePlot(get_data, every=gr.Timer(5), x=\"Date\", y=\"Sales\", y_title=\"Sales ($ millions)\", overlay_point=True, width=500, height=500)\n\ndemo.queue().launch()  Run the demo with queuing enabled\n```\n\nYou now have a Dashboard that refreshes every 5 seconds, pulling the data from your Google Sheet.\n\n", "heading1": "Private Google Sheets", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "And that's all there is to it! With just a few lines of code, you can use `gradio` and other libraries to read data from a public or private Google Sheet and then display and plot the data in a real-time dashboard.\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/creating-a-realtime-dashboard-from-google-sheets", "source_page_title": "Other Tutorials - Creating A Realtime Dashboard From Google Sheets Guide"}, {"text": "In this Guide, we'll walk you through:\n\n- Introduction of Gradio, and Hugging Face Spaces, and Wandb\n- How to setup a Gradio demo using the Wandb integration for JoJoGAN\n- How to contribute your own Gradio demos after tracking your experiments on wandb to the Wandb organization on Hugging Face\n\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Weights and Biases (W&B) allows data scientists and machine learning scientists to track their machine learning experiments at every stage, from training to production. Any metric can be aggregated over samples and shown in panels in a customizable and searchable dashboard, like below:\n\n<img alt=\"Screen Shot 2022-08-01 at 5 54 59 PM\" src=\"https://user-images.githubusercontent.com/81195143/182252755-4a0e1ca8-fd25-40ff-8c91-c9da38aaa9ec.png\">\n\n", "heading1": "What is Wandb?", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Gradio\n\nGradio lets users demo their machine learning models as a web app, all in a few lines of Python. Gradio wraps any Python function (such as a machine learning model's inference function) into a user interface and the demos can be launched inside jupyter notebooks, colab notebooks, as well as embedded in your own website and hosted on Hugging Face Spaces for free.\n\nGet started [here](https://gradio.app/getting_started)\n\nHugging Face Spaces\n\nHugging Face Spaces is a free hosting option for Gradio demos. Spaces comes with 3 SDK options: Gradio, Streamlit and Static HTML demos. Spaces can be public or private and the workflow is similar to github repos. There are over 2000+ spaces currently on Hugging Face. Learn more about spaces [here](https://huggingface.co/spaces/launch).\n\n", "heading1": "What are Hugging Face Spaces & Gradio?", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "Now, let's walk you through how to do this on your own. We'll make the assumption that you're new to W&B and Gradio for the purposes of this tutorial.\n\nLet's get started!\n\n1. Create a W&B account\n\n   Follow [these quick instructions](https://app.wandb.ai/login) to create your free account if you don\u2019t have one already. It shouldn't take more than a couple minutes. Once you're done (or if you've already got an account), next, we'll run a quick colab.\n\n2. Open Colab Install Gradio and W&B\n\n   We'll be following along with the colab provided in the JoJoGAN repo with some minor modifications to use Wandb and Gradio more effectively.\n\n   [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/mchong6/JoJoGAN/blob/main/stylize.ipynb)\n\n   Install Gradio and Wandb at the top:\n\n   ```sh\n   pip install gradio wandb\n   ```\n\n3. Finetune StyleGAN and W&B experiment tracking\n\n   This next step will open a W&B dashboard to track your experiments and a gradio panel showing pretrained models to choose from a drop down menu from a Gradio Demo hosted on Huggingface Spaces. Here's the code you need for that:\n\n   ```python\n   alpha =  1.0\n   alpha = 1-alpha\n\n   preserve_color = True\n   num_iter = 100\n   log_interval = 50\n\n   samples = []\n   column_names = [\"Reference (y)\", \"Style Code(w)\", \"Real Face Image(x)\"]\n\n   wandb.init(project=\"JoJoGAN\")\n   config = wandb.config\n   config.num_iter = num_iter\n   config.preserve_color = preserve_color\n   wandb.log(\n   {\"Style reference\": [wandb.Image(transforms.ToPILImage()(target_im))]},\n   step=0)\n\n   load discriminator for perceptual loss\n   discriminator = Discriminator(1024, 2).eval().to(device)\n   ckpt = torch.load('models/stylegan2-ffhq-config-f.pt', map_location=lambda storage, loc: storage)\n   discriminator.load_state_dict(ckpt[\"d\"], strict=False)\n\n   reset generator\n   del generator\n   generator = deepcopy(original_generator)\n\n   g_optim = optim.Adam(generator.parameters(),", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": ": storage)\n   discriminator.load_state_dict(ckpt[\"d\"], strict=False)\n\n   reset generator\n   del generator\n   generator = deepcopy(original_generator)\n\n   g_optim = optim.Adam(generator.parameters(), lr=2e-3, betas=(0, 0.99))\n\n   Which layers to swap for generating a family of plausible real images -> fake image\n   if preserve_color:\n       id_swap = [9,11,15,16,17]\n   else:\n       id_swap = list(range(7, generator.n_latent))\n\n   for idx in tqdm(range(num_iter)):\n       mean_w = generator.get_latent(torch.randn([latents.size(0), latent_dim]).to(device)).unsqueeze(1).repeat(1, generator.n_latent, 1)\n       in_latent = latents.clone()\n       in_latent[:, id_swap] = alpha*latents[:, id_swap] + (1-alpha)*mean_w[:, id_swap]\n\n       img = generator(in_latent, input_is_latent=True)\n\n       with torch.no_grad():\n           real_feat = discriminator(targets)\n       fake_feat = discriminator(img)\n\n       loss = sum([F.l1_loss(a, b) for a, b in zip(fake_feat, real_feat)])/len(fake_feat)\n\n       wandb.log({\"loss\": loss}, step=idx)\n       if idx % log_interval == 0:\n           generator.eval()\n           my_sample = generator(my_w, input_is_latent=True)\n           generator.train()\n           my_sample = transforms.ToPILImage()(utils.make_grid(my_sample, normalize=True, range=(-1, 1)))\n           wandb.log(\n           {\"Current stylization\": [wandb.Image(my_sample)]},\n           step=idx)\n       table_data = [\n               wandb.Image(transforms.ToPILImage()(target_im)),\n               wandb.Image(img),\n               wandb.Image(my_sample),\n           ]\n       samples.append(table_data)\n\n       g_optim.zero_grad()\n       loss.backward()\n       g_optim.step()\n\n   out_table = wandb.Table(data=samples, columns=column_names)\n   wandb.log({\"Current Samples\": out_table})\n   ```\n4. Save, Download, and Load Model\n\n    Here's how to save and download your model.\n\n   ```python\n   from PIL import Image\n   import torch\n   torch.backends.cudnn.benchmark = True\n   from torchvision impor", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "ave, Download, and Load Model\n\n    Here's how to save and download your model.\n\n   ```python\n   from PIL import Image\n   import torch\n   torch.backends.cudnn.benchmark = True\n   from torchvision import transforms, utils\n   from util import *\n   import math\n   import random\n   import numpy as np\n   from torch import nn, autograd, optim\n   from torch.nn import functional as F\n   from tqdm import tqdm\n   import lpips\n   from model import *\n   from e4e_projection import projection as e4e_projection\n   \n   from copy import deepcopy\n   import imageio\n   \n   import os\n   import sys\n   import torchvision.transforms as transforms\n   from argparse import Namespace\n   from e4e.models.psp import pSp\n   from util import *\n   from huggingface_hub import hf_hub_download\n   from google.colab import files\n   \n   torch.save({\"g\": generator.state_dict()}, \"your-model-name.pt\")\n   \n   files.download('your-model-name.pt')\n   \n   latent_dim = 512\n   device=\"cuda\"\n   model_path_s = hf_hub_download(repo_id=\"akhaliq/jojogan-stylegan2-ffhq-config-f\", filename=\"stylegan2-ffhq-config-f.pt\")\n   original_generator = Generator(1024, latent_dim, 8, 2).to(device)\n   ckpt = torch.load(model_path_s, map_location=lambda storage, loc: storage)\n   original_generator.load_state_dict(ckpt[\"g_ema\"], strict=False)\n   mean_latent = original_generator.mean_latent(10000)\n   \n   generator = deepcopy(original_generator)\n   \n   ckpt = torch.load(\"/content/JoJoGAN/your-model-name.pt\", map_location=lambda storage, loc: storage)\n   generator.load_state_dict(ckpt[\"g\"], strict=False)\n   generator.eval()\n   \n   plt.rcParams['figure.dpi'] = 150\n   \n   transform = transforms.Compose(\n       [\n           transforms.Resize((1024, 1024)),\n           transforms.ToTensor(),\n           transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n       ]\n   )\n   \n   def inference(img):\n       img.save('out.jpg')\n       aligned_face = align_face('out.jpg')\n   \n       my_w = e4e_projection(aligned_face, \"out.pt\", device).unsqueeze(0)", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": ".5, 0.5)),\n       ]\n   )\n   \n   def inference(img):\n       img.save('out.jpg')\n       aligned_face = align_face('out.jpg')\n   \n       my_w = e4e_projection(aligned_face, \"out.pt\", device).unsqueeze(0)\n       with torch.no_grad():\n           my_sample = generator(my_w, input_is_latent=True)\n   \n       npimage = my_sample[0].cpu().permute(1, 2, 0).detach().numpy()\n       imageio.imwrite('filename.jpeg', npimage)\n       return 'filename.jpeg'\n   ````\n\n5. Build a Gradio Demo\n\n   ```python\n   import gradio as gr\n   \n   title = \"JoJoGAN\"\n   description = \"Gradio Demo for JoJoGAN: One Shot Face Stylization. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below.\"\n   \n   demo = gr.Interface(\n       inference,\n       gr.Image(type=\"pil\"),\n       gr.Image(type=\"file\"),\n       title=title,\n       description=description\n   )\n   \n   demo.launch(share=True)\n   ```\n\n6. Integrate Gradio into your W&B Dashboard\n\n   The last step\u2014integrating your Gradio demo with your W&B dashboard\u2014is just one extra line:\n\n   ```python\n   demo.integrate(wandb=wandb)\n   ```\n\n   Once you call integrate, a demo will be created and you can integrate it into your dashboard or report.\n\n   Outside of W&B with Web components, using the `gradio-app` tags, anyone can embed Gradio demos on HF spaces directly into their blogs, websites, documentation, etc.:\n   \n   ```html\n   <gradio-app space=\"akhaliq/JoJoGAN\"> </gradio-app>\n   ```\n\n7. (Optional) Embed W&B plots in your Gradio App\n\n   It's also possible to embed W&B plots within Gradio apps. To do so, you can create a W&B Report of your plots and\n   embed them within your Gradio app within a `gr.HTML` block.\n\n   The Report will need to be public and you will need to wrap the URL within an iFrame like this:\n\n   ```python\n   import gradio as gr\n   \n   def wandb_report(url):\n       iframe = f'<iframe src={url} style=\"border:none;height:1024px;width:100%\">'\n       return gr.HTML(iframe)\n   \n   with gr.Blocks() a", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "``python\n   import gradio as gr\n   \n   def wandb_report(url):\n       iframe = f'<iframe src={url} style=\"border:none;height:1024px;width:100%\">'\n       return gr.HTML(iframe)\n   \n   with gr.Blocks() as demo:\n       report_url = 'https://wandb.ai/_scott/pytorch-sweeps-demo/reports/loss-22-10-07-16-00-17---VmlldzoyNzU2NzAx'\n       report = wandb_report(report_url)\n   \n   demo.launch(share=True)\n   ```\n\n", "heading1": "Setting up a Gradio Demo for JoJoGAN", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "We hope you enjoyed this brief demo of embedding a Gradio demo to a W&B report! Thanks for making it to the end. To recap:\n\n- Only one single reference image is needed for fine-tuning JoJoGAN which usually takes about 1 minute on a GPU in colab. After training, style can be applied to any input image. Read more in the paper.\n\n- W&B tracks experiments with just a few lines of code added to a colab and you can visualize, sort, and understand your experiments in a single, centralized dashboard.\n\n- Gradio, meanwhile, demos the model in a user friendly interface to share anywhere on the web.\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "- Create an account on Hugging Face [here](https://huggingface.co/join).\n- Add Gradio Demo under your username, see this [course](https://huggingface.co/course/chapter9/4?fw=pt) for setting up Gradio Demo on Hugging Face.\n- Request to join wandb organization [here](https://huggingface.co/wandb).\n- Once approved transfer model from your username to Wandb organization\n", "heading1": "How to contribute Gradio demos on HF spaces on the Wandb organization", "source_page_url": "https://gradio.app/guides/Gradio-and-Wandb-Integration", "source_page_title": "Other Tutorials - Gradio And Wandb Integration Guide"}, {"text": "In this guide we will demonstrate some of the ways you can use Gradio with Comet. We will cover the basics of using Comet with Gradio and show you some of the ways that you can leverage Gradio's advanced features such as [Embedding with iFrames](https://www.gradio.app/guides/sharing-your-app/embedding-with-iframes) and [State](https://www.gradio.app/docs/state) to build some amazing model evaluation workflows.\n\nHere is a list of the topics covered in this guide.\n\n1. Logging Gradio UI's to your Comet Experiments\n2. Embedding Gradio Applications directly into your Comet Projects\n3. Embedding Hugging Face Spaces directly into your Comet Projects\n4. Logging Model Inferences from your Gradio Application to Comet\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "[Comet](https://www.comet.com?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs) is an MLOps Platform that is designed to help Data Scientists and Teams build better models faster! Comet provides tooling to Track, Explain, Manage, and Monitor your models in a single place! It works with Jupyter Notebooks and Scripts and most importantly it's 100% free!\n\n", "heading1": "What is Comet?", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "First, install the dependencies needed to run these examples\n\n```shell\npip install comet_ml torch torchvision transformers gradio shap requests Pillow\n```\n\nNext, you will need to [sign up for a Comet Account](https://www.comet.com/signup?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs). Once you have your account set up, [grab your API Key](https://www.comet.com/docs/v2/guides/getting-started/quickstart/get-an-api-key?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs) and configure your Comet credentials\n\nIf you're running these examples as a script, you can either export your credentials as environment variables\n\n```shell\nexport COMET_API_KEY=\"<Your API Key>\"\nexport COMET_WORKSPACE=\"<Your Workspace Name>\"\nexport COMET_PROJECT_NAME=\"<Your Project Name>\"\n```\n\nor set them in a `.comet.config` file in your working directory. You file should be formatted in the following way.\n\n```shell\n[comet]\napi_key=<Your API Key>\nworkspace=<Your Workspace Name>\nproject_name=<Your Project Name>\n```\n\nIf you are using the provided Colab Notebooks to run these examples, please run the cell with the following snippet before starting the Gradio UI. Running this cell allows you to interactively add your API key to the notebook.\n\n```python\nimport comet_ml\ncomet_ml.init()\n```\n\n", "heading1": "Setup", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-evaluation/gradio/notebooks/Gradio_and_Comet.ipynb)\n\nIn this example, we will go over how to log your Gradio Applications to Comet and interact with them using the Gradio Custom Panel.\n\nLet's start by building a simple Image Classification example using `resnet18`.\n\n```python\nimport comet_ml\n\nimport requests\nimport torch\nfrom PIL import Image\nfrom torchvision import transforms\n\ntorch.hub.download_url_to_file(\"https://github.com/pytorch/hub/raw/master/images/dog.jpg\", \"dog.jpg\")\n\nif torch.cuda.is_available():\n    device = \"cuda\"\nelse:\n    device = \"cpu\"\n\nmodel = torch.hub.load(\"pytorch/vision:v0.6.0\", \"resnet18\", pretrained=True).eval()\nmodel = model.to(device)\n\nDownload human-readable labels for ImageNet.\nresponse = requests.get(\"https://git.io/JJkYN\")\nlabels = response.text.split(\"\\n\")\n\n\ndef predict(inp):\n    inp = Image.fromarray(inp.astype(\"uint8\"), \"RGB\")\n    inp = transforms.ToTensor()(inp).unsqueeze(0)\n    with torch.no_grad():\n        prediction = torch.nn.functional.softmax(model(inp.to(device))[0], dim=0)\n    return {labels[i]: float(prediction[i]) for i in range(1000)}\n\n\ninputs = gr.Image()\noutputs = gr.Label(num_top_classes=3)\n\nio = gr.Interface(\n    fn=predict, inputs=inputs, outputs=outputs, examples=[\"dog.jpg\"]\n)\nio.launch(inline=False, share=True)\n\nexperiment = comet_ml.Experiment()\nexperiment.add_tag(\"image-classifier\")\n\nio.integrate(comet_ml=experiment)\n```\n\nThe last line in this snippet will log the URL of the Gradio Application to your Comet Experiment. You can find the URL in the Text Tab of your Experiment.\n\n<video width=\"560\" height=\"315\" controls>\n    <source src=\"https://user-images.githubusercontent.com/7529846/214328034-09369d4d-8b94-4c4a-aa3c-25e3ed8394c4.mp4\"></source>\n</video>\n\nAdd the Gradio Panel to your Experiment to interact with your application.\n\n<video width=", "heading1": "1. Logging Gradio UI's to your Comet Experiments", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "r-images.githubusercontent.com/7529846/214328034-09369d4d-8b94-4c4a-aa3c-25e3ed8394c4.mp4\"></source>\n</video>\n\nAdd the Gradio Panel to your Experiment to interact with your application.\n\n<video width=\"560\" height=\"315\" controls>\n    <source src=\"https://user-images.githubusercontent.com/7529846/214328194-95987f83-c180-4929-9bed-c8a0d3563ed7.mp4\"></source>\n</video>\n\n", "heading1": "1. Logging Gradio UI's to your Comet Experiments", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/KZnpH7msPq0?start=9\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\nIf you are permanently hosting your Gradio application, you can embed the UI using the Gradio Panel Extended custom Panel.\n\nGo to your Comet Project page, and head over to the Panels tab. Click the `+ Add` button to bring up the Panels search page.\n\n<img width=\"560\" alt=\"adding-panels\" src=\"https://user-images.githubusercontent.com/7529846/214329314-70a3ff3d-27fb-408c-a4d1-4b58892a3854.jpeg\">\n\nNext, search for Gradio Panel Extended in the Public Panels section and click `Add`.\n\n<img width=\"560\" alt=\"gradio-panel-extended\" src=\"https://user-images.githubusercontent.com/7529846/214325577-43226119-0292-46be-a62a-0c7a80646ebb.png\">\n\nOnce you have added your Panel, click `Edit` to access to the Panel Options page and paste in the URL of your Gradio application.\n\n![Edit-Gradio-Panel-Options](https://user-images.githubusercontent.com/7529846/214573001-23814b5a-ca65-4ace-a8a5-b27cdda70f7a.gif)\n\n<img width=\"560\" alt=\"Edit-Gradio-Panel-URL\" src=\"https://user-images.githubusercontent.com/7529846/214334843-870fe726-0aa1-4b21-bbc6-0c48f56c48d8.png\">\n\n", "heading1": "2. Embedding Gradio Applications directly into your Comet Projects", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/KZnpH7msPq0?start=107\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\nYou can also embed Gradio Applications that are hosted on Hugging Faces Spaces into your Comet Projects using the Hugging Face Spaces Panel.\n\nGo to your Comet Project page, and head over to the Panels tab. Click the `+ Add` button to bring up the Panels search page. Next, search for the Hugging Face Spaces Panel in the Public Panels section and click `Add`.\n\n<img width=\"560\" height=\"315\" alt=\"huggingface-spaces-panel\" src=\"https://user-images.githubusercontent.com/7529846/214325606-99aa3af3-b284-4026-b423-d3d238797e12.png\">\n\nOnce you have added your Panel, click Edit to access to the Panel Options page and paste in the path of your Hugging Face Space e.g. `pytorch/ResNet`\n\n<img width=\"560\" height=\"315\" alt=\"Edit-HF-Space\" src=\"https://user-images.githubusercontent.com/7529846/214335868-c6f25dee-13db-4388-bcf5-65194f850b02.png\">\n\n", "heading1": "3. Embedding Hugging Face Spaces directly into your Comet Projects", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/KZnpH7msPq0?start=176\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-evaluation/gradio/notebooks/Logging_Model_Inferences_with_Comet_and_Gradio.ipynb)\n\nIn the previous examples, we demonstrated the various ways in which you can interact with a Gradio application through the Comet UI. Additionally, you can also log model inferences, such as SHAP plots, from your Gradio application to Comet.\n\nIn the following snippet, we're going to log inferences from a Text Generation model. We can persist an Experiment across multiple inference calls using Gradio's [State](https://www.gradio.app/docs/state) object. This will allow you to log multiple inferences from a model to a single Experiment.\n\n```python\nimport comet_ml\nimport gradio as gr\nimport shap\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nif torch.cuda.is_available():\n    device = \"cuda\"\nelse:\n    device = \"cpu\"\n\nMODEL_NAME = \"gpt2\"\n\nmodel = AutoModelForCausalLM.from_pretrained(MODEL_NAME)\n\nset model decoder to true\nmodel.config.is_decoder = True\nset text-generation params under task_specific_params\nmodel.config.task_specific_params[\"text-generation\"] = {\n    \"do_sample\": True,\n    \"max_length\": 50,\n    \"temperature\": 0.7,\n    \"top_k\": 50,\n    \"no_repeat_ngram_size\": 2,\n}\nmodel = model.to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\nexplainer = shap.Explainer(model, tokenizer)\n\n\ndef start_experiment():\n    \"\"\"Returns an APIExperiment object that is thread safe\n    and can be used to log inferences to a single Experiment\n    \"\"\"\n    try:\n        api = comet_ml.API()\n        workspace = api.get_default_", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "  \"\"\"Returns an APIExperiment object that is thread safe\n    and can be used to log inferences to a single Experiment\n    \"\"\"\n    try:\n        api = comet_ml.API()\n        workspace = api.get_default_workspace()\n        project_name = comet_ml.config.get_config()[\"comet.project_name\"]\n\n        experiment = comet_ml.APIExperiment(\n            workspace=workspace, project_name=project_name\n        )\n        experiment.log_other(\"Created from\", \"gradio-inference\")\n\n        message = f\"Started Experiment: [{experiment.name}]({experiment.url})\"\n\n        return (experiment, message)\n\n    except Exception as e:\n        return None, None\n\n\ndef predict(text, state, message):\n    experiment = state\n\n    shap_values = explainer([text])\n    plot = shap.plots.text(shap_values, display=False)\n\n    if experiment is not None:\n        experiment.log_other(\"message\", message)\n        experiment.log_html(plot)\n\n    return plot\n\n\nwith gr.Blocks() as demo:\n    start_experiment_btn = gr.Button(\"Start New Experiment\")\n    experiment_status = gr.Markdown()\n\n    Log a message to the Experiment to provide more context\n    experiment_message = gr.Textbox(label=\"Experiment Message\")\n    experiment = gr.State()\n\n    input_text = gr.Textbox(label=\"Input Text\", lines=5, interactive=True)\n    submit_btn = gr.Button(\"Submit\")\n\n    output = gr.HTML(interactive=True)\n\n    start_experiment_btn.click(\n        start_experiment, outputs=[experiment, experiment_status]\n    )\n    submit_btn.click(\n        predict, inputs=[input_text, experiment, experiment_message], outputs=[output]\n    )\n```\n\nInferences from this snippet will be saved in the HTML tab of your experiment.\n\n<video width=\"560\" height=\"315\" controls>\n    <source src=\"https://user-images.githubusercontent.com/7529846/214328610-466e5c81-4814-49b9-887c-065aca14dd30.mp4\"></source>\n</video>\n\n", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "887c-065aca14dd30.mp4\"></source>\n</video>\n\n", "heading1": "4. Logging Model Inferences to Comet", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "We hope you found this guide useful and that it provides some inspiration to help you build awesome model evaluation workflows with Comet and Gradio.\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "- Create an account on Hugging Face [here](https://huggingface.co/join).\n- Add Gradio Demo under your username, see this [course](https://huggingface.co/course/chapter9/4?fw=pt) for setting up Gradio Demo on Hugging Face.\n- Request to join the Comet organization [here](https://huggingface.co/Comet).\n\n", "heading1": "How to contribute Gradio demos on HF spaces on the Comet organization", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "- [Comet Documentation](https://www.comet.com/docs/v2/?utm_source=gradio&utm_medium=referral&utm_campaign=gradio-integration&utm_content=gradio-docs)\n", "heading1": "Additional Resources", "source_page_url": "https://gradio.app/guides/Gradio-and-Comet", "source_page_title": "Other Tutorials - Gradio And Comet Guide"}, {"text": "Gradio features a built-in theming engine that lets you customize the look and feel of your app. You can choose from a variety of themes, or create your own. To do so, pass the `theme=` kwarg to the `Blocks` or `Interface` constructor. For example:\n\n```python\nwith gr.Blocks(theme=gr.themes.Soft()) as demo:\n    ...\n```\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-soft.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nGradio comes with a set of prebuilt themes which you can load from `gr.themes.*`. These are:\n\n\n* `gr.themes.Base()` - the `\"base\"` theme sets the primary color to blue but otherwise has minimal styling, making it particularly useful as a base for creating new, custom themes.\n* `gr.themes.Default()` - the `\"default\"` Gradio 5 theme, with a vibrant orange primary color and gray secondary color.\n* `gr.themes.Origin()` - the `\"origin\"` theme is most similar to Gradio 4 styling. Colors, especially in light mode, are more subdued than the Gradio 5 default theme.\n* `gr.themes.Citrus()` - the `\"citrus\"` theme uses a yellow primary color, highlights form elements that are in focus, and includes fun 3D effects when buttons are clicked.\n* `gr.themes.Monochrome()` - the `\"monochrome\"` theme uses a black primary and white secondary color, and uses serif-style fonts, giving the appearance of a black-and-white newspaper. \n* `gr.themes.Soft()` - the `\"soft\"` theme uses a purple primary color and white secondary color. It also increases the border radius around buttons and form elements and highlights labels.\n* `gr.themes.Glass()` - the `\"glass\"` theme has a blue primary color and a transclucent gray secondary color. The theme also uses vertical gradients to create a glassy effect.\n* `gr.themes.Ocean()` - the `\"ocean\"` theme has a blue-green primary color and gray secondary color. The theme also uses horizontal gradients, especially for buttons and some form elements.\n\n\nEach of these themes set values for hundreds of CSS variables. You can use preb", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "lor and gray secondary color. The theme also uses horizontal gradients, especially for buttons and some form elements.\n\n\nEach of these themes set values for hundreds of CSS variables. You can use prebuilt themes as a starting point for your own custom themes, or you can create your own themes from scratch. Let's take a look at each approach.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "The easiest way to build a theme is using the Theme Builder. To launch the Theme Builder locally, run the following code:\n\n```python\nimport gradio as gr\n\ngr.themes.builder()\n```\n\n$demo_theme_builder\n\nYou can use the Theme Builder running on Spaces above, though it runs much faster when you launch it locally via `gr.themes.builder()`.\n\nAs you edit the values in the Theme Builder, the app will preview updates in real time. You can download the code to generate the theme you've created so you can use it in any Gradio app.\n\nIn the rest of the guide, we will cover building themes programmatically.\n\n", "heading1": "Using the Theme Builder", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Although each theme has hundreds of CSS variables, the values for most these variables are drawn from 8 core variables which can be set through the constructor of each prebuilt theme. Modifying these 8 arguments allows you to quickly change the look and feel of your app.\n\nCore Colors\n\nThe first 3 constructor arguments set the colors of the theme and are `gradio.themes.Color` objects. Internally, these Color objects hold brightness values for the palette of a single hue, ranging from 50, 100, 200..., 800, 900, 950. Other CSS variables are derived from these 3 colors.\n\nThe 3 color constructor arguments are:\n\n- `primary_hue`: This is the color draws attention in your theme. In the default theme, this is set to `gradio.themes.colors.orange`.\n- `secondary_hue`: This is the color that is used for secondary elements in your theme. In the default theme, this is set to `gradio.themes.colors.blue`.\n- `neutral_hue`: This is the color that is used for text and other neutral elements in your theme. In the default theme, this is set to `gradio.themes.colors.gray`.\n\nYou could modify these values using their string shortcuts, such as\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(primary_hue=\"red\", secondary_hue=\"pink\")) as demo:\n    ...\n```\n\nor you could use the `Color` objects directly, like this:\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(primary_hue=gr.themes.colors.red, secondary_hue=gr.themes.colors.pink)) as demo:\n    ...\n```\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-extended-step-1.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nPredefined colors are:\n\n- `slate`\n- `gray`\n- `zinc`\n- `neutral`\n- `stone`\n- `red`\n- `orange`\n- `amber`\n- `yellow`\n- `lime`\n- `green`\n- `emerald`\n- `teal`\n- `cyan`\n- `sky`\n- `blue`\n- `indigo`\n- `violet`\n- `purple`\n- `fuchsia`\n- `pink`\n- `rose`\n\nYou could also create your own custom `Color` objects and pass them in.\n\nCore Sizing\n\nThe next 3 constructor arguments set the sizing of the theme and are `gradio.", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "`\n- `fuchsia`\n- `pink`\n- `rose`\n\nYou could also create your own custom `Color` objects and pass them in.\n\nCore Sizing\n\nThe next 3 constructor arguments set the sizing of the theme and are `gradio.themes.Size` objects. Internally, these Size objects hold pixel size values that range from `xxs` to `xxl`. Other CSS variables are derived from these 3 sizes.\n\n- `spacing_size`: This sets the padding within and spacing between elements. In the default theme, this is set to `gradio.themes.sizes.spacing_md`.\n- `radius_size`: This sets the roundedness of corners of elements. In the default theme, this is set to `gradio.themes.sizes.radius_md`.\n- `text_size`: This sets the font size of text. In the default theme, this is set to `gradio.themes.sizes.text_md`.\n\nYou could modify these values using their string shortcuts, such as\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(spacing_size=\"sm\", radius_size=\"none\")) as demo:\n    ...\n```\n\nor you could use the `Size` objects directly, like this:\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(spacing_size=gr.themes.sizes.spacing_sm, radius_size=gr.themes.sizes.radius_none)) as demo:\n    ...\n```\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-extended-step-2.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nThe predefined size objects are:\n\n- `radius_none`\n- `radius_sm`\n- `radius_md`\n- `radius_lg`\n- `spacing_sm`\n- `spacing_md`\n- `spacing_lg`\n- `text_sm`\n- `text_md`\n- `text_lg`\n\nYou could also create your own custom `Size` objects and pass them in.\n\nCore Fonts\n\nThe final 2 constructor arguments set the fonts of the theme. You can pass a list of fonts to each of these arguments to specify fallbacks. If you provide a string, it will be loaded as a system font. If you provide a `gradio.themes.GoogleFont`, the font will be loaded from Google Fonts.\n\n- `font`: This sets the primary font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Sans\")`.\n- `font_mono`: This sets th", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "font will be loaded from Google Fonts.\n\n- `font`: This sets the primary font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Sans\")`.\n- `font_mono`: This sets the monospace font of the theme. In the default theme, this is set to `gradio.themes.GoogleFont(\"IBM Plex Mono\")`.\n\nYou could modify these values such as the following:\n\n```python\nwith gr.Blocks(theme=gr.themes.Default(font=[gr.themes.GoogleFont(\"Inconsolata\"), \"Arial\", \"sans-serif\"])) as demo:\n    ...\n```\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-extended-step-3.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\n", "heading1": "Extending Themes via the Constructor", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "You can also modify the values of CSS variables after the theme has been loaded. To do so, use the `.set()` method of the theme object to get access to the CSS variables. For example:\n\n```python\ntheme = gr.themes.Default(primary_hue=\"blue\").set(\n    loader_color=\"FF0000\",\n    slider_color=\"FF0000\",\n)\n\nwith gr.Blocks(theme=theme) as demo:\n    ...\n```\n\nIn the example above, we've set the `loader_color` and `slider_color` variables to `FF0000`, despite the overall `primary_color` using the blue color palette. You can set any CSS variable that is defined in the theme in this manner.\n\nYour IDE type hinting should help you navigate these variables. Since there are so many CSS variables, let's take a look at how these variables are named and organized.\n\nCSS Variable Naming Conventions\n\nCSS variable names can get quite long, like `button_primary_background_fill_hover_dark`! However they follow a common naming convention that makes it easy to understand what they do and to find the variable you're looking for. Separated by underscores, the variable name is made up of:\n\n1. The target element, such as `button`, `slider`, or `block`.\n2. The target element type or sub-element, such as `button_primary`, or `block_label`.\n3. The property, such as `button_primary_background_fill`, or `block_label_border_width`.\n4. Any relevant state, such as `button_primary_background_fill_hover`.\n5. If the value is different in dark mode, the suffix `_dark`. For example, `input_border_color_focus_dark`.\n\nOf course, many CSS variable names are shorter than this, such as `table_border_color`, or `input_shadow`.\n\nCSS Variable Organization\n\nThough there are hundreds of CSS variables, they do not all have to have individual values. They draw their values by referencing a set of core variables and referencing each other. This allows us to only have to modify a few variables to change the look and feel of the entire theme, while also getting finer control of individual elements that we may wan", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "d referencing each other. This allows us to only have to modify a few variables to change the look and feel of the entire theme, while also getting finer control of individual elements that we may want to modify.\n\nReferencing Core Variables\n\nTo reference one of the core constructor variables, precede the variable name with an asterisk. To reference a core color, use the `*primary_`, `*secondary_`, or `*neutral_` prefix, followed by the brightness value. For example:\n\n```python\ntheme = gr.themes.Default(primary_hue=\"blue\").set(\n    button_primary_background_fill=\"*primary_200\",\n    button_primary_background_fill_hover=\"*primary_300\",\n)\n```\n\nIn the example above, we've set the `button_primary_background_fill` and `button_primary_background_fill_hover` variables to `*primary_200` and `*primary_300`. These variables will be set to the 200 and 300 brightness values of the blue primary color palette, respectively.\n\nSimilarly, to reference a core size, use the `*spacing_`, `*radius_`, or `*text_` prefix, followed by the size value. For example:\n\n```python\ntheme = gr.themes.Default(radius_size=\"md\").set(\n    button_primary_border_radius=\"*radius_xl\",\n)\n```\n\nIn the example above, we've set the `button_primary_border_radius` variable to `*radius_xl`. This variable will be set to the `xl` setting of the medium radius size range.\n\nReferencing Other Variables\n\nVariables can also reference each other. For example, look at the example below:\n\n```python\ntheme = gr.themes.Default().set(\n    button_primary_background_fill=\"FF0000\",\n    button_primary_background_fill_hover=\"FF0000\",\n    button_primary_border=\"FF0000\",\n)\n```\n\nHaving to set these values to a common color is a bit tedious. Instead, we can reference the `button_primary_background_fill` variable in the `button_primary_background_fill_hover` and `button_primary_border` variables, using a `*` prefix.\n\n```python\ntheme = gr.themes.Default().set(\n    button_primary_background_fill=\"FF0000\",\n    button_primary_back", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "mary_background_fill_hover` and `button_primary_border` variables, using a `*` prefix.\n\n```python\ntheme = gr.themes.Default().set(\n    button_primary_background_fill=\"FF0000\",\n    button_primary_background_fill_hover=\"*button_primary_background_fill\",\n    button_primary_border=\"*button_primary_background_fill\",\n)\n```\n\nNow, if we change the `button_primary_background_fill` variable, the `button_primary_background_fill_hover` and `button_primary_border` variables will automatically update as well.\n\nThis is particularly useful if you intend to share your theme - it makes it easy to modify the theme without having to change every variable.\n\nNote that dark mode variables automatically reference each other. For example:\n\n```python\ntheme = gr.themes.Default().set(\n    button_primary_background_fill=\"FF0000\",\n    button_primary_background_fill_dark=\"AAAAAA\",\n    button_primary_border=\"*button_primary_background_fill\",\n    button_primary_border_dark=\"*button_primary_background_fill_dark\",\n)\n```\n\n`button_primary_border_dark` will draw its value from `button_primary_background_fill_dark`, because dark mode always draw from the dark version of the variable.\n\n", "heading1": "Extending Themes via `.set()`", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Let's say you want to create a theme from scratch! We'll go through it step by step - you can also see the source of prebuilt themes in the gradio source repo for reference - [here's the source](https://github.com/gradio-app/gradio/blob/main/gradio/themes/monochrome.py) for the Monochrome theme.\n\nOur new theme class will inherit from `gradio.themes.Base`, a theme that sets a lot of convenient defaults. Let's make a simple demo that creates a dummy theme called Seafoam, and make a simple app that uses it.\n\n$code_theme_new_step_1\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-new-step-1.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nThe Base theme is very barebones, and uses `gr.themes.Blue` as it primary color - you'll note the primary button and the loading animation are both blue as a result. Let's change the defaults core arguments of our app. We'll overwrite the constructor and pass new defaults for the core constructor arguments.\n\nWe'll use `gr.themes.Emerald` as our primary color, and set secondary and neutral hues to `gr.themes.Blue`. We'll make our text larger using `text_lg`. We'll use `Quicksand` as our default font, loaded from Google Fonts.\n\n$code_theme_new_step_2\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-new-step-2.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nSee how the primary button and the loading animation are now green? These CSS variables are tied to the `primary_hue` variable.\n\nLet's modify the theme a bit more directly. We'll call the `set()` method to overwrite CSS variable values explicitly. We can use any CSS logic, and reference our core constructor arguments using the `*` prefix.\n\n$code_theme_new_step_3\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-new-step-3.hf.space?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nLook how fun our theme looks now! With just a few variable changes, our theme looks completely different.\n\nYou may find it helpful to explore the [source code ", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "ght\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nLook how fun our theme looks now! With just a few variable changes, our theme looks completely different.\n\nYou may find it helpful to explore the [source code of the other prebuilt themes](https://github.com/gradio-app/gradio/blob/main/gradio/themes) to see how they modified the base theme. You can also find your browser's Inspector useful to select elements from the UI and see what CSS variables are being used in the styles panel.\n\n", "heading1": "Creating a Full Theme", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "Once you have created a theme, you can upload it to the HuggingFace Hub to let others view it, use it, and build off of it!\n\nUploading a Theme\n\nThere are two ways to upload a theme, via the theme class instance or the command line. We will cover both of them with the previously created `seafoam` theme.\n\n- Via the class instance\n\nEach theme instance has a method called `push_to_hub` we can use to upload a theme to the HuggingFace hub.\n\n```python\nseafoam.push_to_hub(repo_name=\"seafoam\",\n                    version=\"0.0.1\",\n\t\t\t\t\thf_token=\"<token>\")\n```\n\n- Via the command line\n\nFirst save the theme to disk\n\n```python\nseafoam.dump(filename=\"seafoam.json\")\n```\n\nThen use the `upload_theme` command:\n\n```bash\nupload_theme\\\n\"seafoam.json\"\\\n\"seafoam\"\\\n--version \"0.0.1\"\\\n--hf_token \"<token>\"\n```\n\nIn order to upload a theme, you must have a HuggingFace account and pass your [Access Token](https://huggingface.co/docs/huggingface_hub/quick-startlogin)\nas the `hf_token` argument. However, if you log in via the [HuggingFace command line](https://huggingface.co/docs/huggingface_hub/quick-startlogin) (which comes installed with `gradio`),\nyou can omit the `hf_token` argument.\n\nThe `version` argument lets you specify a valid [semantic version](https://www.geeksforgeeks.org/introduction-semantic-versioning/) string for your theme.\nThat way your users are able to specify which version of your theme they want to use in their apps. This also lets you publish updates to your theme without worrying\nabout changing how previously created apps look. The `version` argument is optional. If omitted, the next patch version is automatically applied.\n\nTheme Previews\n\nBy calling `push_to_hub` or `upload_theme`, the theme assets will be stored in a [HuggingFace space](https://huggingface.co/docs/hub/spaces-overview).\n\nFor example, the theme preview for the calm seafoam theme is here: [calm seafoam preview](https://huggingface.co/spaces/shivalikasingh/calm_seafoam).\n\n<div class=\"wrapper\">\n<ifr", "heading1": "Sharing Themes", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "hub/spaces-overview).\n\nFor example, the theme preview for the calm seafoam theme is here: [calm seafoam preview](https://huggingface.co/spaces/shivalikasingh/calm_seafoam).\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://shivalikasingh-calm-seafoam.hf.space/?__theme=light\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nDiscovering Themes\n\nThe [Theme Gallery](https://huggingface.co/spaces/gradio/theme-gallery) shows all the public gradio themes. After publishing your theme,\nit will automatically show up in the theme gallery after a couple of minutes.\n\nYou can sort the themes by the number of likes on the space and from most to least recently created as well as toggling themes between light and dark mode.\n\n<div class=\"wrapper\">\n<iframe\n\tsrc=\"https://gradio-theme-gallery.static.hf.space\"\n\tframeborder=\"0\"\n></iframe>\n</div>\n\nDownloading\n\nTo use a theme from the hub, use the `from_hub` method on the `ThemeClass` and pass it to your app:\n\n```python\nmy_theme = gr.Theme.from_hub(\"gradio/seafoam\")\n\nwith gr.Blocks(theme=my_theme) as demo:\n    ....\n```\n\nYou can also pass the theme string directly to `Blocks` or `Interface` (`gr.Blocks(theme=\"gradio/seafoam\")`)\n\nYou can pin your app to an upstream theme version by using semantic versioning expressions.\n\nFor example, the following would ensure the theme we load from the `seafoam` repo was between versions `0.0.1` and `0.1.0`:\n\n```python\nwith gr.Blocks(theme=\"gradio/seafoam@>=0.0.1,<0.1.0\") as demo:\n    ....\n```\n\nEnjoy creating your own themes! If you make one you're proud of, please share it with the world by uploading it to the hub!\nIf you tag us on [Twitter](https://twitter.com/gradio) we can give your theme a shout out!\n\n<style>\n.wrapper {\n    position: relative;\n    padding-bottom: 56.25%;\n    padding-top: 25px;\n    height: 0;\n}\n.wrapper iframe {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n}\n</style>\n", "heading1": "Sharing Themes", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "er iframe {\n    position: absolute;\n    top: 0;\n    left: 0;\n    width: 100%;\n    height: 100%;\n}\n</style>\n", "heading1": "Sharing Themes", "source_page_url": "https://gradio.app/guides/theming-guide", "source_page_title": "Other Tutorials - Theming Guide Guide"}, {"text": "By default, every Gradio demo includes a built-in queuing system that scales to thousands of requests. When a user of your app submits a request (i.e. submits an input to your function), Gradio adds the request to the queue, and requests are processed in order, generally speaking (this is not exactly true, as discussed below). When the user's request has finished processing, the Gradio server returns the result back to the user using server-side events (SSE). The SSE protocol has several advantages over simply using HTTP POST requests: \n\n(1) They do not time out -- most browsers raise a timeout error if they do not get a response to a POST request after a short period of time (e.g. 1 min). This can be a problem if your inference function takes longer than 1 minute to run or if many people are trying out your demo at the same time, resulting in increased latency.\n\n(2) They allow the server to send multiple updates to the frontend. This means, for example, that the server can send a real-time ETA of how long your prediction will take to complete.\n\nTo configure the queue, simply call the `.queue()` method before launching an `Interface`, `TabbedInterface`, `ChatInterface` or any `Blocks`. Here's an example:\n\n```py\nimport gradio as gr\n\napp = gr.Interface(lambda x:x, \"image\", \"image\")\napp.queue()  <-- Sets up a queue with default parameters\napp.launch()\n```\n\n**How Requests are Processed from the Queue**\n\nWhen a Gradio server is launched, a pool of threads is used to execute requests from the queue. By default, the maximum size of this thread pool is `40` (which is the default inherited from FastAPI, on which the Gradio server is based). However, this does *not* mean that 40 requests are always processed in parallel from the queue. \n\nInstead, Gradio uses a **single-function-single-worker** model by default. This means that each worker thread is only assigned a single function from among all of the functions that could be part of your Gradio app. This ensures that you do", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "-single-worker** model by default. This means that each worker thread is only assigned a single function from among all of the functions that could be part of your Gradio app. This ensures that you do not see, for example, out-of-memory errors, due to multiple workers calling a machine learning model at the same time. Suppose you have 3 functions in your Gradio app: A, B, and C. And you see the following sequence of 7 requests come in from users using your app:\n\n```\n1 2 3 4 5 6 7\n-------------\nA B A A C B A\n```\n\nInitially, 3 workers will get dispatched to handle requests 1, 2, and 5 (corresponding to functions: A, B, C). As soon as any of these workers finish, they will start processing the next function in the queue of the same function type, e.g. the worker that finished processing request 1 will start processing request 3, and so on.\n\nIf you want to change this behavior, there are several parameters that can be used to configure the queue and help reduce latency. Let's go through them one-by-one.\n\n\nThe `default_concurrency_limit` parameter in `queue()`\n\nThe first parameter we will explore is the `default_concurrency_limit` parameter in `queue()`. This controls how many workers can execute the same event. By default, this is set to `1`, but you can set it to a higher integer: `2`, `10`, or even `None` (in the last case, there is no limit besides the total number of available workers). \n\nThis is useful, for example, if your Gradio app does not call any resource-intensive functions. If your app only queries external APIs, then you can set the `default_concurrency_limit` much higher. Increasing this parameter can **linearly multiply the capacity of your server to handle requests**.\n\nSo why not set this parameter much higher all the time? Keep in mind that since requests are processed in parallel, each request will consume memory to store the data and weights for processing. This means that you might get out-of-memory errors if you increase the `default_concurrenc", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "sts are processed in parallel, each request will consume memory to store the data and weights for processing. This means that you might get out-of-memory errors if you increase the `default_concurrency_limit` too high. You may also start to get diminishing returns if the `default_concurrency_limit` is too high because of costs of switching between different worker threads.\n\n**Recommendation**: Increase the `default_concurrency_limit` parameter as high as you can while you continue to see performance gains or until you hit memory limits on your machine. You can [read about Hugging Face Spaces machine specs here](https://huggingface.co/docs/hub/spaces-overview).\n\n\nThe `concurrency_limit` parameter in events\n\nYou can also set the number of requests that can be processed in parallel for each event individually. These take priority over the  `default_concurrency_limit` parameter described previously.\n\nTo do this, set the `concurrency_limit` parameter of any event listener, e.g. `btn.click(..., concurrency_limit=20)` or in the `Interface` or `ChatInterface` classes: e.g. `gr.Interface(..., concurrency_limit=20)`. By default, this parameter is set to the global `default_concurrency_limit`.\n\n\nThe `max_threads` parameter in `launch()`\n\nIf your demo uses non-async functions, e.g. `def` instead of `async def`, they will be run in a threadpool. This threadpool has a size of 40 meaning that only 40 threads can be created to run your non-async functions. If you are running into this limit, you can increase the threadpool size with `max_threads`. The default value is 40.\n\nTip: You should use async functions whenever possible to increase the number of concurrent requests your app can handle. Quick functions that are not CPU-bound are good candidates to be written as `async`. This [guide](https://fastapi.tiangolo.com/async/) is a good primer on the concept.\n\n\nThe `max_size` parameter in `queue()`\n\nA more blunt way to reduce the wait times is simply to prevent too many pe", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "is [guide](https://fastapi.tiangolo.com/async/) is a good primer on the concept.\n\n\nThe `max_size` parameter in `queue()`\n\nA more blunt way to reduce the wait times is simply to prevent too many people from joining the queue in the first place. You can set the maximum number of requests that the queue processes using the `max_size` parameter of `queue()`. If a request arrives when the queue is already of the maximum size, it will not be allowed to join the queue and instead, the user will receive an error saying that the queue is full and to try again. By default, `max_size=None`, meaning that there is no limit to the number of users that can join the queue.\n\nParadoxically, setting a `max_size` can often improve user experience because it prevents users from being dissuaded by very long queue wait times. Users who are more interested and invested in your demo will keep trying to join the queue, and will be able to get their results faster.\n\n**Recommendation**: For a better user experience, set a `max_size` that is reasonable given your expectations of how long users might be willing to wait for a prediction.\n\nThe `max_batch_size` parameter in events\n\nAnother way to increase the parallelism of your Gradio demo is to write your function so that it can accept **batches** of inputs. Most deep learning models can process batches of samples more efficiently than processing individual samples.\n\nIf you write your function to process a batch of samples, Gradio will automatically batch incoming requests together and pass them into your function as a batch of samples. You need to set `batch` to `True` (by default it is `False`) and set a `max_batch_size` (by default it is `4`) based on the maximum number of samples your function is able to handle. These two parameters can be passed into `gr.Interface()` or to an event in Blocks such as `.click()`.\n\nWhile setting a batch is conceptually similar to having workers process requests in parallel, it is often _faster_ than set", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "e passed into `gr.Interface()` or to an event in Blocks such as `.click()`.\n\nWhile setting a batch is conceptually similar to having workers process requests in parallel, it is often _faster_ than setting the `concurrency_count` for deep learning models. The downside is that you might need to adapt your function a little bit to accept batches of samples instead of individual samples.\n\nHere's an example of a function that does _not_ accept a batch of inputs -- it processes a single input at a time:\n\n```py\nimport time\n\ndef trim_words(word, length):\n    return word[:int(length)]\n\n```\n\nHere's the same function rewritten to take in a batch of samples:\n\n```py\nimport time\n\ndef trim_words(words, lengths):\n    trimmed_words = []\n    for w, l in zip(words, lengths):\n        trimmed_words.append(w[:int(l)])\n    return [trimmed_words]\n\n```\n\nThe second function can be used with `batch=True` and an appropriate `max_batch_size` parameter.\n\n**Recommendation**: If possible, write your function to accept batches of samples, and then set `batch` to `True` and the `max_batch_size` as high as possible based on your machine's memory limits.\n\n", "heading1": "Overview of Gradio's Queueing System", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "If you have done everything above, and your demo is still not fast enough, you can upgrade the hardware that your model is running on. Changing the model from running on CPUs to running on GPUs will usually provide a 10x-50x increase in inference time for deep learning models.\n\nIt is particularly straightforward to upgrade your Hardware on Hugging Face Spaces. Simply click on the \"Settings\" tab in your Space and choose the Space Hardware you'd like.\n\n![](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/spaces-gpu-settings.png)\n\nWhile you might need to adapt portions of your machine learning inference code to run on a GPU (here's a [handy guide](https://cnvrg.io/pytorch-cuda/) if you are using PyTorch), Gradio is completely agnostic to the choice of hardware and will work completely fine if you use it with CPUs, GPUs, TPUs, or any other hardware!\n\nNote: your GPU memory is different than your CPU memory, so if you upgrade your hardware,\nyou might need to adjust the value of the `default_concurrency_limit` parameter described above.\n\n", "heading1": "Upgrading your Hardware (GPUs, TPUs, etc.)", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "Congratulations! You know how to set up a Gradio demo for maximum performance. Good luck on your next viral demo!\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/setting-up-a-demo-for-maximum-performance", "source_page_title": "Other Tutorials - Setting Up A Demo For Maximum Performance Guide"}, {"text": "Gradio features [blocks](https://www.gradio.app/docs/blocks) to easily layout applications. To use this feature, you need to stack or nest layout components and create a hierarchy with them. This isn't difficult to implement and maintain for small projects, but after the project gets more complex, this component hierarchy becomes difficult to maintain and reuse.\n\nIn this guide, we are going to explore how we can wrap the layout classes to create more maintainable and easy-to-read applications without sacrificing flexibility.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "We are going to follow the implementation from this Huggingface Space example:\n\n<gradio-app\nspace=\"gradio/wrapping-layouts\">\n</gradio-app>\n\n", "heading1": "Example", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "The wrapping utility has two important classes. The first one is the ```LayoutBase``` class and the other one is the ```Application``` class.\n\nWe are going to look at the ```render``` and ```attach_event``` functions of them for brevity. You can look at the full implementation from [the example code](https://huggingface.co/spaces/WoWoWoWololo/wrapping-layouts/blob/main/app.py).\n\nSo let's start with the ```LayoutBase``` class.\n\nLayoutBase Class\n\n1. Render Function\n\n    Let's look at the ```render``` function in the ```LayoutBase``` class:\n\n```python\nother LayoutBase implementations\n\ndef render(self) -> None:\n    with self.main_layout:\n        for renderable in self.renderables:\n            renderable.render()\n\n    self.main_layout.render()\n```\nThis is a little confusing at first but if you consider the default implementation you can understand it easily.\nLet's look at an example:\n\nIn the default implementation, this is what we're doing:\n\n```python\nwith Row():\n    left_textbox = Textbox(value=\"left_textbox\")\n    right_textbox = Textbox(value=\"right_textbox\")\n```\n\nNow, pay attention to the Textbox variables. These variables' ```render``` parameter is true by default. So as we use the ```with``` syntax and create these variables, they are calling the ```render``` function under the ```with``` syntax.\n\nWe know the render function is called in the constructor with the implementation from the ```gradio.blocks.Block``` class:\n\n```python\nclass Block:\n    constructor parameters are omitted for brevity\n    def __init__(self, ...):\n        other assign functions \n\n        if render:\n            self.render()\n```\n\nSo our implementation looks like this:\n\n```python\nself.main_layout -> Row()\nwith self.main_layout:\n    left_textbox.render()\n    right_textbox.render()\n```\n\nWhat this means is by calling the components' render functions under the ```with``` syntax, we are actually simulating the default implementation.\n\nSo now let's consider two nested ```with```s to see ho", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "at this means is by calling the components' render functions under the ```with``` syntax, we are actually simulating the default implementation.\n\nSo now let's consider two nested ```with```s to see how the outer one works. For this, let's expand our example with the ```Tab``` component:\n\n```python\nwith Tab():\n    with Row():\n        first_textbox = Textbox(value=\"first_textbox\")\n        second_textbox = Textbox(value=\"second_textbox\")\n```\n\nPay attention to the Row and Tab components this time. We have created the Textbox variables above and added them to Row with the ```with``` syntax. Now we need to add the Row component to the Tab component. You can see that the Row component is created with default parameters, so its render parameter is true, that's why the render function is going to be executed under the Tab component's ```with``` syntax.\n\nTo mimic this implementation, we need to call the ```render``` function of the ```main_layout``` variable after the ```with``` syntax of the ```main_layout``` variable.\n\nSo the implementation looks like this:\n\n```python\nwith tab_main_layout:\n    with row_main_layout:\n        first_textbox.render()\n        second_textbox.render()\n\n    row_main_layout.render()\n\ntab_main_layout.render()\n```\n\nThe default implementation and our implementation are the same, but we are using the render function ourselves. So it requires a little work.\n\nNow, let's take a look at the ```attach_event``` function.\n\n2. Attach Event Function\n\n    The function is left as not implemented because it is specific to the class, so each class has to implement its `attach_event` function.\n\n```python\n    other LayoutBase implementations\n\n    def attach_event(self, block_dict: Dict[str, Block]) -> None:\n        raise NotImplementedError\n```\n\nCheck out the ```block_dict``` variable in the ```Application``` class's ```attach_event``` function.\n\nApplication Class\n\n1. Render Function\n\n```python\n    other Application implementations\n\n    def _render(self):\n     ", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "ct``` variable in the ```Application``` class's ```attach_event``` function.\n\nApplication Class\n\n1. Render Function\n\n```python\n    other Application implementations\n\n    def _render(self):\n        with self.app:\n            for child in self.children:\n                child.render()\n\n        self.app.render()\n```\n\nFrom the explanation of the ```LayoutBase``` class's ```render``` function, we can understand the ```child.render``` part.\n\nSo let's look at the bottom part, why are we calling the ```app``` variable's ```render``` function? It's important to call this function because if we look at the implementation in the ```gradio.blocks.Blocks``` class, we can see that it is adding the components and event functions into the root component. To put it another way, it is creating and structuring the gradio application.\n\n2. Attach Event Function\n\n    Let's see how we can attach events to components:\n\n```python\n    other Application implementations\n\n    def _attach_event(self):\n        block_dict: Dict[str, Block] = {}\n\n        for child in self.children:\n            block_dict.update(child.global_children_dict)\n\n        with self.app:\n            for child in self.children:\n                try:\n                    child.attach_event(block_dict=block_dict)\n                except NotImplementedError:\n                    print(f\"{child.name}'s attach_event is not implemented\")\n```\n\nYou can see why the ```global_children_list``` is used in the ```LayoutBase``` class from the example code. With this, all the components in the application are gathered into one dictionary, so the component can access all the components with their names.\n\nThe ```with``` syntax is used here again to attach events to components. If we look at the ```__exit__``` function in the ```gradio.blocks.Blocks``` class, we can see that it is calling the ```attach_load_events``` function which is used for setting event triggers to components. So we have to use the ```with``` syntax to trigger the ```_", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "Blocks``` class, we can see that it is calling the ```attach_load_events``` function which is used for setting event triggers to components. So we have to use the ```with``` syntax to trigger the ```__exit__``` function.\n\nOf course, we can call ```attach_load_events``` without using the ```with``` syntax, but the function needs a ```Context.root_block```, and it is set in the ```__enter__``` function. So we used the ```with``` syntax here rather than calling the function ourselves.\n\n", "heading1": "Implementation", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "In this guide, we saw\n\n- How we can wrap the layouts\n- How components are rendered\n- How we can structure our application with wrapped layout classes\n\nBecause the classes used in this guide are used for demonstration purposes, they may still not be totally optimized or modular. But that would make the guide much longer!\n\nI hope this guide helps you gain another view of the layout classes and gives you an idea about how you can use them for your needs. See the full implementation of our example [here](https://huggingface.co/spaces/WoWoWoWololo/wrapping-layouts/blob/main/app.py).\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/wrapping-layouts", "source_page_title": "Other Tutorials - Wrapping Layouts Guide"}, {"text": "**[OpenAPI](https://www.openapis.org/)** is a widely adopted standard for describing RESTful APIs in a machine-readable format, typically as a JSON  file. \n\nYou can create a Gradio UI from an OpenAPI Spec **in 1 line of Python**, instantly generating an interactive web interface for any API, making it accessible for demos, testing, or sharing with non-developers, without writing custom frontend code.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/from-openapi-spec", "source_page_title": "Other Tutorials - From Openapi Spec Guide"}, {"text": "Gradio now provides a convenient function, `gr.load_openapi`, that can automatically generate a Gradio app from an OpenAPI v3 specification. This function parses the spec, creates UI components for each endpoint and parameter, and lets you interact with the API directly from your browser.\n\nHere's a minimal example:\n\n```python\nimport gradio as gr\n\ndemo = gr.load_openapi(\n    openapi_spec=\"https://petstore3.swagger.io/api/v3/openapi.json\",\n    base_url=\"https://petstore3.swagger.io/api/v3\",\n    paths=[\"/pet.*\"],\n    methods=[\"get\", \"post\"],\n)\n\ndemo.launch()\n```\n\n**Parameters:**\n- **openapi_spec**: URL, file path, or Python dictionary containing the OpenAPI v3 spec (JSON format only).\n- **base_url**: The base URL for the API endpoints (e.g., `https://api.example.com/v1`).\n- **paths** (optional): List of endpoint path patterns (supports regex) to include. If not set, all paths are included.\n- **methods** (optional): List of HTTP methods (e.g., `[\"get\", \"post\"]`) to include. If not set, all methods are included.\n\nThe generated app will display a sidebar with available endpoints and create interactive forms for each operation, letting you make API calls and view responses in real time.\n\n", "heading1": "How it works", "source_page_url": "https://gradio.app/guides/from-openapi-spec", "source_page_title": "Other Tutorials - From Openapi Spec Guide"}, {"text": "Once your Gradio app is running, you can share the URL with others so they can try out the API through a friendly web interface\u2014no code required. For even more power, you can launch the app as an MCP (Model Control Protocol) server using [Gradio's MCP integration](https://www.gradio.app/guides/building-mcp-server-with-gradio), enabling programmatic access and orchestration of your API via the MCP ecosystem. This makes it easy to build, share, and automate API workflows with minimal effort.\n\n", "heading1": "Next steps", "source_page_url": "https://gradio.app/guides/from-openapi-spec", "source_page_title": "Other Tutorials - From Openapi Spec Guide"}, {"text": "When you demo a machine learning model, you might want to collect data from users who try the model, particularly data points in which the model is not behaving as expected. Capturing these \"hard\" data points is valuable because it allows you to improve your machine learning model and make it more reliable and robust.\n\nGradio simplifies the collection of this data by including a **Flag** button with every `Interface`. This allows a user or tester to easily send data back to the machine where the demo is running. In this Guide, we discuss more about how to use the flagging feature, both with `gradio.Interface` as well as with `gradio.Blocks`.\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Flagging with Gradio's `Interface` is especially easy. By default, underneath the output components, there is a button marked **Flag**. When a user testing your model sees input with interesting output, they can click the flag button to send the input and output data back to the machine where the demo is running. The sample is saved to a CSV log file (by default). If the demo involves images, audio, video, or other types of files, these are saved separately in a parallel directory and the paths to these files are saved in the CSV file.\n\nThere are [four parameters](https://gradio.app/docs/interfaceinitialization) in `gradio.Interface` that control how flagging works. We will go over them in greater detail.\n\n- `flagging_mode`: this parameter can be set to either `\"manual\"` (default), `\"auto\"`, or `\"never\"`.\n  - `manual`: users will see a button to flag, and samples are only flagged when the button is clicked.\n  - `auto`: users will not see a button to flag, but every sample will be flagged automatically.\n  - `never`: users will not see a button to flag, and no sample will be flagged.\n- `flagging_options`: this parameter can be either `None` (default) or a list of strings.\n  - If `None`, then the user simply clicks on the **Flag** button and no additional options are shown.\n  - If a list of strings are provided, then the user sees several buttons, corresponding to each of the strings that are provided. For example, if the value of this parameter is `[\"Incorrect\", \"Ambiguous\"]`, then buttons labeled **Flag as Incorrect** and **Flag as Ambiguous** appear. This only applies if `flagging_mode` is `\"manual\"`.\n  - The chosen option is then logged along with the input and output.\n- `flagging_dir`: this parameter takes a string.\n  - It represents what to name the directory where flagged data is stored.\n- `flagging_callback`: this parameter takes an instance of a subclass of the `FlaggingCallback` class\n  - Using this parameter allows you to write custom code that gets run whe", "heading1": "The **Flag** button in `gradio.Interface`", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "flagged data is stored.\n- `flagging_callback`: this parameter takes an instance of a subclass of the `FlaggingCallback` class\n  - Using this parameter allows you to write custom code that gets run when the flag button is clicked\n  - By default, this is set to an instance of `gr.JSONLogger`\n\n", "heading1": "The **Flag** button in `gradio.Interface`", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Within the directory provided by the `flagging_dir` argument, a JSON file will log the flagged data.\n\nHere's an example: The code below creates the calculator interface embedded below it:\n\n```python\nimport gradio as gr\n\n\ndef calculator(num1, operation, num2):\n    if operation == \"add\":\n        return num1 + num2\n    elif operation == \"subtract\":\n        return num1 - num2\n    elif operation == \"multiply\":\n        return num1 * num2\n    elif operation == \"divide\":\n        return num1 / num2\n\n\niface = gr.Interface(\n    calculator,\n    [\"number\", gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]), \"number\"],\n    \"number\",\n    flagging_mode=\"manual\"\n)\n\niface.launch()\n```\n\n<gradio-app space=\"gradio/calculator-flagging-basic\"></gradio-app>\n\nWhen you click the flag button above, the directory where the interface was launched will include a new flagged subfolder, with a csv file inside it. This csv file includes all the data that was flagged.\n\n```directory\n+-- flagged/\n|   +-- logs.csv\n```\n\n_flagged/logs.csv_\n\n```csv\nnum1,operation,num2,Output,timestamp\n5,add,7,12,2022-01-31 11:40:51.093412\n6,subtract,1.5,4.5,2022-01-31 03:25:32.023542\n```\n\nIf the interface involves file data, such as for Image and Audio components, folders will be created to store those flagged data as well. For example an `image` input to `image` output interface will create the following structure.\n\n```directory\n+-- flagged/\n|   +-- logs.csv\n|   +-- image/\n|   |   +-- 0.png\n|   |   +-- 1.png\n|   +-- Output/\n|   |   +-- 0.png\n|   |   +-- 1.png\n```\n\n_flagged/logs.csv_\n\n```csv\nim,Output timestamp\nim/0.png,Output/0.png,2022-02-04 19:49:58.026963\nim/1.png,Output/1.png,2022-02-02 10:40:51.093412\n```\n\nIf you wish for the user to provide a reason for flagging, you can pass a list of strings to the `flagging_options` argument of Interface. Users will have to select one of these choices when flagging, and the option will be saved as an additional column to the CSV.\n\nIf we go back to the calculator example, the fo", "heading1": "What happens to flagged data?", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "` argument of Interface. Users will have to select one of these choices when flagging, and the option will be saved as an additional column to the CSV.\n\nIf we go back to the calculator example, the following code will create the interface embedded below it.\n\n```python\niface = gr.Interface(\n    calculator,\n    [\"number\", gr.Radio([\"add\", \"subtract\", \"multiply\", \"divide\"]), \"number\"],\n    \"number\",\n    flagging_mode=\"manual\",\n    flagging_options=[\"wrong sign\", \"off by one\", \"other\"]\n)\n\niface.launch()\n```\n\n<gradio-app space=\"gradio/calculator-flagging-options\"></gradio-app>\n\nWhen users click the flag button, the csv file will now include a column indicating the selected option.\n\n_flagged/logs.csv_\n\n```csv\nnum1,operation,num2,Output,flag,timestamp\n5,add,7,-12,wrong sign,2022-02-04 11:40:51.093412\n6,subtract,1.5,3.5,off by one,2022-02-04 11:42:32.062512\n```\n\n", "heading1": "What happens to flagged data?", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "What about if you are using `gradio.Blocks`? On one hand, you have even more flexibility\nwith Blocks -- you can write whatever Python code you want to run when a button is clicked,\nand assign that using the built-in events in Blocks.\n\nAt the same time, you might want to use an existing `FlaggingCallback` to avoid writing extra code.\nThis requires two steps:\n\n1. You have to run your callback's `.setup()` somewhere in the code prior to the\n   first time you flag data\n2. When the flagging button is clicked, then you trigger the callback's `.flag()` method,\n   making sure to collect the arguments correctly and disabling the typical preprocessing.\n\nHere is an example with an image sepia filter Blocks demo that lets you flag\ndata using the default `CSVLogger`:\n\n$code_blocks_flag\n$demo_blocks_flag\n\n", "heading1": "Flagging with Blocks", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Important Note: please make sure your users understand when the data they submit is being saved, and what you plan on doing with it. This is especially important when you use `flagging_mode=auto` (when all of the data submitted through the demo is being flagged)\n\nThat's all! Happy building :)\n", "heading1": "Privacy", "source_page_url": "https://gradio.app/guides/using-flagging", "source_page_title": "Other Tutorials - Using Flagging Guide"}, {"text": "Gradio is a Python library that allows you to quickly create customizable web apps for your machine learning models and data processing pipelines. Gradio apps can be deployed on [Hugging Face Spaces](https://hf.space) for free.\n\nIn some cases though, you might want to deploy a Gradio app on your own web server. You might already be using [Nginx](https://www.nginx.com/), a highly performant web server, to serve your website (say `https://www.example.com`), and you want to attach Gradio to a specific subpath on your website (e.g. `https://www.example.com/gradio-demo`).\n\nIn this Guide, we will guide you through the process of running a Gradio app behind Nginx on your own web server to achieve this.\n\n**Prerequisites**\n\n1. A Linux web server with [Nginx installed](https://www.nginx.com/blog/setting-up-nginx/) and [Gradio installed](/quickstart)\n2. A working Gradio app saved as a python file on your web server\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. Start by editing the Nginx configuration file on your web server. By default, this is located at: `/etc/nginx/nginx.conf`\n\nIn the `http` block, add the following line to include server block configurations from a separate file:\n\n```bash\ninclude /etc/nginx/sites-enabled/*;\n```\n\n2. Create a new file in the `/etc/nginx/sites-available` directory (create the directory if it does not already exist), using a filename that represents your app, for example: `sudo nano /etc/nginx/sites-available/my_gradio_app`\n\n3. Paste the following into your file editor:\n\n```bash\nserver {\n    listen 80;\n    server_name example.com www.example.com;  Change this to your domain name\n\n    location /gradio-demo/ {  Change this if you'd like to server your Gradio app on a different path\n        proxy_pass http://127.0.0.1:7860/; Change this if your Gradio app will be running on a different port\n        proxy_buffering off;\n        proxy_redirect off;\n        proxy_http_version 1.1;\n        proxy_set_header Upgrade $http_upgrade;\n        proxy_set_header Connection \"upgrade\";\n        proxy_set_header Host $host;\n        proxy_set_header X-Forwarded-Host $host;\n        proxy_set_header X-Forwarded-Proto $scheme;\n    }\n}\n```\n\n\nTip: Setting the `X-Forwarded-Host` and `X-Forwarded-Proto` headers is important as Gradio uses these, in conjunction with the `root_path` parameter discussed below, to construct the public URL that your app is being served on. Gradio uses the public URL to fetch various static assets. If these headers are not set, your Gradio app may load in a broken state.\n\n*Note:* The `$host` variable does not include the host port. If you are serving your Gradio application on a raw IP address and port, you should use the `$http_host` variable instead, in these lines:\n\n```bash\n        proxy_set_header Host $host;\n        proxy_set_header X-Forwarded-Host $host;\n```\n\n", "heading1": "Editing your Nginx configuration file", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. Before you launch your Gradio app, you'll need to set the `root_path` to be the same as the subpath that you specified in your nginx configuration. This is necessary for Gradio to run on any subpath besides the root of the domain.\n\n    *Note:* Instead of a subpath, you can also provide a complete URL for `root_path` (beginning with `http` or `https`) in which case the `root_path` is treated as an absolute URL instead of a URL suffix (but in this case, you'll need to update the `root_path` if the domain changes).\n\nHere's a simple example of a Gradio app with a custom `root_path` corresponding to the Nginx configuration above.\n\n```python\nimport gradio as gr\nimport time\n\ndef test(x):\ntime.sleep(4)\nreturn x\n\ngr.Interface(test, \"textbox\", \"textbox\").queue().launch(root_path=\"/gradio-demo\")\n```\n\n2. Start a `tmux` session by typing `tmux` and pressing enter (optional)\n\nIt's recommended that you run your Gradio app in a `tmux` session so that you can keep it running in the background easily\n\n3. Then, start your Gradio app. Simply type in `python` followed by the name of your Gradio python file. By default, your app will run on `localhost:7860`, but if it starts on a different port, you will need to update the nginx configuration file above.\n\n", "heading1": "Run your Gradio app on your web server", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "1. If you are in a tmux session, exit by typing CTRL+B (or CMD+B), followed by the \"D\" key.\n\n2. Finally, restart nginx by running `sudo systemctl restart nginx`.\n\nAnd that's it! If you visit `https://example.com/gradio-demo` on your browser, you should see your Gradio app running there\n\n", "heading1": "Restart Nginx", "source_page_url": "https://gradio.app/guides/running-gradio-on-your-web-server-with-nginx", "source_page_title": "Other Tutorials - Running Gradio On Your Web Server With Nginx Guide"}, {"text": "The next generation of AI user interfaces is moving towards audio-native experiences. Users will be able to speak to chatbots and receive spoken responses in return. Several models have been built under this paradigm, including GPT-4o and [mini omni](https://github.com/gpt-omni/mini-omni).\n\nIn this guide, we'll walk you through building your own conversational chat application using mini omni as an example. You can see a demo of the finished app below:\n\n<video src=\"https://github.com/user-attachments/assets/db36f4db-7535-49f1-a2dd-bd36c487ebdf\" controls\nheight=\"600\" width=\"600\" style=\"display: block; margin: auto;\" autoplay=\"true\" loop=\"true\">\n</video>\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "Our application will enable the following user experience:\n\n1. Users click a button to start recording their message\n2. The app detects when the user has finished speaking and stops recording\n3. The user's audio is passed to the omni model, which streams back a response\n4. After omni mini finishes speaking, the user's microphone is reactivated\n5. All previous spoken audio, from both the user and omni, is displayed in a chatbot component\n\nLet's dive into the implementation details.\n\n", "heading1": "Application Overview", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "We'll stream the user's audio from their microphone to the server and determine if the user has stopped speaking on each new chunk of audio.\n\nHere's our `process_audio` function:\n\n```python\nimport numpy as np\nfrom utils import determine_pause\n\ndef process_audio(audio: tuple, state: AppState):\n    if state.stream is None:\n        state.stream = audio[1]\n        state.sampling_rate = audio[0]\n    else:\n        state.stream = np.concatenate((state.stream, audio[1]))\n\n    pause_detected = determine_pause(state.stream, state.sampling_rate, state)\n    state.pause_detected = pause_detected\n\n    if state.pause_detected and state.started_talking:\n        return gr.Audio(recording=False), state\n    return None, state\n```\n\nThis function takes two inputs:\n1. The current audio chunk (a tuple of `(sampling_rate, numpy array of audio)`)\n2. The current application state\n\nWe'll use the following `AppState` dataclass to manage our application state:\n\n```python\nfrom dataclasses import dataclass\n\n@dataclass\nclass AppState:\n    stream: np.ndarray | None = None\n    sampling_rate: int = 0\n    pause_detected: bool = False\n    stopped: bool = False\n    conversation: list = []\n```\n\nThe function concatenates new audio chunks to the existing stream and checks if the user has stopped speaking. If a pause is detected, it returns an update to stop recording. Otherwise, it returns `None` to indicate no changes.\n\nThe implementation of the `determine_pause` function is specific to the omni-mini project and can be found [here](https://huggingface.co/spaces/gradio/omni-mini/blob/eb027808c7bfe5179b46d9352e3fa1813a45f7c3/app.pyL98).\n\n", "heading1": "Processing User Audio", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "After processing the user's audio, we need to generate and stream the chatbot's response. Here's our `response` function:\n\n```python\nimport io\nimport tempfile\nfrom pydub import AudioSegment\n\ndef response(state: AppState):\n    if not state.pause_detected and not state.started_talking:\n        return None, AppState()\n    \n    audio_buffer = io.BytesIO()\n\n    segment = AudioSegment(\n        state.stream.tobytes(),\n        frame_rate=state.sampling_rate,\n        sample_width=state.stream.dtype.itemsize,\n        channels=(1 if len(state.stream.shape) == 1 else state.stream.shape[1]),\n    )\n    segment.export(audio_buffer, format=\"wav\")\n\n    with tempfile.NamedTemporaryFile(suffix=\".wav\", delete=False) as f:\n        f.write(audio_buffer.getvalue())\n    \n    state.conversation.append({\"role\": \"user\",\n                                \"content\": {\"path\": f.name,\n                                \"mime_type\": \"audio/wav\"}})\n    \n    output_buffer = b\"\"\n\n    for mp3_bytes in speaking(audio_buffer.getvalue()):\n        output_buffer += mp3_bytes\n        yield mp3_bytes, state\n\n    with tempfile.NamedTemporaryFile(suffix=\".mp3\", delete=False) as f:\n        f.write(output_buffer)\n    \n    state.conversation.append({\"role\": \"assistant\",\n                    \"content\": {\"path\": f.name,\n                                \"mime_type\": \"audio/mp3\"}})\n    yield None, AppState(conversation=state.conversation)\n```\n\nThis function:\n1. Converts the user's audio to a WAV file\n2. Adds the user's message to the conversation history\n3. Generates and streams the chatbot's response using the `speaking` function\n4. Saves the chatbot's response as an MP3 file\n5. Adds the chatbot's response to the conversation history\n\nNote: The implementation of the `speaking` function is specific to the omni-mini project and can be found [here](https://huggingface.co/spaces/gradio/omni-mini/blob/main/app.pyL116).\n\n", "heading1": "Generating the Response", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "Now let's put it all together using Gradio's Blocks API:\n\n```python\nimport gradio as gr\n\ndef start_recording_user(state: AppState):\n    if not state.stopped:\n        return gr.Audio(recording=True)\n\nwith gr.Blocks() as demo:\n    with gr.Row():\n        with gr.Column():\n            input_audio = gr.Audio(\n                label=\"Input Audio\", sources=\"microphone\", type=\"numpy\"\n            )\n        with gr.Column():\n            chatbot = gr.Chatbot(label=\"Conversation\", type=\"messages\")\n            output_audio = gr.Audio(label=\"Output Audio\", streaming=True, autoplay=True)\n    state = gr.State(value=AppState())\n\n    stream = input_audio.stream(\n        process_audio,\n        [input_audio, state],\n        [input_audio, state],\n        stream_every=0.5,\n        time_limit=30,\n    )\n    respond = input_audio.stop_recording(\n        response,\n        [state],\n        [output_audio, state]\n    )\n    respond.then(lambda s: s.conversation, [state], [chatbot])\n\n    restart = output_audio.stop(\n        start_recording_user,\n        [state],\n        [input_audio]\n    )\n    cancel = gr.Button(\"Stop Conversation\", variant=\"stop\")\n    cancel.click(lambda: (AppState(stopped=True), gr.Audio(recording=False)), None,\n                [state, input_audio], cancels=[respond, restart])\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\nThis setup creates a user interface with:\n- An input audio component for recording user messages\n- A chatbot component to display the conversation history\n- An output audio component for the chatbot's responses\n- A button to stop and reset the conversation\n\nThe app streams user audio in 0.5-second chunks, processes it, generates responses, and updates the conversation history accordingly.\n\n", "heading1": "Building the Gradio App", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "This guide demonstrates how to build a conversational chatbot application using Gradio and the mini omni model. You can adapt this framework to create various audio-based chatbot demos. To see the full application in action, visit the Hugging Face Spaces demo: https://huggingface.co/spaces/gradio/omni-mini\n\nFeel free to experiment with different models, audio processing techniques, or user interface designs to create your own unique conversational AI experiences!", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/conversational-chatbot", "source_page_title": "Streaming - Conversational Chatbot Guide"}, {"text": "First, we'll install the following requirements in our system:\n\n```\nopencv-python\ntorch\ntransformers>=4.43.0\nspaces\n```\n\nThen, we'll download the model from the Hugging Face Hub:\n\n```python\nfrom transformers import RTDetrForObjectDetection, RTDetrImageProcessor\n\nimage_processor = RTDetrImageProcessor.from_pretrained(\"PekingU/rtdetr_r50vd\")\nmodel = RTDetrForObjectDetection.from_pretrained(\"PekingU/rtdetr_r50vd\").to(\"cuda\")\n```\n\nWe're moving the model to the GPU. We'll be deploying our model to Hugging Face Spaces and running the inference in the [free ZeroGPU cluster](https://huggingface.co/zero-gpu-explorers). \n\n\n", "heading1": "Setting up the Model", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "Our inference function will accept a video and a desired confidence threshold.\nObject detection models identify many objects and assign a confidence score to each object. The lower the confidence, the higher the chance of a false positive. So we will let our users set the conference threshold.\n\nOur function will iterate over the frames in the video and run the RT-DETR model over each frame.\nWe will then draw the bounding boxes for each detected object in the frame and save the frame to a new output video.\nThe function will yield each output video in chunks of two seconds.\n\nIn order to keep inference times as low as possible on ZeroGPU (there is a time-based quota),\nwe will halve the original frames-per-second in the output video and resize the input frames to be half the original \nsize before running the model.\n\nThe code for the inference function is below - we'll go over it piece by piece.\n\n```python\nimport spaces\nimport cv2\nfrom PIL import Image\nimport torch\nimport time\nimport numpy as np\nimport uuid\n\nfrom draw_boxes import draw_bounding_boxes\n\nSUBSAMPLE = 2\n\n@spaces.GPU\ndef stream_object_detection(video, conf_threshold):\n    cap = cv2.VideoCapture(video)\n\n    This means we will output mp4 videos\n    video_codec = cv2.VideoWriter_fourcc(*\"mp4v\") type: ignore\n    fps = int(cap.get(cv2.CAP_PROP_FPS))\n\n    desired_fps = fps // SUBSAMPLE\n    width  = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) // 2\n    height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) // 2\n\n    iterating, frame = cap.read()\n\n    n_frames = 0\n\n    Use UUID to create a unique video file\n    output_video_name = f\"output_{uuid.uuid4()}.mp4\"\n\n    Output Video\n    output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) type: ignore\n    batch = []\n\n    while iterating:\n        frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5)\n        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n        if n_frames % SUBSAMPLE == 0:\n            batch.append(frame)\n        if len(batc", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "      frame = cv2.resize( frame, (0,0), fx=0.5, fy=0.5)\n        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n        if n_frames % SUBSAMPLE == 0:\n            batch.append(frame)\n        if len(batch) == 2 * desired_fps:\n            inputs = image_processor(images=batch, return_tensors=\"pt\").to(\"cuda\")\n\n            with torch.no_grad():\n                outputs = model(**inputs)\n\n            boxes = image_processor.post_process_object_detection(\n                outputs,\n                target_sizes=torch.tensor([(height, width)] * len(batch)),\n                threshold=conf_threshold)\n            \n            for i, (array, box) in enumerate(zip(batch, boxes)):\n                pil_image = draw_bounding_boxes(Image.fromarray(array), box, model, conf_threshold)\n                frame = np.array(pil_image)\n                Convert RGB to BGR\n                frame = frame[:, :, ::-1].copy()\n                output_video.write(frame)\n\n            batch = []\n            output_video.release()\n            yield output_video_name\n            output_video_name = f\"output_{uuid.uuid4()}.mp4\"\n            output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height)) type: ignore\n\n        iterating, frame = cap.read()\n        n_frames += 1\n```\n\n1. **Reading from the Video**\n\nOne of the industry standards for creating videos in python is OpenCV so we will use it in this app.\n\nThe `cap` variable is how we will read from the input video. Whenever we call `cap.read()`, we are reading the next frame in the video.\n\nIn order to stream video in Gradio, we need to yield a different video file for each \"chunk\" of the output video.\nWe create the next video file to write to with the `output_video = cv2.VideoWriter(output_video_name, video_codec, desired_fps, (width, height))` line. The `video_codec` is how we specify the type of video file. Only \"mp4\" and \"ts\" files are supported for video sreaming at the moment.\n\n\n2. **The Inference Loop**\n\nFor each frame i", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "dth, height))` line. The `video_codec` is how we specify the type of video file. Only \"mp4\" and \"ts\" files are supported for video sreaming at the moment.\n\n\n2. **The Inference Loop**\n\nFor each frame in the video, we will resize it to be half the size. OpenCV reads files in `BGR` format, so will convert to the expected `RGB` format of transfomers. That's what the first two lines of the while loop are doing. \n\nWe take every other frame and add it to a `batch` list so that the output video is half the original FPS. When the batch covers two seconds of video, we will run the model. The two second threshold was chosen to keep the processing time of each batch small enough so that video is smoothly displayed in the server while not requiring too many separate forward passes. In order for video streaming to work properly in Gradio, the batch size should be at least 1 second. \n\nWe run the forward pass of the model and then use the `post_process_object_detection` method of the model to scale the detected bounding boxes to the size of the input frame.\n\nWe make use of a custom function to draw the bounding boxes (source [here](https://huggingface.co/spaces/gradio/rt-detr-object-detection/blob/main/draw_boxes.pyL14)). We then have to convert from `RGB` to `BGR` before writing back to the output video.\n\nOnce we have finished processing the batch, we create a new output video file for the next batch.\n\n", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "The UI code is pretty similar to other kinds of Gradio apps. \nWe'll use a standard two-column layout so that users can see the input and output videos side by side.\n\nIn order for streaming to work, we have to set `streaming=True` in the output video. Setting the video\nto autoplay is not necessary but it's a better experience for users.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as app:\n    gr.HTML(\n        \"\"\"\n    <h1 style='text-align: center'>\n    Video Object Detection with <a href='https://huggingface.co/PekingU/rtdetr_r101vd_coco_o365' target='_blank'>RT-DETR</a>\n    </h1>\n    \"\"\")\n    with gr.Row():\n        with gr.Column():\n            video = gr.Video(label=\"Video Source\")\n            conf_threshold = gr.Slider(\n                label=\"Confidence Threshold\",\n                minimum=0.0,\n                maximum=1.0,\n                step=0.05,\n                value=0.30,\n            )\n        with gr.Column():\n            output_video = gr.Video(label=\"Processed Video\", streaming=True, autoplay=True)\n\n    video.upload(\n        fn=stream_object_detection,\n        inputs=[video, conf_threshold],\n        outputs=[output_video],\n    )\n\n\n```\n\n\n", "heading1": "The Gradio Demo", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "You can check out our demo hosted on Hugging Face Spaces [here](https://huggingface.co/spaces/gradio/rt-detr-object-detection). \n\nIt is also embedded on this page below\n\n$demo_rt-detr-object-detection", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/object-detection-from-video", "source_page_title": "Streaming - Object Detection From Video Guide"}, {"text": "Just like the classic Magic 8 Ball, a user should ask it a question orally and then wait for a response. Under the hood, we'll use Whisper to transcribe the audio and then use an LLM to generate a magic-8-ball-style answer. Finally, we'll use Parler TTS to read the response aloud.\n\n", "heading1": "The Overview", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "First let's define the UI and put placeholders for all the python logic.\n\n```python\nimport gradio as gr\n\nwith gr.Blocks() as block:\n    gr.HTML(\n        f\"\"\"\n        <h1 style='text-align: center;'> Magic 8 Ball \ud83c\udfb1 </h1>\n        <h3 style='text-align: center;'> Ask a question and receive wisdom </h3>\n        <p style='text-align: center;'> Powered by <a href=\"https://github.com/huggingface/parler-tts\"> Parler-TTS</a>\n        \"\"\"\n    )\n    with gr.Group():\n        with gr.Row():\n            audio_out = gr.Audio(label=\"Spoken Answer\", streaming=True, autoplay=True)\n            answer = gr.Textbox(label=\"Answer\")\n            state = gr.State()\n        with gr.Row():\n            audio_in = gr.Audio(label=\"Speak your question\", sources=\"microphone\", type=\"filepath\")\n\n    audio_in.stop_recording(generate_response, audio_in, [state, answer, audio_out])\\\n        .then(fn=read_response, inputs=state, outputs=[answer, audio_out])\n\nblock.launch()\n```\n\nWe're placing the output Audio and Textbox components and the input Audio component in separate rows. In order to stream the audio from the server, we'll set `streaming=True` in the output Audio component. We'll also set `autoplay=True` so that the audio plays as soon as it's ready.\nWe'll be using the Audio input component's `stop_recording` event to trigger our application's logic when a user stops recording from their microphone.\n\nWe're separating the logic into two parts. First, `generate_response` will take the recorded audio, transcribe it and generate a response with an LLM. We're going to store the response in a `gr.State` variable that then gets passed to the `read_response` function that generates the audio.\n\nWe're doing this in two parts because only `read_response` will require a GPU. Our app will run on Hugging Faces [ZeroGPU](https://huggingface.co/zero-gpu-explorers) which has time-based quotas. Since generating the response can be done with Hugging Face's Inference API, we shouldn't include that code in our GPU func", "heading1": "The UI", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "GPU](https://huggingface.co/zero-gpu-explorers) which has time-based quotas. Since generating the response can be done with Hugging Face's Inference API, we shouldn't include that code in our GPU function as it will needlessly use our GPU quota.\n\n", "heading1": "The UI", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "As mentioned above, we'll use [Hugging Face's Inference API](https://huggingface.co/docs/huggingface_hub/guides/inference) to transcribe the audio and generate a response from an LLM. After instantiating the client, I use the `automatic_speech_recognition` method (this automatically uses Whisper running on Hugging Face's Inference Servers) to transcribe the audio. Then I pass the question to an LLM (Mistal-7B-Instruct) to generate a response. We are prompting the LLM to act like a magic 8 ball with the system message.\n\nOur `generate_response` function will also send empty updates to the output textbox and audio components (returning `None`). \nThis is because I want the Gradio progress tracker to be displayed over the components but I don't want to display the answer until the audio is ready.\n\n\n```python\nfrom huggingface_hub import InferenceClient\n\nclient = InferenceClient(token=os.getenv(\"HF_TOKEN\"))\n\ndef generate_response(audio):\n    gr.Info(\"Transcribing Audio\", duration=5)\n    question = client.automatic_speech_recognition(audio).text\n\n    messages = [{\"role\": \"system\", \"content\": (\"You are a magic 8 ball.\"\n                                              \"Someone will present to you a situation or question and your job \"\n                                              \"is to answer with a cryptic adage or proverb such as \"\n                                              \"'curiosity killed the cat' or 'The early bird gets the worm'.\"\n                                              \"Keep your answers short and do not include the phrase 'Magic 8 Ball' in your response. If the question does not make sense or is off-topic, say 'Foolish questions get foolish answers.'\"\n                                              \"For example, 'Magic 8 Ball, should I get a dog?', 'A dog is ready for you but are you ready for the dog?'\")},\n                {\"role\": \"user\", \"content\": f\"Magic 8 Ball please answer this question -  {question}\"}]\n    \n    response = client.chat_completion(messages,", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "for you but are you ready for the dog?'\")},\n                {\"role\": \"user\", \"content\": f\"Magic 8 Ball please answer this question -  {question}\"}]\n    \n    response = client.chat_completion(messages, max_tokens=64, seed=random.randint(1, 5000),\n                                      model=\"mistralai/Mistral-7B-Instruct-v0.3\")\n\n    response = response.choices[0].message.content.replace(\"Magic 8 Ball\", \"\").replace(\":\", \"\")\n    return response, None, None\n```\n\n\nNow that we have our text response, we'll read it aloud with Parler TTS. The `read_response` function will be a python generator that yields the next chunk of audio as it's ready.\n\n\nWe'll be using the [Mini v0.1](https://huggingface.co/parler-tts/parler_tts_mini_v0.1) for the feature extraction but the [Jenny fine tuned version](https://huggingface.co/parler-tts/parler-tts-mini-jenny-30H) for the voice. This is so that the voice is consistent across generations.\n\n\nStreaming audio with transformers requires a custom Streamer class. You can see the implementation [here](https://huggingface.co/spaces/gradio/magic-8-ball/blob/main/streamer.py). Additionally, we'll convert the output to bytes so that it can be streamed faster from the backend. \n\n\n```python\nfrom streamer import ParlerTTSStreamer\nfrom transformers import AutoTokenizer, AutoFeatureExtractor, set_seed\nimport numpy as np\nimport spaces\nimport torch\nfrom threading import Thread\n\n\ndevice = \"cuda:0\" if torch.cuda.is_available() else \"mps\" if torch.backends.mps.is_available() else \"cpu\"\ntorch_dtype = torch.float16 if device != \"cpu\" else torch.float32\n\nrepo_id = \"parler-tts/parler_tts_mini_v0.1\"\n\njenny_repo_id = \"ylacombe/parler-tts-mini-jenny-30H\"\n\nmodel = ParlerTTSForConditionalGeneration.from_pretrained(\n    jenny_repo_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True\n).to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(repo_id)\nfeature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)\n\nsampling_rate = model.audio_encoder.config.sampling_rate\nf", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "sage=True\n).to(device)\n\ntokenizer = AutoTokenizer.from_pretrained(repo_id)\nfeature_extractor = AutoFeatureExtractor.from_pretrained(repo_id)\n\nsampling_rate = model.audio_encoder.config.sampling_rate\nframe_rate = model.audio_encoder.config.frame_rate\n\n@spaces.GPU\ndef read_response(answer):\n\n    play_steps_in_s = 2.0\n    play_steps = int(frame_rate * play_steps_in_s)\n\n    description = \"Jenny speaks at an average pace with a calm delivery in a very confined sounding environment with clear audio quality.\"\n    description_tokens = tokenizer(description, return_tensors=\"pt\").to(device)\n\n    streamer = ParlerTTSStreamer(model, device=device, play_steps=play_steps)\n    prompt = tokenizer(answer, return_tensors=\"pt\").to(device)\n\n    generation_kwargs = dict(\n        input_ids=description_tokens.input_ids,\n        prompt_input_ids=prompt.input_ids,\n        streamer=streamer,\n        do_sample=True,\n        temperature=1.0,\n        min_new_tokens=10,\n    )\n\n    set_seed(42)\n    thread = Thread(target=model.generate, kwargs=generation_kwargs)\n    thread.start()\n\n    for new_audio in streamer:\n        print(f\"Sample of length: {round(new_audio.shape[0] / sampling_rate, 2)} seconds\")\n        yield answer, numpy_to_mp3(new_audio, sampling_rate=sampling_rate)\n```\n\n", "heading1": "The Logic", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "You can see our final application [here](https://huggingface.co/spaces/gradio/magic-8-ball)!\n\n\n", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/streaming-ai-generated-audio", "source_page_title": "Streaming - Streaming Ai Generated Audio Guide"}, {"text": "Automatic speech recognition (ASR), the conversion of spoken speech to text, is a very important and thriving area of machine learning. ASR algorithms run on practically every smartphone, and are becoming increasingly embedded in professional workflows, such as digital assistants for nurses and doctors. Because ASR algorithms are designed to be used directly by customers and end users, it is important to validate that they are behaving as expected when confronted with a wide variety of speech patterns (different accents, pitches, and background audio conditions).\n\nUsing `gradio`, you can easily build a demo of your ASR model and share that with a testing team, or test it yourself by speaking through the microphone on your device.\n\nThis tutorial will show how to take a pretrained speech-to-text model and deploy it with a Gradio interface. We will start with a **_full-context_** model, in which the user speaks the entire audio before the prediction runs. Then we will adapt the demo to make it **_streaming_**, meaning that the audio model will convert speech as you speak. \n\nPrerequisites\n\nMake sure you have the `gradio` Python package already [installed](/getting_started). You will also need a pretrained speech recognition model. In this tutorial, we will build demos from 2 ASR libraries:\n\n- Transformers (for this, `pip install torch transformers torchaudio`)\n\nMake sure you have at least one of these installed so that you can follow along the tutorial. You will also need `ffmpeg` [installed on your system](https://www.ffmpeg.org/download.html), if you do not already have it, to process files from the microphone.\n\nHere's how to build a real time speech recognition (ASR) app:\n\n1. [Set up the Transformers ASR Model](1-set-up-the-transformers-asr-model)\n2. [Create a Full-Context ASR Demo with Transformers](2-create-a-full-context-asr-demo-with-transformers)\n3. [Create a Streaming ASR Demo with Transformers](3-create-a-streaming-asr-demo-with-transformers)\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "First, you will need to have an ASR model that you have either trained yourself or you will need to download a pretrained model. In this tutorial, we will start by using a pretrained ASR model from the model, `whisper`.\n\nHere is the code to load `whisper` from Hugging Face `transformers`.\n\n```python\nfrom transformers import pipeline\n\np = pipeline(\"automatic-speech-recognition\", model=\"openai/whisper-base.en\")\n```\n\nThat's it!\n\n", "heading1": "1. Set up the Transformers ASR Model", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "We will start by creating a _full-context_ ASR demo, in which the user speaks the full audio before using the ASR model to run inference. This is very easy with Gradio -- we simply create a function around the `pipeline` object above.\n\nWe will use `gradio`'s built in `Audio` component, configured to take input from the user's microphone and return a filepath for the recorded audio. The output component will be a plain `Textbox`.\n\n$code_asr\n$demo_asr\n\nThe `transcribe` function takes a single parameter, `audio`, which is a numpy array of the audio the user recorded. The `pipeline` object expects this in float32 format, so we convert it first to float32, and then extract the transcribed text.\n\n", "heading1": "2. Create a Full-Context ASR Demo with Transformers", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "To make this a *streaming* demo, we need to make these changes:\n\n1. Set `streaming=True` in the `Audio` component\n2. Set `live=True` in the `Interface`\n3. Add a `state` to the interface to store the recorded audio of a user\n\nTip: You can also set `time_limit` and `stream_every` parameters in the interface. The `time_limit` caps the amount of time each user's stream can take. The default is 30 seconds so users won't be able to stream audio for more than 30 seconds. The `stream_every` parameter controls how frequently data is sent to your function. By default it is 0.5 seconds.\n\nTake a look below.\n\n$code_stream_asr\n\nNotice that we now have a state variable because we need to track all the audio history. `transcribe` gets called whenever there is a new small chunk of audio, but we also need to keep track of all the audio spoken so far in the state. As the interface runs, the `transcribe` function gets called, with a record of all the previously spoken audio in the `stream` and the new chunk of audio as `new_chunk`. We return the new full audio to be stored back in its current state, and we also return the transcription. Here, we naively append the audio together and call the `transcriber` object on the entire audio. You can imagine more efficient ways of handling this, such as re-processing only the last 5 seconds of audio whenever a new chunk of audio is received. \n\n$demo_stream_asr\n\nNow the ASR model will run inference as you speak! \n", "heading1": "3. Create a Streaming ASR Demo with Transformers", "source_page_url": "https://gradio.app/guides/real-time-speech-recognition", "source_page_title": "Streaming - Real Time Speech Recognition Guide"}, {"text": "Modern voice applications should feel natural and responsive, moving beyond the traditional \"click-to-record\" pattern. By combining Groq's fast inference capabilities with automatic speech detection, we can create a more intuitive interaction model where users can simply start talking whenever they want to engage with the AI.\n\n> Credits: VAD and Gradio code inspired by [WillHeld's Diva-audio-chat](https://huggingface.co/spaces/WillHeld/diva-audio-chat/tree/main).\n\nIn this tutorial, you will learn how to create a multimodal Gradio and Groq app that has automatic speech detection. You can also watch the full video tutorial which includes a demo of the application:\n\n<iframe width=\"560\" height=\"315\" src=\"https://www.youtube.com/embed/azXaioGdm2Q\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n\n", "heading1": "Introduction", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "Many voice apps currently work by the user clicking record, speaking, then stopping the recording. While this can be a powerful demo, the most natural mode of interaction with voice requires the app to dynamically detect when the user is speaking, so they can talk back and forth without having to continually click a record button. \n\nCreating a natural interaction with voice and text requires a dynamic and low-latency response. Thus, we need both automatic voice detection and fast inference. With @ricky0123/vad-web powering speech detection and Groq powering the LLM, both of these requirements are met. Groq provides a lightning fast response, and Gradio allows for easy creation of impressively functional apps.\n\nThis tutorial shows you how to build a calorie tracking app where you speak to an AI that automatically detects when you start and stop your response, and provides its own text response back to guide you with questions that allow it to give a calorie estimate of your last meal.\n\n", "heading1": "Background", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "- **Gradio**: Provides the web interface and audio handling capabilities\n- **@ricky0123/vad-web**: Handles voice activity detection\n- **Groq**: Powers fast LLM inference for natural conversations\n- **Whisper**: Transcribes speech to text\n\nSetting Up the Environment\n\nFirst, let\u2019s install and import our essential libraries and set up a client for using the Groq API. Here\u2019s how to do it:\n\n`requirements.txt`\n```\ngradio\ngroq\nnumpy\nsoundfile\nlibrosa\nspaces\nxxhash\ndatasets\n```\n\n`app.py`\n```python\nimport groq\nimport gradio as gr\nimport soundfile as sf\nfrom dataclasses import dataclass, field\nimport os\n\nInitialize Groq client securely\napi_key = os.environ.get(\"GROQ_API_KEY\")\nif not api_key:\n    raise ValueError(\"Please set the GROQ_API_KEY environment variable.\")\nclient = groq.Client(api_key=api_key)\n```\n\nHere, we\u2019re pulling in key libraries to interact with the Groq API, build a sleek UI with Gradio, and handle audio data. We\u2019re accessing the Groq API key securely with a key stored in an environment variable, which is a security best practice for avoiding leaking the API key.\n\n---\n\nState Management for Seamless Conversations\n\nWe need a way to keep track of our conversation history, so the chatbot remembers past interactions, and manage other states like whether recording is currently active. To do this, let\u2019s create an `AppState` class:\n\n```python\n@dataclass\nclass AppState:\n    conversation: list = field(default_factory=list)\n    stopped: bool = False\n    model_outs: Any = None\n```\n\nOur `AppState` class is a handy tool for managing conversation history and tracking whether recording is on or off. Each instance will have its own fresh list of conversations, making sure chat history is isolated to each session. \n\n---\n\nTranscribing Audio with Whisper on Groq\n\nNext, we\u2019ll create a function to transcribe the user\u2019s audio input into text using Whisper, a powerful transcription model hosted on Groq. This transcription will also help us determine whether there\u2019s meani", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "e\u2019ll create a function to transcribe the user\u2019s audio input into text using Whisper, a powerful transcription model hosted on Groq. This transcription will also help us determine whether there\u2019s meaningful speech in the input. Here\u2019s how:\n\n```python\ndef transcribe_audio(client, file_name):\n    if file_name is None:\n        return None\n\n    try:\n        with open(file_name, \"rb\") as audio_file:\n            response = client.audio.transcriptions.with_raw_response.create(\n                model=\"whisper-large-v3-turbo\",\n                file=(\"audio.wav\", audio_file),\n                response_format=\"verbose_json\",\n            )\n            completion = process_whisper_response(response.parse())\n            return completion\n    except Exception as e:\n        print(f\"Error in transcription: {e}\")\n        return f\"Error in transcription: {str(e)}\"\n```\n\nThis function opens the audio file and sends it to Groq\u2019s Whisper model for transcription, requesting detailed JSON output. verbose_json is needed to get information to determine if speech was included in the audio. We also handle any potential errors so our app doesn\u2019t fully crash if there\u2019s an issue with the API request. \n\n```python\ndef process_whisper_response(completion):\n    \"\"\"\n    Process Whisper transcription response and return text or null based on no_speech_prob\n    \n    Args:\n        completion: Whisper transcription response object\n        \n    Returns:\n        str or None: Transcribed text if no_speech_prob <= 0.7, otherwise None\n    \"\"\"\n    if completion.segments and len(completion.segments) > 0:\n        no_speech_prob = completion.segments[0].get('no_speech_prob', 0)\n        print(\"No speech prob:\", no_speech_prob)\n\n        if no_speech_prob > 0.7:\n            return None\n            \n        return completion.text.strip()\n    \n    return None\n```\n\nWe also need to interpret the audio data response. The process_whisper_response function takes the resulting completion from Whisper and checks if the audio was j", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "ext.strip()\n    \n    return None\n```\n\nWe also need to interpret the audio data response. The process_whisper_response function takes the resulting completion from Whisper and checks if the audio was just background noise or had actual speaking that was transcribed. It uses a threshold of 0.7 to interpret the no_speech_prob, and will return None if there was no speech. Otherwise, it will return the text transcript of the conversational response from the human.\n\n\n---\n\nAdding Conversational Intelligence with LLM Integration\n\nOur chatbot needs to provide intelligent, friendly responses that flow naturally. We\u2019ll use a Groq-hosted Llama-3.2 for this:\n\n```python\ndef generate_chat_completion(client, history):\n    messages = []\n    messages.append(\n        {\n            \"role\": \"system\",\n            \"content\": \"In conversation with the user, ask questions to estimate and provide (1) total calories, (2) protein, carbs, and fat in grams, (3) fiber and sugar content. Only ask *one question at a time*. Be conversational and natural.\",\n        }\n    )\n\n    for message in history:\n        messages.append(message)\n\n    try:\n        completion = client.chat.completions.create(\n            model=\"llama-3.2-11b-vision-preview\",\n            messages=messages,\n        )\n        return completion.choices[0].message.content\n    except Exception as e:\n        return f\"Error in generating chat completion: {str(e)}\"\n```\n\nWe\u2019re defining a system prompt to guide the chatbot\u2019s behavior, ensuring it asks one question at a time and keeps things conversational. This setup also includes error handling to ensure the app gracefully manages any issues.\n\n---\n\nVoice Activity Detection for Hands-Free Interaction\n\nTo make our chatbot hands-free, we\u2019ll add Voice Activity Detection (VAD) to automatically detect when someone starts or stops speaking. Here\u2019s how to implement it using ONNX in JavaScript:\n\n```javascript\nasync function main() {\n  const script1 = document.createElement(\"script\");\n  scrip", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "ly detect when someone starts or stops speaking. Here\u2019s how to implement it using ONNX in JavaScript:\n\n```javascript\nasync function main() {\n  const script1 = document.createElement(\"script\");\n  script1.src = \"https://cdn.jsdelivr.net/npm/onnxruntime-web@1.14.0/dist/ort.js\";\n  document.head.appendChild(script1)\n  const script2 = document.createElement(\"script\");\n  script2.onload = async () =>  {\n    console.log(\"vad loaded\");\n    var record = document.querySelector('.record-button');\n    record.textContent = \"Just Start Talking!\"\n    \n    const myvad = await vad.MicVAD.new({\n      onSpeechStart: () => {\n        var record = document.querySelector('.record-button');\n        var player = document.querySelector('streaming-out')\n        if (record != null && (player == null || player.paused)) {\n          record.click();\n        }\n      },\n      onSpeechEnd: (audio) => {\n        var stop = document.querySelector('.stop-button');\n        if (stop != null) {\n          stop.click();\n        }\n      }\n    })\n    myvad.start()\n  }\n  script2.src = \"https://cdn.jsdelivr.net/npm/@ricky0123/vad-web@0.0.7/dist/bundle.min.js\";\n}\n```\n\nThis script loads our VAD model and sets up functions to start and stop recording automatically. When the user starts speaking, it triggers the recording, and when they stop, it ends the recording.\n\n---\n\nBuilding a User Interface with Gradio\n\nNow, let\u2019s create an intuitive and visually appealing user interface with Gradio. This interface will include an audio input for capturing voice, a chat window for displaying responses, and state management to keep things synchronized.\n\n```python\nwith gr.Blocks(theme=theme, js=js) as demo:\n    with gr.Row():\n        input_audio = gr.Audio(\n            label=\"Input Audio\",\n            sources=[\"microphone\"],\n            type=\"numpy\",\n            streaming=False,\n            waveform_options=gr.WaveformOptions(waveform_color=\"B83A4B\"),\n        )\n    with gr.Row():\n        chatbot = gr.Chatbot(label=\"Conversati", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "       type=\"numpy\",\n            streaming=False,\n            waveform_options=gr.WaveformOptions(waveform_color=\"B83A4B\"),\n        )\n    with gr.Row():\n        chatbot = gr.Chatbot(label=\"Conversation\", type=\"messages\")\n    state = gr.State(value=AppState())\n```\n\nIn this code block, we\u2019re using Gradio\u2019s `Blocks` API to create an interface with an audio input, a chat display, and an application state manager. The color customization for the waveform adds a nice visual touch.\n\n---\n\nHandling Recording and Responses\n\nFinally, let\u2019s link the recording and response components to ensure the app reacts smoothly to user inputs and provides responses in real-time.\n\n```python\n    stream = input_audio.start_recording(\n        process_audio,\n        [input_audio, state],\n        [input_audio, state],\n    )\n    respond = input_audio.stop_recording(\n        response, [state, input_audio], [state, chatbot]\n    )\n```\n\nThese lines set up event listeners for starting and stopping the recording, processing the audio input, and generating responses. By linking these events, we create a cohesive experience where users can simply talk, and the chatbot handles the rest.\n\n---\n\n", "heading1": "Key Components", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "1. When you open the app, the VAD system automatically initializes and starts listening for speech\n2. As soon as you start talking, it triggers the recording automatically\n3. When you stop speaking, the recording ends and:\n   - The audio is transcribed using Whisper\n   - The transcribed text is sent to the LLM\n   - The LLM generates a response about calorie tracking\n   - The response is displayed in the chat interface\n4. This creates a natural back-and-forth conversation where you can simply talk about your meals and get instant feedback on nutritional content\n\nThis app demonstrates how to create a natural voice interface that feels responsive and intuitive. By combining Groq's fast inference with automatic speech detection, we've eliminated the need for manual recording controls while maintaining high-quality interactions. The result is a practical calorie tracking assistant that users can simply talk to as naturally as they would to a human nutritionist.\n\nLink to GitHub repository: [Groq Gradio Basics](https://github.com/bklieger-groq/gradio-groq-basics/tree/main/calorie-tracker)", "heading1": "Summary", "source_page_url": "https://gradio.app/guides/automatic-voice-detection", "source_page_title": "Streaming - Automatic Voice Detection Guide"}, {"text": "Start by installing all the dependencies. Add the following lines to a `requirements.txt` file and run `pip install -r requirements.txt`:\n\n```bash\nopencv-python\ntwilio\ngradio>=5.0\ngradio-webrtc\nonnxruntime-gpu\n```\n\nWe'll use the ONNX runtime to speed up YOLOv10 inference. This guide assumes you have access to a GPU. If you don't, change `onnxruntime-gpu` to `onnxruntime`. Without a GPU, the model will run slower, resulting in a laggy demo.\n\nWe'll use OpenCV for image manipulation and the [Gradio WebRTC](https://github.com/freddyaboulton/gradio-webrtc) custom component to use [WebRTC](https://webrtc.org/) under the hood, achieving near-zero latency.\n\n**Note**: If you want to deploy this app on any cloud provider, you'll need to use the free Twilio API for their [TURN servers](https://www.twilio.com/docs/stun-turn). Create a free account on Twilio. If you're not familiar with TURN servers, consult this [guide](https://www.twilio.com/docs/stun-turn/faqfaq-what-is-nat).\n\n", "heading1": "Setting up", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "We'll download the YOLOv10 model from the Hugging Face hub and instantiate a custom inference class to use this model. \n\nThe implementation of the inference class isn't covered in this guide, but you can find the source code [here](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n/blob/main/inference.pyL9) if you're interested. This implementation borrows heavily from this [github repository](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection).\n\nWe're using the `yolov10-n` variant because it has the lowest latency. See the [Performance](https://github.com/THU-MIG/yolov10?tab=readme-ov-fileperformance) section of the README in the YOLOv10 GitHub repository.\n\n```python\nfrom huggingface_hub import hf_hub_download\nfrom inference import YOLOv10\n\nmodel_file = hf_hub_download(\n    repo_id=\"onnx-community/yolov10n\", filename=\"onnx/model.onnx\"\n)\n\nmodel = YOLOv10(model_file)\n\ndef detection(image, conf_threshold=0.3):\n    image = cv2.resize(image, (model.input_width, model.input_height))\n    new_image = model.detect_objects(image, conf_threshold)\n    return new_image\n```\n\nOur inference function, `detection`, accepts a numpy array from the webcam and a desired confidence threshold. Object detection models like YOLO identify many objects and assign a confidence score to each. The lower the confidence, the higher the chance of a false positive. We'll let users adjust the confidence threshold.\n\nThe function returns a numpy array corresponding to the same input image with all detected objects in bounding boxes.\n\n", "heading1": "The Inference Function", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "The Gradio demo is straightforward, but we'll implement a few specific features:\n\n1. Use the `WebRTC` custom component to ensure input and output are sent to/from the server with WebRTC. \n2. The [WebRTC](https://github.com/freddyaboulton/gradio-webrtc) component will serve as both an input and output component.\n3. Utilize the `time_limit` parameter of the `stream` event. This parameter sets a processing time for each user's stream. In a multi-user setting, such as on Spaces, we'll stop processing the current user's stream after this period and move on to the next. \n\nWe'll also apply custom CSS to center the webcam and slider on the page.\n\n```python\nimport gradio as gr\nfrom gradio_webrtc import WebRTC\n\ncss = \"\"\".my-group {max-width: 600px !important; max-height: 600px !important;}\n         .my-column {display: flex !important; justify-content: center !important; align-items: center !important;}\"\"\"\n\nwith gr.Blocks(css=css) as demo:\n    gr.HTML(\n        \"\"\"\n        <h1 style='text-align: center'>\n        YOLOv10 Webcam Stream (Powered by WebRTC \u26a1\ufe0f)\n        </h1>\n        \"\"\"\n    )\n    with gr.Column(elem_classes=[\"my-column\"]):\n        with gr.Group(elem_classes=[\"my-group\"]):\n            image = WebRTC(label=\"Stream\", rtc_configuration=rtc_configuration)\n            conf_threshold = gr.Slider(\n                label=\"Confidence Threshold\",\n                minimum=0.0,\n                maximum=1.0,\n                step=0.05,\n                value=0.30,\n            )\n\n        image.stream(\n            fn=detection, inputs=[image, conf_threshold], outputs=[image], time_limit=10\n        )\n\nif __name__ == \"__main__\":\n    demo.launch()\n```\n\n", "heading1": "The Gradio Demo", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "Our app is hosted on Hugging Face Spaces [here](https://huggingface.co/spaces/freddyaboulton/webrtc-yolov10n). \n\nYou can use this app as a starting point to build real-time image applications with Gradio. Don't hesitate to open issues in the space or in the [WebRTC component GitHub repo](https://github.com/freddyaboulton/gradio-webrtc) if you have any questions or encounter problems.", "heading1": "Conclusion", "source_page_url": "https://gradio.app/guides/object-detection-from-webcam-with-webrtc", "source_page_title": "Streaming - Object Detection From Webcam With Webrtc Guide"}, {"text": "To create a demo that has both the input and the output components, you simply need to set the values of the `inputs` and `outputs` parameter in `Interface()`. Here's an example demo of a simple image filter:\n\n$code_sepia_filter\n$demo_sepia_filter\n\n", "heading1": "Standard demos", "source_page_url": "https://gradio.app/guides/four-kinds-of-interfaces", "source_page_title": "Building Interfaces - Four Kinds Of Interfaces Guide"}, {"text": "What about demos that only contain outputs? In order to build such a demo, you simply set the value of the `inputs` parameter in `Interface()` to `None`. Here's an example demo of a mock image generation model:\n\n$code_fake_gan_no_input\n$demo_fake_gan_no_input\n\n", "heading1": "Output-only demos", "source_page_url": "https://gradio.app/guides/four-kinds-of-interfaces", "source_page_title": "Building Interfaces - Four Kinds Of Interfaces Guide"}, {"text": "Similarly, to create a demo that only contains inputs, set the value of `outputs` parameter in `Interface()` to be `None`. Here's an example demo that saves any uploaded image to disk:\n\n$code_save_file_no_output\n$demo_save_file_no_output\n\n", "heading1": "Input-only demos", "source_page_url": "https://gradio.app/guides/four-kinds-of-interfaces", "source_page_title": "Building Interfaces - Four Kinds Of Interfaces Guide"}, {"text": "A demo that has a single component as both the input and the output. It can simply be created by setting the values of the `inputs` and `outputs` parameter as the same component. Here's an example demo of a text generation model:\n\n$code_unified_demo_text_generation\n$demo_unified_demo_text_generation\n\nIt may be the case that none of the 4 cases fulfill your exact needs. In this case, you need to use the `gr.Blocks()` approach!", "heading1": "Unified demos", "source_page_url": "https://gradio.app/guides/four-kinds-of-interfaces", "source_page_title": "Building Interfaces - Four Kinds Of Interfaces Guide"}, {"text": "Gradio includes more than 30 pre-built components (as well as many [community-built _custom components_](https://www.gradio.app/custom-components/gallery)) that can be used as inputs or outputs in your demo. These components correspond to common data types in machine learning and data science, e.g. the `gr.Image` component is designed to handle input or output images, the `gr.Label` component displays classification labels and probabilities, the `gr.LinePlot` component displays line plots, and so on. \n\n", "heading1": "Gradio Components", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "We used the default versions of the `gr.Textbox` and `gr.Slider`, but what if you want to change how the UI components look or behave?\n\nLet's say you want to customize the slider to have values from 1 to 10, with a default of 2. And you wanted to customize the output text field \u2014 you want it to be larger and have a label.\n\nIf you use the actual classes for `gr.Textbox` and `gr.Slider` instead of the string shortcuts, you have access to much more customizability through component attributes.\n\n$code_hello_world_2\n$demo_hello_world_2\n\n", "heading1": "Components Attributes", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "Suppose you had a more complex function, with multiple outputs as well. In the example below, we define a function that takes a string, boolean, and number, and returns a string and number. \n\n$code_hello_world_3\n$demo_hello_world_3\n\nJust as each component in the `inputs` list corresponds to one of the parameters of the function, in order, each component in the `outputs` list corresponds to one of the values returned by the function, in order.\n\n", "heading1": "Multiple Input and Output Components", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "Gradio supports many types of components, such as `Image`, `DataFrame`, `Video`, or `Label`. Let's try an image-to-image function to get a feel for these!\n\n$code_sepia_filter\n$demo_sepia_filter\n\nWhen using the `Image` component as input, your function will receive a NumPy array with the shape `(height, width, 3)`, where the last dimension represents the RGB values. We'll return an image as well in the form of a NumPy array. \n\nGradio handles the preprocessing and postprocessing to convert images to NumPy arrays and vice versa. You can also control the preprocessing performed with the `type=` keyword argument. For example, if you wanted your function to take a file path to an image instead of a NumPy array, the input `Image` component could be written as:\n\n```python\ngr.Image(type=\"filepath\")\n```\n\nYou can read more about the built-in Gradio components and how to customize them in the [Gradio docs](https://gradio.app/docs).\n\n", "heading1": "An Image Example", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "You can provide example data that a user can easily load into `Interface`. This can be helpful to demonstrate the types of inputs the model expects, as well as to provide a way to explore your dataset in conjunction with your model. To load example data, you can provide a **nested list** to the `examples=` keyword argument of the Interface constructor. Each sublist within the outer list represents a data sample, and each element within the sublist represents an input for each input component. The format of example data for each component is specified in the [Docs](https://gradio.app/docscomponents).\n\n$code_calculator\n$demo_calculator\n\nYou can load a large dataset into the examples to browse and interact with the dataset through Gradio. The examples will be automatically paginated (you can configure this through the `examples_per_page` argument of `Interface`).\n\nContinue learning about examples in the [More On Examples](https://gradio.app/guides/more-on-examples) guide.\n\n", "heading1": "Example Inputs", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "In the previous example, you may have noticed the `title=` and `description=` keyword arguments in the `Interface` constructor that helps users understand your app.\n\nThere are three arguments in the `Interface` constructor to specify where this content should go:\n\n- `title`: which accepts text and can display it at the very top of interface, and also becomes the page title.\n- `description`: which accepts text, markdown or HTML and places it right under the title.\n- `article`: which also accepts text, markdown or HTML and places it below the interface.\n\n![annotated](https://github.com/gradio-app/gradio/blob/main/guides/assets/annotated.png?raw=true)\n\nAnother useful keyword argument is `label=`, which is present in every `Component`. This modifies the label text at the top of each `Component`. You can also add the `info=` keyword argument to form elements like `Textbox` or `Radio` to provide further information on their usage.\n\n```python\ngr.Number(label='Age', info='In years, must be greater than 0')\n```\n\n", "heading1": "Descriptive Content", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "If your prediction function takes many inputs, you may want to hide some of them within a collapsed accordion to avoid cluttering the UI. The `Interface` class takes an `additional_inputs` argument which is similar to `inputs` but any input components included here are not visible by default. The user must click on the accordion to show these components. The additional inputs are passed into the prediction function, in order, after the standard inputs.\n\nYou can customize the appearance of the accordion by using the optional `additional_inputs_accordion` argument, which accepts a string (in which case, it becomes the label of the accordion), or an instance of the `gr.Accordion()` class (e.g. this lets you control whether the accordion is open or closed by default).\n\nHere's an example:\n\n$code_interface_with_additional_inputs\n$demo_interface_with_additional_inputs\n\n", "heading1": "Additional Inputs within an Accordion", "source_page_url": "https://gradio.app/guides/the-interface-class", "source_page_title": "Building Interfaces - The Interface Class Guide"}, {"text": "Adding examples to an Interface is as easy as providing a list of lists to the `examples`\nkeyword argument.\nEach sublist is a data sample, where each element corresponds to an input of the prediction function.\nThe inputs must be ordered in the same order as the prediction function expects them.\n\nIf your interface only has one input component, then you can provide your examples as a regular list instead of a list of lists.\n\nLoading Examples from a Directory\n\nYou can also specify a path to a directory containing your examples. If your Interface takes only a single file-type input, e.g. an image classifier, you can simply pass a directory filepath to the `examples=` argument, and the `Interface` will load the images in the directory as examples.\nIn the case of multiple inputs, this directory must\ncontain a log.csv file with the example values.\nIn the context of the calculator demo, we can set `examples='/demo/calculator/examples'` and in that directory we include the following `log.csv` file:\n\n```csv\nnum,operation,num2\n5,\"add\",3\n4,\"divide\",2\n5,\"multiply\",3\n```\n\nThis can be helpful when browsing flagged data. Simply point to the flagged directory and the `Interface` will load the examples from the flagged data.\n\nProviding Partial Examples\n\nSometimes your app has many input components, but you would only like to provide examples for a subset of them. In order to exclude some inputs from the examples, pass `None` for all data samples corresponding to those particular components.\n\n", "heading1": "Providing Examples", "source_page_url": "https://gradio.app/guides/more-on-examples", "source_page_title": "Building Interfaces - More On Examples Guide"}, {"text": "You may wish to provide some cached examples of your model for users to quickly try out, in case your model takes a while to run normally.\nIf `cache_examples=True`, your Gradio app will run all of the examples and save the outputs when you call the `launch()` method. This data will be saved in a directory called `gradio_cached_examples` in your working directory by default. You can also set this directory with the `GRADIO_EXAMPLES_CACHE` environment variable, which can be either an absolute path or a relative path to your working directory.\n\nWhenever a user clicks on an example, the output will automatically be populated in the app now, using data from this cached directory instead of actually running the function. This is useful so users can quickly try out your model without adding any load!\n\nAlternatively, you can set `cache_examples=\"lazy\"`. This means that each particular example will only get cached after it is first used (by any user) in the Gradio app. This is helpful if your prediction function is long-running and you do not want to wait a long time for your Gradio app to start.\n\nKeep in mind once the cache is generated, it will not be updated automatically in future launches. If the examples or function logic change, delete the cache folder to clear the cache and rebuild it with another `launch()`.\n", "heading1": "Caching examples", "source_page_url": "https://gradio.app/guides/more-on-examples", "source_page_title": "Building Interfaces - More On Examples Guide"}, {"text": "If the state is something that should be accessible to all function calls and all users, you can create a variable outside the function call and access it inside the function. For example, you may load a large model outside the function and use it inside the function so that every function call does not need to reload the model.\n\n$code_score_tracker\n\nIn the code above, the `scores` array is shared between all users. If multiple users are accessing this demo, their scores will all be added to the same list, and the returned top 3 scores will be collected from this shared reference.\n\n", "heading1": "Global State", "source_page_url": "https://gradio.app/guides/interface-state", "source_page_title": "Building Interfaces - Interface State Guide"}, {"text": "Another type of data persistence Gradio supports is session state, where data persists across multiple submits within a page session. However, data is _not_ shared between different users of your model. To store data in a session state, you need to do three things:\n\n1. Pass in an extra parameter into your function, which represents the state of the interface.\n2. At the end of the function, return the updated value of the state as an extra return value.\n3. Add the `'state'` input and `'state'` output components when creating your `Interface`\n\nHere's a simple app to illustrate session state - this app simply stores users previous submissions and displays them back to the user:\n\n\n$code_interface_state\n$demo_interface_state\n\n\nNotice how the state persists across submits within each page, but if you load this demo in another tab (or refresh the page), the demos will not share chat history. Here, we could not store the submission history in a global variable, otherwise the submission history would then get jumbled between different users.\n\nThe initial value of the `State` is `None` by default. If you pass a parameter to the `value` argument of `gr.State()`, it is used as the default value of the state instead. \n\nNote: the `Interface` class only supports a single session state variable (though it can be a list with multiple elements). For more complex use cases, you can use Blocks, [which supports multiple `State` variables](/guides/state-in-blocks/). Alternatively, if you are building a chatbot that maintains user state, consider using the `ChatInterface` abstraction, [which manages state automatically](/guides/creating-a-chatbot-fast).\n", "heading1": "Session State", "source_page_url": "https://gradio.app/guides/interface-state", "source_page_title": "Building Interfaces - Interface State Guide"}, {"text": "You can make interfaces automatically refresh by setting `live=True` in the interface. Now the interface will recalculate as soon as the user input changes.\n\n$code_calculator_live\n$demo_calculator_live\n\nNote there is no submit button, because the interface resubmits automatically on change.\n\n", "heading1": "Live Interfaces", "source_page_url": "https://gradio.app/guides/reactive-interfaces", "source_page_title": "Building Interfaces - Reactive Interfaces Guide"}, {"text": "Some components have a \"streaming\" mode, such as `Audio` component in microphone mode, or the `Image` component in webcam mode. Streaming means data is sent continuously to the backend and the `Interface` function is continuously being rerun.\n\nThe difference between `gr.Audio(source='microphone')` and `gr.Audio(source='microphone', streaming=True)`, when both are used in `gr.Interface(live=True)`, is that the first `Component` will automatically submit data and run the `Interface` function when the user stops recording, whereas the second `Component` will continuously send data and run the `Interface` function _during_ recording.\n\nHere is example code of streaming images from the webcam.\n\n$code_stream_frames\n\nStreaming can also be done in an output component. A `gr.Audio(streaming=True)` output component can take a stream of audio data yielded piece-wise by a generator function and combines them into a single audio file. For a detailed example, see our guide on performing [automatic speech recognition](/guides/real-time-speech-recognition) with Gradio.\n", "heading1": "Streaming Components", "source_page_url": "https://gradio.app/guides/reactive-interfaces", "source_page_title": "Building Interfaces - Reactive Interfaces Guide"}, {"text": "**Prerequisite**: Gradio requires [Python 3.10 or higher](https://www.python.org/downloads/).\n\n\nWe recommend installing Gradio using `pip`, which is included by default in Python. Run this in your terminal or command prompt:\n\n```bash\npip install --upgrade gradio\n```\n\n\nTip: It is best to install Gradio in a virtual environment. Detailed installation instructions for all common operating systems <a href=\"https://www.gradio.app/main/guides/installing-gradio-in-a-virtual-environment\">are provided here</a>. \n\n", "heading1": "Installation", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "You can run Gradio in your favorite code editor, Jupyter notebook, Google Colab, or anywhere else you write Python. Let's write your first Gradio app:\n\n\n$code_hello_world_4\n\n\nTip: We shorten the imported name from <code>gradio</code> to <code>gr</code>. This is a widely adopted convention for better readability of code. \n\nNow, run your code. If you've written the Python code in a file named `app.py`, then you would run `python app.py` from the terminal.\n\nThe demo below will open in a browser on [http://localhost:7860](http://localhost:7860) if running from a file. If you are running within a notebook, the demo will appear embedded within the notebook.\n\n$demo_hello_world_4\n\nType your name in the textbox on the left, drag the slider, and then press the Submit button. You should see a friendly greeting on the right.\n\nTip: When developing locally, you can run your Gradio app in <strong>hot reload mode</strong>, which automatically reloads the Gradio app whenever you make changes to the file. To do this, simply type in <code>gradio</code> before the name of the file instead of <code>python</code>. In the example above, you would type: `gradio app.py` in your terminal. You can also enable <strong>vibe mode</strong> by using the <code>--vibe</code> flag, e.g. <code>gradio --vibe app.py</code>, which provides an in-browser chat that can be used to write or edit your Gradio app using natural language. Learn more in the <a href=\"https://www.gradio.app/guides/developing-faster-with-reload-mode\">Hot Reloading Guide</a>.\n\n\n**Understanding the `Interface` Class**\n\nYou'll notice that in order to make your first demo, you created an instance of the `gr.Interface` class. The `Interface` class is designed to create demos for machine learning models which accept one or more inputs, and return one or more outputs. \n\nThe `Interface` class has three core arguments:\n\n- `fn`: the function to wrap a user interface (UI) around\n- `inputs`: the Gradio component(s) to use for the input. The num", "heading1": "Building Your First Demo", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "turn one or more outputs. \n\nThe `Interface` class has three core arguments:\n\n- `fn`: the function to wrap a user interface (UI) around\n- `inputs`: the Gradio component(s) to use for the input. The number of components should match the number of arguments in your function.\n- `outputs`: the Gradio component(s) to use for the output. The number of components should match the number of return values from your function.\n\nThe `fn` argument is very flexible -- you can pass *any* Python function that you want to wrap with a UI. In the example above, we saw a relatively simple function, but the function could be anything from a music generator to a tax calculator to the prediction function of a pretrained machine learning model.\n\nThe `inputs` and `outputs` arguments take one or more Gradio components. As we'll see, Gradio includes more than [30 built-in components](https://www.gradio.app/docs/gradio/introduction) (such as the `gr.Textbox()`, `gr.Image()`, and `gr.HTML()` components) that are designed for machine learning applications. \n\nTip: For the `inputs` and `outputs` arguments, you can pass in the name of these components as a string (`\"textbox\"`) or an instance of the class (`gr.Textbox()`).\n\nIf your function accepts more than one argument, as is the case above, pass a list of input components to `inputs`, with each input component corresponding to one of the arguments of the function, in order. The same holds true if your function returns more than one value: simply pass in a list of components to `outputs`. This flexibility makes the `Interface` class a very powerful way to create demos.\n\nWe'll dive deeper into the `gr.Interface` on our series on [building Interfaces](https://www.gradio.app/main/guides/the-interface-class).\n\n", "heading1": "Building Your First Demo", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "What good is a beautiful demo if you can't share it? Gradio lets you easily share a machine learning demo without having to worry about the hassle of hosting on a web server. Simply set `share=True` in `launch()`, and a publicly accessible URL will be created for your demo. Let's revisit our example demo,  but change the last line as follows:\n\n```python\nimport gradio as gr\n\ndef greet(name):\n    return \"Hello \" + name + \"!\"\n\ndemo = gr.Interface(fn=greet, inputs=\"textbox\", outputs=\"textbox\")\n    \ndemo.launch(share=True)  Share your demo with just 1 extra parameter \ud83d\ude80\n```\n\nWhen you run this code, a public URL will be generated for your demo in a matter of seconds, something like:\n\n\ud83d\udc49 &nbsp; `https://a23dsf231adb.gradio.live`\n\nNow, anyone around the world can try your Gradio demo from their browser, while the machine learning model and all computation continues to run locally on your computer.\n\nTo learn more about sharing your demo, read our dedicated guide on [sharing your Gradio application](https://www.gradio.app/guides/sharing-your-app).\n\n\n", "heading1": "Sharing Your Demo", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "So far, we've been discussing the `Interface` class, which is a high-level class that lets to build demos quickly with Gradio. But what else does Gradio include?\n\nCustom Demos with `gr.Blocks`\n\nGradio offers a low-level approach for designing web apps with more customizable layouts and data flows with the `gr.Blocks` class. Blocks supports things like controlling where components appear on the page, handling multiple data flows and more complex interactions (e.g. outputs can serve as inputs to other functions), and updating properties/visibility of components based on user interaction \u2014 still all in Python. \n\nYou can build very custom and complex applications using `gr.Blocks()`. For example, the popular image generation [Automatic1111 Web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) is built using Gradio Blocks. We dive deeper into the `gr.Blocks` on our series on [building with Blocks](https://www.gradio.app/guides/blocks-and-event-listeners).\n\nChatbots with `gr.ChatInterface`\n\nGradio includes another high-level class, `gr.ChatInterface`, which is specifically designed to create Chatbot UIs. Similar to `Interface`, you supply a function and Gradio creates a fully working Chatbot UI. If you're interested in creating a chatbot, you can jump straight to [our dedicated guide on `gr.ChatInterface`](https://www.gradio.app/guides/creating-a-chatbot-fast).\n\nThe Gradio Python & JavaScript Ecosystem\n\nThat's the gist of the core `gradio` Python library, but Gradio is actually so much more! It's an entire ecosystem of Python and JavaScript libraries that let you build machine learning applications, or query them programmatically, in Python or JavaScript. Here are other related parts of the Gradio ecosystem:\n\n* [Gradio Python Client](https://www.gradio.app/guides/getting-started-with-the-python-client) (`gradio_client`): query any Gradio app programmatically in Python.\n* [Gradio JavaScript Client](https://www.gradio.app/guides/getting-started-with-t", "heading1": "An Overview of Gradio", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "app/guides/getting-started-with-the-python-client) (`gradio_client`): query any Gradio app programmatically in Python.\n* [Gradio JavaScript Client](https://www.gradio.app/guides/getting-started-with-the-js-client) (`@gradio/client`): query any Gradio app programmatically in JavaScript.\n* [Gradio-Lite](https://www.gradio.app/guides/gradio-lite) (`@gradio/lite`): write Gradio apps in Python that run entirely in the browser (no server needed!), thanks to Pyodide. \n* [Hugging Face Spaces](https://huggingface.co/spaces): the most popular place to host Gradio applications \u2014 for free!\n\n", "heading1": "An Overview of Gradio", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "Keep learning about Gradio sequentially using the Gradio Guides, which include explanations as well as example code and embedded interactive demos. Next up: [let's dive deeper into the Interface class](https://www.gradio.app/guides/the-interface-class).\n\nOr, if you already know the basics and are looking for something specific, you can search the more [technical API documentation](https://www.gradio.app/docs/).\n\n\n", "heading1": "What's Next?", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}, {"text": "You can also build Gradio applications without writing any code. Simply type `gradio sketch` into your terminal to open up an editor that lets you define and modify Gradio components, adjust their layouts, add events, all through a web editor. Or [use this hosted version of Gradio Sketch, running on Hugging Face Spaces](https://huggingface.co/spaces/aliabid94/Sketch).", "heading1": "Gradio Sketch", "source_page_url": "https://gradio.app/guides/quickstart", "source_page_title": "Getting Started - Quickstart Guide"}]
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