text stringlengths 0 2k | heading1 stringlengths 4 79 | source_page_url stringclasses 183 values | source_page_title stringclasses 183 values |
|---|---|---|---|
**Using Dataframe as an input component.**
How Dataframe will pass its value to your function:
Type: `pd.DataFrame | np.ndarray | pl.DataFrame | list[list]`
Passes the uploaded spreadsheet data as a `pandas.DataFrame`, `numpy.array`,
`polars.DataFrame`, or native 2D Python `list[list]` depending on `type`.
Example Code
import gradio as gr
def predict(
value: pd.DataFrame | np.ndarray | pl.DataFrame | list[list]
):
process value from the Dataframe component
return "prediction"
interface = gr.Interface(predict, gr.Dataframe(), gr.Textbox())
interface.launch()
**Using Dataframe as an output component**
How Dataframe expects you to return a value:
Type: `pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | None`
Expects data in any of these formats:
* `pandas.DataFrame`
* `pandas.Styler`
* `numpy.array`
* `polars.DataFrame`
* `list[list]`
* `list`
* `dict` with keys 'data' (and optionally 'headers')
* `str` path to a csv, which is rendered as the spreadsheet.
Example Code
import gradio as gr
def predict(text) -> pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | None
process value to return to the Dataframe component
return value
interface = gr.Interface(predict, gr.Textbox(), gr.Dataframe())
interface.launch()
| Behavior | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
Parameters ▼
value: pd.DataFrame | Styler | np.ndarray | pl.DataFrame | list | list[list] | dict | str | Callable | None
default `= None`
Default value to display in the DataFrame. Supports pandas, numpy, polars, and
list of lists. If a Styler is provided, it will be used to set the displayed
value in the DataFrame (e.g. to set precision of numbers) if the `interactive`
is False. If a Callable function is provided, the function will be called
whenever the app loads to set the initial value of the component.
headers: list[str] | None
default `= None`
List of str header names. These are used to set the column headers of the
dataframe if the value does not have headers. If None, no headers are shown.
row_count: int | None
default `= None`
The number of rows to initially display in the dataframe. If None, the number
of rows is determined automatically based on the `value`.
row_limits: tuple[int | None, int | None] | None
default `= None`
A tuple of two integers specifying the minimum and maximum number of rows that
can be created in the dataframe via the UI. If the first element is None,
there is no minimum number of rows. If the second element is None, there is no
maximum number of rows. Only applies if `interactive` is True.
col_count: None
default `= None`
This parameter is deprecated. Please use `column_count` instead.
column_count: int | None
default `= None`
The number of columns to initially display in the dataframe. If None, the
number of columns is determined automatically based on the `value`.
column_limits: tuple[int | None, int | None] | None
default `= None`
A tuple of two integers specifying the minimum and maximum number of columns
that can be created in the dataframe via the UI. If the first element is None,
there is no minimum number of columns. If the second element is None, there is
no maximum number of columns. Only applies if | Initialization | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
t can be created in the dataframe via the UI. If the first element is None,
there is no minimum number of columns. If the second element is None, there is
no maximum number of columns. Only applies if `interactive` is True.
datatype: Literal['str', 'number', 'bool', 'date', 'markdown', 'html', 'image', 'auto'] | list[Literal['str', 'number', 'bool', 'date', 'markdown', 'html']]
default `= "str"`
Datatype of values in sheet. Can be provided per column as a list of strings,
or for the entire sheet as a single string. Valid datatypes are "str",
"number", "bool", "date", and "markdown". Boolean columns will display as
checkboxes. If the datatype "auto" is used, the column datatypes are
automatically selected based on the value input if possible.
type: Literal['pandas', 'numpy', 'array', 'polars']
default `= "pandas"`
Type of value to be returned by component. "pandas" for pandas dataframe,
"numpy" for numpy array, "polars" for polars dataframe, or "array" for a
Python list of lists.
latex_delimiters: list[dict[str, str | bool]] | None
default `= None`
A list of dicts of the form {"left": open delimiter (str), "right": close
delimiter (str), "display": whether to display in newline (bool)} that will be
used to render LaTeX expressions. If not provided, `latex_delimiters` is set
to `[{ "left": "$$", "right": "$$", "display": True }]`, so only expressions
enclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass
in an empty list to disable LaTeX rendering. For more information, see the
[KaTeX documentation](https://katex.org/docs/autorender.html). Only applies to
columns whose datatype is "markdown".
label: str | I18nData | None
default `= None`
the label for this component. Appears above the component and is also used as
the header if there are a table of examples for this component. If None and
used in a `gr.Interface`, the label will be the name of the parameter this
component is | Initialization | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
e the component and is also used as
the header if there are a table of examples for this component. If None and
used in a `gr.Interface`, the label will be the name of the parameter this
component is assigned to.
show_label: bool | None
default `= None`
if True, will display label.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
max_height: int | str
default `= 500`
The maximum height of the dataframe, specified in pixels if a number is
passed, or in CSS units if a string is passed. If more rows are created than
can fit in the height, a scrollbar will appear.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
interactive: bool | None
default `= None`
if True, will allow users to edit the dataframe; if False, can only be used to
display data. If not provided, this is inferred based on whether the component
is used as an input or output.
visible: bool | Literal['hidden']
default `= True`
If False, co | Initialization | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
used to
display data. If not provided, this is inferred based on whether the component
is used as an input or output.
visible: bool | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
elem_id: str | None
default `= None`
An optional string that is assigned as the id of this component in the HTML
DOM. Can be used for targeting CSS styles.
elem_classes: list[str] | str | None
default `= None`
An optional list of strings that are assigned as the classes of this component
in the HTML DOM. Can be used for targeting CSS styles.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
wrap: bool
default `= False`
If True, the text in table cells will wrap when appropriate. If False and the
`column_width` parameter is not set, the column widths will expand based on
the cell contents and the table may need to be horizontally scrolled. If
`column_width` is set, then any overflow text will be hidden.
line_breaks: bool
default `= True`
| Initialization | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
nd based on
the cell contents and the table may need to be horizontally scrolled. If
`column_width` is set, then any overflow text will be hidden.
line_breaks: bool
default `= True`
If True (default), will enable Github-flavored Markdown line breaks in chatbot
messages. If False, single new lines will be ignored. Only applies for columns
of type "markdown."
column_widths: list[str | int] | None
default `= None`
An optional list representing the width of each column. The elements of the
list should be in the format "100px" (ints are also accepted and converted to
pixel values) or "10%". The percentage width is calculated based on the
viewport width of the table. If not provided, the column widths will be
automatically determined based on the content of the cells.
buttons: list[Literal['fullscreen', 'copy']] | None
default `= None`
A list of buttons to show in the top right corner of the component. Valid
options are "fullscreen" and "copy". The "fullscreen" button allows the user
to view the table in fullscreen mode. The "copy" button allows the user to
copy the table data to the clipboard. By default, all buttons are shown.
show_row_numbers: bool
default `= False`
If True, will display row numbers in a separate column.
max_chars: int | None
default `= None`
Maximum number of characters to display in each cell before truncating
(single-clicking a cell value will still reveal the full content). If None, no
truncation is applied.
show_search: Literal['none', 'search', 'filter']
default `= "none"`
Show a search input in the toolbar. If "search", a search input is shown. If
"filter", a search input and filter buttons are shown. If "none", no search
input is shown.
pinned_columns: int | None
default `= None`
If provided, will pin the specified number of columns from the left.
static_columns: list[int] | None
default `= None`
List o | Initialization | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
pinned_columns: int | None
default `= None`
If provided, will pin the specified number of columns from the left.
static_columns: list[int] | None
default `= None`
List of column indices (int) that should not be editable. Only applies when
interactive=True. When specified, col_count is automatically set to "fixed"
and columns cannot be inserted or deleted.
| Initialization | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
Shortcuts
gradio.Dataframe
Interface String Shortcut `"dataframe"`
Initialization Uses default values
gradio.Numpy
Interface String Shortcut `"numpy"`
Initialization Uses type="numpy"
gradio.Matrix
Interface String Shortcut `"matrix"`
Initialization Uses type="array"
gradio.List
Interface String Shortcut `"list"`
Initialization Uses type="array", col_count=1
| Shortcuts | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
filter_recordsmatrix_transposetax_calculatorsort_records
| Demos | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The Dataframe component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listeners
Dataframe.change(fn, ···)
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.
Dataframe.input(fn, ···)
This listener is triggered when the user changes the value of the Dataframe.
Dataframe.select(fn, ···)
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
<https://www.gradio.app/main/docs/gradio/eventdata> for more details.
Dataframe.edit(fn, ···)
This listener is triggered when the user edits the Dataframe (e.g. image)
using the built-in editor.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. Often a machine learning
model's prediction function. Each parameter of the function corresponds to one
input component, and the function should return a single value or a tuple of
values, with each element in the tuple corresponding to one output component.
inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as inputs. If the fun | Event Listeners | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
nent.
inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as inputs. If the function takes no inputs,
this should be an empty list.
outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as outputs. If the function returns no
outputs, this should be an empty list.
api_name: str | None
default `= None`
defines how the endpoint appears in the API docs. Can be a string or None. If
set to a string, the endpoint will be exposed in the API docs with the given
name. If None (default), the name of the function will be used as the API
endpoint.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
If True, will place the request on the queue, if the queue has been enabl | Event Listeners | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
If True, will place the request on the queue, if the queue has been enabled.
If False, will not put this event on the queue, even if the queue has been
enabled. If None, will use the queue setting of the gradio app.
batch: bool
default `= False`
If True, then the function should process a batch of inputs, meaning that it
should accept a list of input values for each parameter. The lists should be
of equal length (and be up to length `max_batch_size`). The function is then
*required* to return a tuple of lists (even if there is only 1 output
component), with each list in the tuple corresponding to one output component.
max_batch_size: int
default `= 4`
Maximum number of inputs to batch together if this is called from the queue
(only relevant if batch=True)
preprocess: bool
default `= True`
If False, will not run preprocessing of component data before running 'fn'
(e.g. leaving it as a base64 string if this method is called with the `Image`
component).
postprocess: bool
default `= True`
If False, will not run postprocessing of component data before returning 'fn'
output to the browser.
cancels: dict[str, Any] | list[dict[str, Any]] | None
default `= None`
A list of other events to cancel when this listener is triggered. For example,
setting cancels=[click_event] will cancel the click_event, where click_event
is the return value of another components .click method. Functions that have
not yet run (or generators that are iterating) will be cancelled, but
functions that are currently running will be allowed to finish.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
| Event Listeners | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
, 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
api_visibility: Literal['public', 'private', 'undocumented']
default `= "public"`
controls the visibility and accessibility of this endpoint. Can be "public"
(shown in API docs and callable by clients), "private" (hidden from API docs
and not callable by clients), or "undocumented" (hidden from API docs but
callable by clients and via gr.load). If fn is None, api_visibility will
automatically be set to "private".
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as ide | Event Listeners | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
`= 0.5`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
[Styling The Gradio Dataframe](../../guides/styling-the-gradio-
dataframe/)[Filters Tables And Stats](../../guides/filters-tables-and-stats/)
| Event Listeners | https://gradio.app/docs/gradio/dataframe | Gradio - Dataframe Docs |
ChatInterface is Gradio's high-level abstraction for creating chatbot UIs,
and allows you to create a web-based demo around a chatbot model in a few
lines of code. Only one parameter is required: fn, which takes a function that
governs the response of the chatbot based on the user input and chat history.
Additional parameters can be used to control the appearance and behavior of
the demo.
| Description | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
**Basic Example** : A chatbot that echoes back the users’s message
import gradio as gr
def echo(message, history):
return message
demo = gr.ChatInterface(fn=echo, examples=["hello", "hola", "merhaba"], title="Echo Bot")
demo.launch()
**Custom Chatbot** : A `gr.ChatInterface` with a custom `gr.Chatbot` that
includes a placeholder as well as upvote/downvote buttons. The upvote/downvote
buttons are automatically added when a `.like()` event is attached to a
`gr.Chatbot`. In order to attach event listeners to your custom chatbot, wrap
the `gr.Chatbot` as well as the `gr.ChatInterface` inside of a `gr.Blocks`
like this:
import gradio as gr
def yes(message, history):
return "yes"
def vote(data: gr.LikeData):
if data.liked:
print("You upvoted this response: " + data.value["value"])
else:
print("You downvoted this response: " + data.value["value"])
with gr.Blocks() as demo:
chatbot = gr.Chatbot(placeholder="<strong>Your Personal Yes-Man</strong><br>Ask Me Anything")
chatbot.like(vote, None, None)
gr.ChatInterface(fn=yes, chatbot=chatbot)
demo.launch()
| Example Usage | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
Parameters ▼
fn: Callable
the function to wrap the chat interface around. 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.
multimodal: bool
default `= False`
if True, the chat interface will use a `gr.MultimodalTextbox` component for
the input, which allows for the uploading of multimedia files. If False, the
chat interface will use a gr.Textbox component for the input. If this is True,
the first argument of `fn` should accept not a `str` message but a `dict`
message with keys "text" and "files"
chatbot: Chatbot | None
default `= None`
an instance of the gr.Chatbot component to use for the chat interface, if you
would like to customize the chatbot properties. If not provided, a default
gr.Chatbot component will be created.
textbox: Textbox | MultimodalTextbox | None
default `= None`
an instance of the gr.Textbox or gr.MultimodalTextbox component to use for the
chat interface, if you would like to customize the textbox properties. If not
provided, a default gr.Textbox or gr.MultimodalTextbox component will be
created.
additional_inputs: str | Component | list[str | Component] | None
default `= None`
an instance or list of instances of gradio components (or their string
shortcuts) to use as additional inputs to the chatbot. If the components are
not already rendered in a surrounding Blocks, then the components will be
displayed under the chatbot, in an accordion. The values of these components
will be passed into `fn` as arguments in order after the chat history.
add | Initialization | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
cks, then the components will be
displayed under the chatbot, in an accordion. The values of these components
will be passed into `fn` as arguments in order after the chat history.
additional_inputs_accordion: str | Accordion | None
default `= None`
if a string is provided, this is the label of the `gr.Accordion` to use to
contain additional inputs. A `gr.Accordion` object can be provided as well to
configure other properties of the container holding the additional inputs.
Defaults to a `gr.Accordion(label="Additional Inputs", open=False)`. This
parameter is only used if `additional_inputs` is provided.
additional_outputs: Component | list[Component] | None
default `= None`
an instance or list of instances of gradio components to use as additional
outputs from the chat function. These must be components that are already
defined in the same Blocks scope. If provided, the chat function should return
additional values for these components. See $demo/chatinterface_artifacts.
editable: bool
default `= False`
if True, users can edit past messages to regenerate responses.
examples: list[str] | list[MultimodalValue] | list[list] | None
default `= None`
sample inputs for the function; if provided, appear within the chatbot and can
be clicked to populate the chatbot input. Should be a list of strings
representing text-only examples, or a list of dictionaries (with keys `text`
and `files`) representing multimodal examples. If `additional_inputs` are
provided, the examples must be a list of lists, where the first element of
each inner list is the string or dictionary example message and the remaining
elements are the example values for the additional inputs -- in this case, the
examples will appear under the chatbot.
example_labels: list[str] | None
default `= None`
labels for the examples, to be displayed instead of the examples themselves.
If provided, should be a list of strings with th | Initialization | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
atbot.
example_labels: list[str] | None
default `= None`
labels for the examples, to be displayed instead of the examples themselves.
If provided, should be a list of strings with the same length as the examples
list. Only applies when examples are displayed within the chatbot (i.e. when
`additional_inputs` is not provided).
example_icons: list[str] | None
default `= None`
icons for the examples, to be displayed above the examples. If provided,
should be a list of string URLs or local paths with the same length as the
examples list. Only applies when examples are displayed within the chatbot
(i.e. when `additional_inputs` is not provided).
run_examples_on_click: bool
default `= True`
if True, clicking on an example will run the example through the chatbot fn
and the response will be displayed in the chatbot. If False, clicking on an
example will only populate the chatbot input with the example message. Has no
effect if `cache_examples` is True
cache_examples: bool | None
default `= None`
if True, caches examples in the server for fast runtime in examples. The
default option in HuggingFace Spaces is True. The default option elsewhere is
False. Note that examples are cached separately from Gradio's queue() so
certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will
not be displayed in Gradio's UI for cached examples.
cache_mode: Literal['eager', 'lazy'] | None
default `= None`
if "eager", all examples are cached at app launch. If "lazy", examples are
cached for all users after the first use by any user of the app. If None, will
use the GRADIO_CACHE_MODE environment variable if defined, or default to
"eager".
title: str | I18nData | None
default `= None`
a title for the interface; if provided, appears above chatbot in large font.
Also used as the tab title when opened in a browser window.
description: str | None
default `= None | Initialization | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
None`
a title for the interface; if provided, appears above chatbot in large font.
Also used as the tab title when opened in a browser window.
description: str | None
default `= None`
a description for the interface; if provided, appears above the chatbot and
beneath the title in regular font. Accepts Markdown and HTML content.
flagging_mode: Literal['never', 'manual'] | None
default `= None`
one of "never", "manual". If "never", users will not see a button to flag an
input and output. If "manual", users will see a button to flag.
flagging_options: list[str] | tuple[str, ...] | None
default `= ('Like', 'Dislike')`
a list of strings representing the options that users can choose from when
flagging a message. Defaults to ["Like", "Dislike"]. These two case-sensitive
strings will render as "thumbs up" and "thumbs down" icon respectively next to
each bot message, but any other strings appear under a separate flag icon.
flagging_dir: str
default `= ".gradio/flagged"`
path to the the directory where flagged data is stored. If the directory does
not exist, it will be created.
analytics_enabled: bool | None
default `= None`
whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED
environment variable if defined, or default to True.
autofocus: bool
default `= True`
if True, autofocuses to the textbox when the page loads.
autoscroll: bool
default `= True`
If True, will automatically scroll to the bottom of the chatbot when a new
message appears, unless the user scrolls up. If False, will not scroll to the
bottom of the chatbot automatically.
submit_btn: str | bool | None
default `= True`
If True, will show a submit button with a submit icon within the textbox. If a
string, will use that string as the submit button text in place of the icon.
If False, will not show a submit button.
stop_btn: str | b | Initialization | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
tton with a submit icon within the textbox. If a
string, will use that string as the submit button text in place of the icon.
If False, will not show a submit button.
stop_btn: str | bool | None
default `= True`
If True, will show a button with a stop icon during generator executions, to
stop generating. If a string, will use that string as the submit button text
in place of the stop icon. If False, will not show a stop button.
concurrency_limit: int | None | Literal['default']
default `= "default"`
if set, this is the maximum number of chatbot submissions that can be running
simultaneously. Can be set to None to mean no limit (any number of chatbot
submissions can be running simultaneously). Set to "default" to use the
default concurrency limit (defined by the `default_concurrency_limit`
parameter in `.queue()`, which is 1 by default).
delete_cache: tuple[int, int] | None
default `= None`
a tuple corresponding [frequency, age] both expressed in number of seconds.
Every `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. For example, setting this to (86400, 86400) will delete temporary
files every day. The cache will be deleted entirely when the server restarts.
If None, no cache deletion will occur.
show_progress: Literal['full', 'minimal', 'hidden']
default `= "minimal"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
fill_height: bool
default `= True`
if True, the chat interface will expand to the height of window.
fill_width: bool
default `= False`
Whether to horizontally expand to fill container fully. If False, centers and
constrains app to | Initialization | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
hat interface will expand to the height of window.
fill_width: bool
default `= False`
Whether to horizontally expand to fill container fully. If False, centers and
constrains app to a maximum width.
api_name: str | None
default `= None`
defines how the chat endpoint appears in the API docs. Can be a string or
None. If set to a string, the endpoint will be exposed in the API docs with
the given name. If None, the name of the function will be used.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
api_visibility: Literal['public', 'private', 'undocumented']
default `= "public"`
Controls the visibility of the chat endpoint. Can be "public" (shown in API
docs and callable), "private" (hidden from API docs and not callable), or
"undocumented" (hidden from API docs but callable).
save_history: bool
default `= False`
if True, will save the chat history to the browser's local storage and display
previous conversations in a side panel.
validator: Callable | None
default `= None`
a function that takes in the inputs and can optionally return a gr.validate()
object for each input.
| Initialization | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
chatinterface_random_responsechatinterface_streaming_echochatinterface_artifacts
[Creating A Chatbot Fast](../../guides/creating-a-chatbot-fast/)[Chatinterface
Examples](../../guides/chatinterface-examples/)[Agents And Tool
Usage](../../guides/agents-and-tool-usage/)[Chatbot Specific
Events](../../guides/chatbot-specific-events/)
| Demos | https://gradio.app/docs/gradio/chatinterface | Gradio - Chatinterface Docs |
Group is a layout element within Blocks which groups together children so
that they do not have any padding or margin between them.
| Description | https://gradio.app/docs/gradio/group | Gradio - Group Docs |
with gr.Group():
gr.Textbox(label="First")
gr.Textbox(label="Last")
| Example Usage | https://gradio.app/docs/gradio/group | Gradio - Group Docs |
Parameters ▼
visible: bool | Literal['hidden']
default `= True`
If False, group will be hidden.
elem_id: str | None
default `= None`
An optional string that is assigned as the id of this component in the HTML
DOM. Can be used for targeting CSS styles.
elem_classes: list[str] | str | None
default `= None`
An optional string or list of strings that are assigned as the class of this
component in the HTML DOM. Can be used for targeting CSS styles.
render: bool
default `= True`
If False, this layout will not be rendered in the Blocks context. Should be
used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= None`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
| Initialization | https://gradio.app/docs/gradio/group | Gradio - Group Docs |
Creates a textarea for users to enter string input or display string output
and also allows for the uploading of multimedia files.
| Description | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
**Using MultimodalTextbox as an input component.**
How MultimodalTextbox will pass its value to your function:
Type: `MultimodalValue | None`
Passes text value and list of file(s) as a `dict` into the function.
Example Code
import gradio as gr
def predict(
value: MultimodalValue | None
):
process value from the MultimodalTextbox component
return "prediction"
interface = gr.Interface(predict, gr.MultimodalTextbox(), gr.Textbox())
interface.launch()
**Using MultimodalTextbox as an output component**
How MultimodalTextbox expects you to return a value:
Type: `MultimodalValue | str | None`
Expects a `dict` with "text" and "files", both optional. The files array is a
list of file paths or URLs.
Example Code
import gradio as gr
def predict(text) -> MultimodalValue | str | None
process value to return to the MultimodalTextbox component
return value
interface = gr.Interface(predict, gr.Textbox(), gr.MultimodalTextbox())
interface.launch()
| Behavior | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
Parameters ▼
value: str | dict[str, str | list] | Callable | None
default `= None`
Default value to show in MultimodalTextbox. A string value, or a dictionary of
the form {"text": "sample text", "files": [{path: "files/file.jpg", orig_name:
"file.jpg", url: "http://image_url.jpg", size: 100}]}. If a function is
provided, the function will be called each time the app loads to set the
initial value of this component.
sources: list[Literal['upload', 'microphone']] | Literal['upload', 'microphone'] | None
default `= None`
A list of sources permitted. "upload" creates a button where users can click
to upload or drop files, "microphone" creates a microphone input. If None,
defaults to ["upload"].
file_types: list[str] | None
default `= None`
List of file extensions or types of files to be uploaded (e.g. ['image',
'.json', '.mp4']). "file" allows any file to be uploaded, "image" allows only
image files to be uploaded, "audio" allows only audio files to be uploaded,
"video" allows only video files to be uploaded, "text" allows only text files
to be uploaded.
file_count: Literal['single', 'multiple', 'directory']
default `= "single"`
if single, allows user to upload one file. If "multiple", user uploads
multiple files. If "directory", user uploads all files in selected directory.
Return type will be list for each file in case of "multiple" or "directory".
lines: int
default `= 1`
minimum number of line rows to provide in textarea.
max_lines: int
default `= 20`
maximum number of line rows to provide in textarea.
placeholder: str | None
default `= None`
placeholder hint to provide behind textarea.
label: str | I18nData | None
default `= None`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr. | Initialization | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
e`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr.Interface`, the label will be the
name of the parameter this component corresponds to.
info: str | I18nData | None
default `= None`
additional component description, appears below the label in smaller font.
Supports markdown / HTML syntax.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display label.
container: bool
default `= True`
If True, will place the component in a container - providing some extra
padding around the border.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
interactive: bool | None
default `= None`
if True, will be rendered as an editable textbox; if False, editing will be
disabled. If not provided, this is inferred based on | Initialization | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
cted first.
interactive: bool | None
default `= None`
if True, will be rendered as an editable textbox; if False, editing will be
disabled. If not provided, this is inferred based on whether the component is
used as an input or output.
visible: bool | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
elem_id: str | None
default `= None`
An optional string that is assigned as the id of this component in the HTML
DOM. Can be used for targeting CSS styles.
autofocus: bool
default `= False`
If True, will focus on the textbox when the page loads. Use this carefully, as
it can cause usability issues for sighted and non-sighted users.
autoscroll: bool
default `= True`
If True, will automatically scroll to the bottom of the textbox when the value
changes, unless the user scrolls up. If False, will not scroll to the bottom
of the textbox when the value changes.
elem_classes: list[str] | str | None
default `= None`
An optional list of strings that are assigned as the classes of this component
in the HTML DOM. Can be used for targeting CSS styles.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these | Initialization | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
rved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
text_align: Literal['left', 'right'] | None
default `= None`
How to align the text in the textbox, can be: "left", "right", or None
(default). If None, the alignment is left if `rtl` is False, or right if `rtl`
is True. Can only be changed if `type` is "text".
rtl: bool
default `= False`
If True and `type` is "text", sets the direction of the text to right-to-left
(cursor appears on the left of the text). Default is False, which renders
cursor on the right.
submit_btn: str | bool | None
default `= True`
If False, will not show a submit button. If a string, will use that string as
the submit button text.
stop_btn: str | bool | None
default `= False`
If True, will show a stop button (useful for streaming demos). If a string,
will use that string as the stop button text.
max_plain_text_length: int
default `= 1000`
Maximum length of plain text in the textbox. If the text exceeds this length,
the text will be pasted as a file. Default is 1000.
html_attributes: InputHTMLAttributes | None
default `= None`
An instance of gr.InputHTMLAttributes, which can be used to set HTML
attributes for the input/textarea elements. Example:
InputHTMLAttributes(autocorrect="off", spellcheck=False) to disable
autocorrect and spellcheck.
| Initialization | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
Shortcuts
gradio.MultimodalTextbox
Interface String Shortcut `"multimodaltextbox"`
Initialization Uses default values
| Shortcuts | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
chatbot_multimodal
| Demos | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The MultimodalTextbox component supports the following event listeners. Each
event listener takes the same parameters, which are listed in the Event
Parameters table below.
Listeners
MultimodalTextbox.change(fn, ···)
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.
MultimodalTextbox.input(fn, ···)
This listener is triggered when the user changes the value of the
MultimodalTextbox.
MultimodalTextbox.select(fn, ···)
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 <https://www.gradio.app/main/docs/gradio/eventdata> for
more details.
MultimodalTextbox.submit(fn, ···)
This listener is triggered when the user presses the Enter key while the
MultimodalTextbox is focused.
MultimodalTextbox.focus(fn, ···)
This listener is triggered when the MultimodalTextbox is focused.
MultimodalTextbox.blur(fn, ···)
This listener is triggered when the MultimodalTextbox is unfocused/blurred.
MultimodalTextbox.stop(fn, ···)
This listener is triggered when the user reaches the end of the media playing
in the MultimodalTextbox.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when thi | Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
d of the media playing
in the MultimodalTextbox.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. Often a machine learning
model's prediction function. Each parameter of the function corresponds to one
input component, and the function should return a single value or a tuple of
values, with each element in the tuple corresponding to one output component.
inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as inputs. If the function takes no inputs,
this should be an empty list.
outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as outputs. If the function returns no
outputs, this should be an empty list.
api_name: str | None
default `= None`
defines how the endpoint appears in the API docs. Can be a string or None. If
set to a string, the endpoint will be exposed in the API docs with the given
name. If None (default), the name of the function will be used as the API
endpoint.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runt | Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
ss: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
If True, will place the request on the queue, if the queue has been enabled.
If False, will not put this event on the queue, even if the queue has been
enabled. If None, will use the queue setting of the gradio app.
batch: bool
default `= False`
If True, then the function should process a batch of inputs, meaning that it
should accept a list of input values for each parameter. The lists should be
of equal length (and be up to length `max_batch_size`). The function is then
*required* to return a tuple of lists (even if there is only 1 output
component), with each list in the tuple corresponding to one output component.
max_batch_size: int
default `= 4`
Maximum number of inputs to batch together if this is called from the queue
(only relevant if batch=True)
preprocess: bool
default `= True`
If False, will not run preprocessing of component data before running 'fn'
(e.g. leaving it as a base64 string if this method is called with the `Image`
component).
postprocess: bool
default `= True`
If False, will not run postprocessing of component data before returning 'fn'
output to the browser.
cancels: dict[str, Any] | list[dict[str, Any]] | None
default `= None`
A list of other events to cancel when this listener is triggered. For example,
setting cancels=[click_event] will cancel the click_event | Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
dict[str, Any] | list[dict[str, Any]] | None
default `= None`
A list of other events to cancel when this listener is triggered. For example,
setting cancels=[click_event] will cancel the click_event, where click_event
is the return value of another components .click method. Functions that have
not yet run (or generators that are iterating) will be cancelled, but
functions that are currently running will be allowed to finish.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
api_visibility: Literal['public', 'private', 'undocumented']
default `= "public"`
controls the visibility and accessibility of this endpoint. Can be "public"
(shown in API docs and callable by clients), "private" (hidden from API docs
and not | Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
'undocumented']
default `= "public"`
controls the visibility and accessibility of this endpoint. Can be "public"
(shown in API docs and callable by clients), "private" (hidden from API docs
and not callable by clients), or "undocumented" (hidden from API docs but
callable by clients and via gr.load). If fn is None, api_visibility will
automatically be set to "private".
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
[Creating A Custom Chatbot With Blocks](../../guides/creating-a-custom-
chatbot-with-blocks/)
| Event Listeners | https://gradio.app/docs/gradio/multimodaltextbox | Gradio - Multimodaltextbox Docs |
This class allows you to pass custom error messages to the user. You can do
so by raising a gr.Error("custom message") anywhere in the code, and when that
line is executed the custom message will appear in a modal on the demo.
You can control for how long the error message is displayed with the
`duration` parameter. If it’s `None`, the message will be displayed forever
until the user closes it. If it’s a number, it will be shown for that many
seconds.
You can also hide the error modal from being shown in the UI by setting
`visible=False`.
Below is a demo of how different values of duration control the error,
info, and warning messages. You can see the code
[here](https://huggingface.co/spaces/freddyaboulton/gradio-error-
duration/blob/244331cf53f6b5fa2fd406ece3bf55c6ccb9f5f2/app.pyL17).
| Description | https://gradio.app/docs/gradio/error | Gradio - Error Docs |
import gradio as gr
def divide(numerator, denominator):
if denominator == 0:
raise gr.Error("Cannot divide by zero!")
gr.Interface(divide, ["number", "number"], "number").launch()
| Example Usage | https://gradio.app/docs/gradio/error | Gradio - Error Docs |
Parameters ▼
message: str
default `= "Error raised."`
The error message to be displayed to the user. Can be HTML, which will be
rendered in the modal.
duration: float | None
default `= 10`
The duration in seconds to display the error message. If None or 0, the error
message will be displayed until the user closes it.
visible: bool
default `= True`
Whether the error message should be displayed in the UI.
title: str
default `= "Error"`
The title to be displayed to the user at the top of the error modal.
print_exception: bool
default `= True`
Whether to print traceback of the error to the console when the error is
raised.
| Initialization | https://gradio.app/docs/gradio/error | Gradio - Error Docs |
calculatorblocks_chained_events
[Alerts](../../guides/alerts/)
| Demos | https://gradio.app/docs/gradio/error | Gradio - Error Docs |
Creates a color picker for user to select a color as string input. Can be
used as an input to pass a color value to a function or as an output to
display a color value.
| Description | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
**Using ColorPicker as an input component.**
How ColorPicker will pass its value to your function:
Type: `str | None`
Passes selected color value as a hex `str` into the function.
Example Code
import gradio as gr
def predict(
value: str | None
):
process value from the ColorPicker component
return "prediction"
interface = gr.Interface(predict, gr.ColorPicker(), gr.Textbox())
interface.launch()
**Using ColorPicker as an output component**
How ColorPicker expects you to return a value:
Type: `str | None`
Expects a hex `str` returned from function and sets color picker value to it.
Example Code
import gradio as gr
def predict(text) -> str | None
process value to return to the ColorPicker component
return value
interface = gr.Interface(predict, gr.Textbox(), gr.ColorPicker())
interface.launch()
| Behavior | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
Parameters ▼
value: str | Callable | None
default `= None`
default color hex code to provide in color picker. If a function is provided,
the function will be called each time the app loads to set the initial value
of this component.
label: str | I18nData | None
default `= None`
the label for this component, displayed above the component if `show_label` is
`True` and is also used as the header if there are a table of examples for
this component. If None and used in a `gr.Interface`, the label will be the
name of the parameter this component corresponds to.
info: str | I18nData | None
default `= None`
additional component description, appears below the label in smaller font.
Supports markdown / HTML syntax.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display label.
container: bool
default `= True`
If True, will place the component in a container - providing some extra
padding around the border.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a cer | Initialization | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
interactive: bool | None
default `= None`
if True, will be rendered as an editable color picker; if False, editing will
be disabled. If not provided, this is inferred based on whether the component
is used as an input or output.
visible: bool | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
elem_id: str | None
default `= None`
An optional string that is assigned as the id of this component in the HTML
DOM. Can be used for targeting CSS styles.
elem_classes: list[str] | str | None
default `= None`
An optional list of strings that are assigned as the classes of this component
in the HTML DOM. Can be used for targeting CSS styles.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of | Initialization | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
ender()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
| Initialization | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
Shortcuts
gradio.ColorPicker
Interface String Shortcut `"colorpicker"`
Initialization Uses default values
| Shortcuts | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
color_picker
| Demos | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The ColorPicker component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listeners
ColorPicker.change(fn, ···)
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.
ColorPicker.input(fn, ···)
This listener is triggered when the user changes the value of the ColorPicker.
ColorPicker.submit(fn, ···)
This listener is triggered when the user presses the Enter key while the
ColorPicker is focused.
ColorPicker.focus(fn, ···)
This listener is triggered when the ColorPicker is focused.
ColorPicker.blur(fn, ···)
This listener is triggered when the ColorPicker is unfocused/blurred.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. Often a machine learning
model's prediction function. Each parameter of the function corresponds to one
input component, and the function should return a single value or a tuple of
values, with each element in the tuple corresponding to one output component.
inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as inputs. If the function takes no inputs,
this should be an empty list.
outputs: Component | BlockContext | Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
| BlockContext] | None
default `= None`
List of gradio.components to use as inputs. If the function takes no inputs,
this should be an empty list.
outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as outputs. If the function returns no
outputs, this should be an empty list.
api_name: str | None
default `= None`
defines how the endpoint appears in the API docs. Can be a string or None. If
set to a string, the endpoint will be exposed in the API docs with the given
name. If None (default), the name of the function will be used as the API
endpoint.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
If True, will place the request on the queue, if the queue has been enabled.
If False, will not put this event on the queue, even if the queue has been
enabled. If None, will u | Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
bool
default `= True`
If True, will place the request on the queue, if the queue has been enabled.
If False, will not put this event on the queue, even if the queue has been
enabled. If None, will use the queue setting of the gradio app.
batch: bool
default `= False`
If True, then the function should process a batch of inputs, meaning that it
should accept a list of input values for each parameter. The lists should be
of equal length (and be up to length `max_batch_size`). The function is then
*required* to return a tuple of lists (even if there is only 1 output
component), with each list in the tuple corresponding to one output component.
max_batch_size: int
default `= 4`
Maximum number of inputs to batch together if this is called from the queue
(only relevant if batch=True)
preprocess: bool
default `= True`
If False, will not run preprocessing of component data before running 'fn'
(e.g. leaving it as a base64 string if this method is called with the `Image`
component).
postprocess: bool
default `= True`
If False, will not run postprocessing of component data before returning 'fn'
output to the browser.
cancels: dict[str, Any] | list[dict[str, Any]] | None
default `= None`
A list of other events to cancel when this listener is triggered. For example,
setting cancels=[click_event] will cancel the click_event, where click_event
is the return value of another components .click method. Functions that have
not yet run (or generators that are iterating) will be cancelled, but
functions that are currently running will be allowed to finish.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` event | Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
e()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
api_visibility: Literal['public', 'private', 'undocumented']
default `= "public"`
controls the visibility and accessibility of this endpoint. Can be "public"
(shown in API docs and callable by clients), "private" (hidden from API docs
and not callable by clients), or "undocumented" (hidden from API docs but
callable by clients and via gr.load). If fn is None, api_visibility will
automatically be set to "private".
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default | Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
| Event Listeners | https://gradio.app/docs/gradio/colorpicker | Gradio - Colorpicker Docs |
Creates an image component that can be used to upload images (as an input)
or display images (as an output).
| Description | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
**Using ImageSlider as an input component.**
How ImageSlider will pass its value to your function:
Type: `image_tuple | None`
Passes the uploaded image as a tuple of `numpy.array`, `PIL.Image` or `str`
filepath depending on `type`.
Example Code
import gradio as gr
def predict(
value: image_tuple | None
):
process value from the ImageSlider component
return "prediction"
interface = gr.Interface(predict, gr.ImageSlider(), gr.Textbox())
interface.launch()
**Using ImageSlider as an output component**
How ImageSlider expects you to return a value:
Type: `tuple[np.ndarray | PIL.Image.Image | str | Path | None, np.ndarray | PIL.Image.Image | str | Path | None] | None`
Expects a tuple of `numpy.array`, `PIL.Image`, or `str` or `pathlib.Path`
filepath to an image which is displayed.
Example Code
import gradio as gr
def predict(text) -> tuple[np.ndarray | PIL.Image.Image | str | Path | None, np.ndarray | PIL.Image.Image | str | Path | None] | None
process value to return to the ImageSlider component
return value
interface = gr.Interface(predict, gr.Textbox(), gr.ImageSlider())
interface.launch()
| Behavior | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
Parameters ▼
value: image_tuple | Callable | None
default `= None`
A tuple of PIL Image, numpy array, path or URL for the default value that
ImageSlider component is going to take, this pair of images should be of equal
size. If a function is provided, the function will be called each time the app
loads to set the initial value of this component.
format: str
default `= "webp"`
File format (e.g. "png" or "gif"). Used to save image if it does not already
have a valid format (e.g. if the image is being returned to the frontend as a
numpy array or PIL Image). The format should be supported by the PIL library.
Applies both when this component is used as an input or output. This parameter
has no effect on SVG files.
height: int | str | None
default `= None`
The height of the component, specified in pixels if a number is passed, or in
CSS units if a string is passed. This has no effect on the preprocessed tuple
of image file or numpy array, but will affect the displayed image.
width: int | str | None
default `= None`
The width of the component, specified in pixels if a number is passed, or in
CSS units if a string is passed. This has no effect on the preprocessed tuple
of image file or numpy array, but will affect the displayed image.
image_mode: Literal['1', 'L', 'P', 'RGB', 'RGBA', 'CMYK', 'YCbCr', 'LAB', 'HSV', 'I', 'F'] | None
default `= "RGB"`
The pixel format and color depth that the image should be loaded and
preprocessed as. "RGB" will load the image as a color image, or "L" as black-
and-white. See https://pillow.readthedocs.io/en/stable/handbook/concepts.html
for other supported image modes and their meaning. This parameter has no
effect on SVG or GIF files. If set to None, the image_mode will be inferred
from the image file types (e.g. "RGBA" for a .png image, "RGB" in most other
cases).
type: Literal['numpy', 'pil', 'filepath']
default `= "numpy"`
The for | Initialization | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
image_mode will be inferred
from the image file types (e.g. "RGBA" for a .png image, "RGB" in most other
cases).
type: Literal['numpy', 'pil', 'filepath']
default `= "numpy"`
The format the images are converted to before being passed into the prediction
function. "numpy" converts the images to numpy arrays with shape (height,
width, 3) and values from 0 to 255, "pil" converts the images to PIL image
objects, "filepath" passes str paths to temporary files containing the images.
To support animated GIFs in input, the `type` should be set to "filepath" or
"pil". To support SVGs, the `type` should be set to "filepath".
label: str | None
default `= None`
the label for this component. Appears above the component and is also used as
the header if there are a table of examples for this component. If None and
used in a `gr.Interface`, the label will be the name of the parameter this
component is assigned to.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display label.
buttons: list[Literal['download', 'fullscreen'] | Button] | None
default `= None`
A list of buttons to show in the top right corner of the component. Valid
options are "download", "fullscreen", or a gr.Button() instance. The
"download" button allows the user to download the image. The "fullscreen"
button allows the user to view the image in fullscreen mode. Custom
gr.Button() instances will appear in the toolbar w | Initialization | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
ce. The
"download" button allows the user to download the image. The "fullscreen"
button allows the user to view the image in fullscreen mode. Custom
gr.Button() instances will appear in the toolbar with their configured icon
and/or label, and clicking them will trigger any .click() events registered on
the button. by default, all of the built-in buttons are shown.
container: bool
default `= True`
If True, will place the component in a container - providing some extra
padding around the border.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
interactive: bool | None
default `= None`
if True, will allow users to upload and edit an image; if False, can only be
used to display images. If not provided, this is inferred based on whether the
component is used as an input or output.
visible: bool | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
elem_id: str | None
default `= None`
An optional string that is assigned as the id of this component in the HTML
DOM. Can be used for targeting CSS styles.
elem_classes: list[str] | str | None
default `= None`
An optional list of strings that are assigned as the classes of this component
in the HTML DOM. Can be used for targeting CSS styles.
render: bool
default `= T | Initialization | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
None
default `= None`
An optional list of strings that are assigned as the classes of this component
in the HTML DOM. Can be used for targeting CSS styles.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
slider_position: float
default `= 50`
The position of the slider as a percentage of the width of the image, between
0 and 100.
max_height: int
default `= 500`
The maximum height of the image.
| Initialization | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
Shortcuts
gradio.ImageSlider
Interface String Shortcut `"imageslider"`
Initialization Uses default values
| Shortcuts | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
imageslider
| Demos | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The ImageSlider component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listeners
ImageSlider.clear(fn, ···)
This listener is triggered when the user clears the ImageSlider using the
clear button for the component.
ImageSlider.change(fn, ···)
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.
ImageSlider.stream(fn, ···)
This listener is triggered when the user streams the ImageSlider.
ImageSlider.select(fn, ···)
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
<https://www.gradio.app/main/docs/gradio/eventdata> for more details.
ImageSlider.upload(fn, ···)
This listener is triggered when the user uploads a file into the ImageSlider.
ImageSlider.input(fn, ···)
This listener is triggered when the user changes the value of the ImageSlider.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. Often a machine learning
model's prediction function. Each parameter of the function corresponds to one
input component, and the function should return a single value or a tuple of
values, with e | Event Listeners | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
red. Often a machine learning
model's prediction function. Each parameter of the function corresponds to one
input component, and the function should return a single value or a tuple of
values, with each element in the tuple corresponding to one output component.
inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as inputs. If the function takes no inputs,
this should be an empty list.
outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as outputs. If the function returns no
outputs, this should be an empty list.
api_name: str | None
default `= None`
defines how the endpoint appears in the API docs. Can be a string or None. If
set to a string, the endpoint will be exposed in the API docs with the given
name. If None (default), the name of the function will be used as the API
endpoint.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Comp | Event Listeners | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
e upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
If True, will place the request on the queue, if the queue has been enabled.
If False, will not put this event on the queue, even if the queue has been
enabled. If None, will use the queue setting of the gradio app.
batch: bool
default `= False`
If True, then the function should process a batch of inputs, meaning that it
should accept a list of input values for each parameter. The lists should be
of equal length (and be up to length `max_batch_size`). The function is then
*required* to return a tuple of lists (even if there is only 1 output
component), with each list in the tuple corresponding to one output component.
max_batch_size: int
default `= 4`
Maximum number of inputs to batch together if this is called from the queue
(only relevant if batch=True)
preprocess: bool
default `= True`
If False, will not run preprocessing of component data before running 'fn'
(e.g. leaving it as a base64 string if this method is called with the `Image`
component).
postprocess: bool
default `= True`
If False, will not run postprocessing of component data before returning 'fn'
output to the browser.
cancels: dict[str, Any] | list[dict[str, Any]] | None
default `= None`
A list of other events to cancel when this listener is triggered. For example,
setting cancels=[click_event] will cancel the click_event, where click_event
is the return value of another components .click method. Functions that have
not yet run (or generators that are iterating) will be cancelled, but
functions that are currently running will be allow | Event Listeners | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
nt
is the return value of another components .click method. Functions that have
not yet run (or generators that are iterating) will be cancelled, but
functions that are currently running will be allowed to finish.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
api_visibility: Literal['public', 'private', 'undocumented']
default `= "public"`
controls the visibility and accessibility of this endpoint. Can be "public"
(shown in API docs and callable by clients), "private" (hidden from API docs
and not callable by clients), or "undocumented" (hidden from API docs but
callable by clients and via gr.load). If fn is None, api_visibility will
automatically be set to "private".
time_limit: int | None
def | Event Listeners | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
ts), or "undocumented" (hidden from API docs but
callable by clients and via gr.load). If fn is None, api_visibility will
automatically be set to "private".
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
| Event Listeners | https://gradio.app/docs/gradio/imageslider | Gradio - Imageslider Docs |
Creates a chatbot that displays user-submitted messages and responses.
Supports a subset of Markdown including bold, italics, code, tables. Also
supports audio/video/image files, which are displayed in the Chatbot, and
other kinds of files which are displayed as links. This component is usually
used as an output component.
| Description | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
The Chatbot component accepts a list of messages, where each message is a
dictionary with `role` and `content` keys. This format is compatible with the
message format expected by most LLM APIs (OpenAI, Claude, HuggingChat, etc.),
making it easy to pipe model outputs directly into the component.
The `role` key should be either `'user'` or `'assistant'`, and the `content`
key can be a string (rendered as markdown/HTML) or a Gradio component (useful
for displaying files, images, plots, and other media).
As an example:
import gradio as gr
history = [
{"role": "assistant", "content": "I am happy to provide you that report and plot."},
{"role": "assistant", "content": gr.Plot(value=make_plot_from_file('quaterly_sales.txt'))}
]
with gr.Blocks() as demo:
gr.Chatbot(history)
demo.launch()
For convenience, you can use the `ChatMessage` dataclass so that your text
editor can give you autocomplete hints and typechecks.
import gradio as gr
history = [
gr.ChatMessage(role="assistant", content="How can I help you?"),
gr.ChatMessage(role="user", content="Can you make me a plot of quarterly sales?"),
gr.ChatMessage(role="assistant", content="I am happy to provide you that report and plot.")
]
with gr.Blocks() as demo:
gr.Chatbot(history)
demo.launch()
| Behavior | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
Parameters ▼
value: list[MessageDict | Message] | Callable | None
default `= None`
Default list of messages to show in chatbot, where each message is of the
format {"role": "user", "content": "Help me."}. Role can be one of "user",
"assistant", or "system". Content should be either text, or media passed as a
Gradio component, e.g. {"content": gr.Image("lion.jpg")}. If a function is
provided, the function will be called each time the app loads to set the
initial value of this component.
label: str | I18nData | None
default `= None`
the label for this component. Appears above the component and is also used as
the header if there are a table of examples for this component. If None and
used in a `gr.Interface`, the label will be the name of the parameter this
component is assigned to.
every: Timer | float | None
default `= None`
Continously calls `value` to recalculate it if `value` is a function (has no
effect otherwise). Can provide a Timer whose tick resets `value`, or a float
that provides the regular interval for the reset Timer.
inputs: Component | list[Component] | set[Component] | None
default `= None`
Components that are used as inputs to calculate `value` if `value` is a
function (has no effect otherwise). `value` is recalculated any time the
inputs change.
show_label: bool | None
default `= None`
if True, will display label.
container: bool
default `= True`
If True, will place the component in a container - providing some extra
padding around the border.
scale: int | None
default `= None`
relative size compared to adjacent Components. For example if Components A and
B are in a Row, and A has scale=2, and B has scale=1, A will be twice as wide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if | Initialization | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
ide
as B. Should be an integer. scale applies in Rows, and to top-level Components
in Blocks where fill_height=True.
min_width: int
default `= 160`
minimum pixel width, will wrap if not sufficient screen space to satisfy this
value. If a certain scale value results in this Component being narrower than
min_width, the min_width parameter will be respected first.
visible: bool | Literal['hidden']
default `= True`
If False, component will be hidden. If "hidden", component will be visually
hidden and not take up space in the layout but still exist in the DOM
elem_id: str | None
default `= None`
An optional string that is assigned as the id of this component in the HTML
DOM. Can be used for targeting CSS styles.
elem_classes: list[str] | str | None
default `= None`
An optional list of strings that are assigned as the classes of this component
in the HTML DOM. Can be used for targeting CSS styles.
autoscroll: bool
default `= True`
If True, will automatically scroll to the bottom of the textbox when the value
changes, unless the user scrolls up. If False, will not scroll to the bottom
of the textbox when the value changes.
render: bool
default `= True`
If False, component will not render be rendered in the Blocks context. Should
be used if the intention is to assign event listeners now but render the
component later.
key: int | str | tuple[int | str, ...] | None
default `= None`
in a gr.render, Components with the same key across re-renders are treated as
the same component, not a new component. Properties set in 'preserved_by_key'
are not reset across a re-render.
preserved_by_key: list[str] | str | None
default `= "value"`
A list of parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they hav | Initialization | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
parameters from this component's constructor. Inside a gr.render()
function, if a component is re-rendered with the same key, these (and only
these) parameters will be preserved in the UI (if they have been changed by
the user or an event listener) instead of re-rendered based on the values
provided during constructor.
height: int | str | None
default `= 400`
The height of the component, specified in pixels if a number is passed, or in
CSS units if a string is passed. If messages exceed the height, the component
will scroll.
resizable: bool
default `= False`
If True, the user of the Gradio app can resize the chatbot by dragging the
bottom right corner.
max_height: int | str | None
default `= None`
The maximum height of the component, specified in pixels if a number is
passed, or in CSS units if a string is passed. If messages exceed the height,
the component will scroll. If messages are shorter than the height, the
component will shrink to fit the content. Will not have any effect if `height`
is set and is smaller than `max_height`.
min_height: int | str | None
default `= None`
The minimum height of the component, specified in pixels if a number is
passed, or in CSS units if a string is passed. If messages exceed the height,
the component will expand to fit the content. Will not have any effect if
`height` is set and is larger than `min_height`.
editable: Literal['user', 'all'] | None
default `= None`
Allows user to edit messages in the chatbot. If set to "user", allows editing
of user messages. If set to "all", allows editing of assistant messages as
well.
latex_delimiters: list[dict[str, str | bool]] | None
default `= None`
A list of dicts of the form {"left": open delimiter (str), "right": close
delimiter (str), "display": whether to display in newline (bool)} that will be
used to render LaTeX expressions. If not provided, `latex_delimiters` is set
to `[{ " | Initialization | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
pen delimiter (str), "right": close
delimiter (str), "display": whether to display in newline (bool)} that will be
used to render LaTeX expressions. If not provided, `latex_delimiters` is set
to `[{ "left": "$$", "right": "$$", "display": True }]`, so only expressions
enclosed in $$ delimiters will be rendered as LaTeX, and in a new line. Pass
in an empty list to disable LaTeX rendering. For more information, see the
[KaTeX documentation](https://katex.org/docs/autorender.html).
rtl: bool
default `= False`
If True, sets the direction of the rendered text to right-to-left. Default is
False, which renders text left-to-right.
buttons: list[Literal['share', 'copy', 'copy_all'] | Button] | None
default `= None`
A list of buttons to show in the top right corner of the component. Valid
options are "share", "copy", "copy_all", or a gr.Button() instance. The
"share" button allows the user to share outputs to Hugging Face Spaces
Discussions. The "copy" button makes a copy button appear next to each
individual chatbot message. The "copy_all" button appears at the component
level and allows the user to copy all chatbot messages. Custom gr.Button()
instances will appear in the toolbar with their configured icon and/or label,
and clicking them will trigger any .click() events registered on the button.
By default, "share" and "copy_all" buttons are shown.
watermark: str | None
default `= None`
If provided, this text will be appended to the end of messages copied from the
chatbot, after a blank line. Useful for indicating that the message is
generated by an AI model.
avatar_images: tuple[str | Path | None, str | Path | None] | None
default `= None`
Tuple of two avatar image paths or URLs for user and bot (in that order). Pass
None for either the user or bot image to skip. Must be within the working
directory of the Gradio app or an external URL.
sanitize_html: bool
default `= True`
If False, w | Initialization | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
rder). Pass
None for either the user or bot image to skip. Must be within the working
directory of the Gradio app or an external URL.
sanitize_html: bool
default `= True`
If False, will disable HTML sanitization for chatbot messages. This is not
recommended, as it can lead to security vulnerabilities.
render_markdown: bool
default `= True`
If False, will disable Markdown rendering for chatbot messages.
feedback_options: list[str] | tuple[str, ...] | None
default `= ('Like', 'Dislike')`
A list of strings representing the feedback options that will be displayed to
the user. The exact case-sensitive strings "Like" and "Dislike" will render as
thumb icons, but any other choices will appear under a separate flag icon.
feedback_value: list[str | None] | None
default `= None`
A list of strings representing the feedback state for entire chat. Only works
when type="messages". Each entry in the list corresponds to that assistant
message, in order, and the value is the feedback given (e.g. "Like",
"Dislike", or any custom feedback option) or None if no feedback was given for
that message.
line_breaks: bool
default `= True`
If True (default), will enable Github-flavored Markdown line breaks in chatbot
messages. If False, single new lines will be ignored. Only applies if
`render_markdown` is True.
layout: Literal['panel', 'bubble'] | None
default `= None`
If "panel", will display the chatbot in a llm style layout. If "bubble", will
display the chatbot with message bubbles, with the user and bot messages on
alterating sides. Will default to "bubble".
placeholder: str | None
default `= None`
a placeholder message to display in the chatbot when it is empty. Centered
vertically and horizontally in the Chatbot. Supports Markdown and HTML. If
None, no placeholder is displayed.
examples: list[ExampleMessage] | None
default `= None`
A list of ex | Initialization | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
ered
vertically and horizontally in the Chatbot. Supports Markdown and HTML. If
None, no placeholder is displayed.
examples: list[ExampleMessage] | None
default `= None`
A list of example messages to display in the chatbot before any user/assistant
messages are shown. Each example should be a dictionary with an optional
"text" key representing the message that should be populated in the Chatbot
when clicked, an optional "files" key, whose value should be a list of files
to populate in the Chatbot, an optional "icon" key, whose value should be a
filepath or URL to an image to display in the example box, and an optional
"display_text" key, whose value should be the text to display in the example
box. If "display_text" is not provided, the value of "text" will be displayed.
allow_file_downloads: <class 'inspect._empty'>
default `= True`
If True, will show a download button for chatbot messages that contain media.
Defaults to True.
group_consecutive_messages: bool
default `= True`
If True, will display consecutive messages from the same role in the same
bubble. If False, will display each message in a separate bubble. Defaults to
True.
allow_tags: list[str] | bool
default `= True`
If a list of tags is provided, these tags will be preserved in the output
chatbot messages, even if `sanitize_html` is `True`. For example, if this list
is ["thinking"], the tags `<thinking>` and `</thinking>` will not be removed.
If True, all custom tags (non-standard HTML tags) will be preserved. If False,
no tags will be preserved. Default value is 'True'.
reasoning_tags: list[tuple[str, str]] | None
default `= None`
If provided, a list of tuples of (open_tag, close_tag) strings. Any text
between these tags will be extracted and displayed in a separate collapsible
message with metadata={"title": "Reasoning"}. For example, [("<thinking>",
"</thinking>")] will extract content between <thinking> and </thi | Initialization | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
s will be extracted and displayed in a separate collapsible
message with metadata={"title": "Reasoning"}. For example, [("<thinking>",
"</thinking>")] will extract content between <thinking> and </thinking> tags.
Each thinking block will be displayed as a separate collapsible message before
the main response. If None (default), no automatic extraction is performed.
like_user_message: bool
default `= False`
If True, will show like/dislike buttons for user messages as well. Defaults to
False.
| Initialization | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
Shortcuts
gradio.Chatbot
Interface String Shortcut `"chatbot"`
Initialization Uses default values
| Shortcuts | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
**Displaying Thoughts/Tool Usage**
You can provide additional metadata regarding any tools used to generate the
response. This is useful for displaying the thought process of LLM agents. For
example,
def generate_response(history):
history.append(
ChatMessage(role="assistant",
content="The weather API says it is 20 degrees Celcius in New York.",
metadata={"title": "🛠️ Used tool Weather API"})
)
return history
Would be displayed as following:
You can also specify metadata with a plain python dictionary,
def generate_response(history):
history.append(
dict(role="assistant",
content="The weather API says it is 20 degrees Celcius in New York.",
metadata={"title": "🛠️ Used tool Weather API"})
)
return history
**Using Gradio Components Inside`gr.Chatbot`**
The `Chatbot` component supports using many of the core Gradio components
(such as `gr.Image`, `gr.Plot`, `gr.Audio`, and `gr.HTML`) inside of the
chatbot. Simply include one of these components as the `content` of a message.
Here’s an example:
import gradio as gr
def load():
return [
{"role": "user", "content": "Can you show me some media?"},
{"role": "assistant", "content": "Here's an audio clip:"},
{"role": "assistant", "content": gr.Audio("https://github.com/gradio-app/gradio/raw/main/gradio/media_assets/audio/audio_sample.wav")},
{"role": "assistant", "content": "And here's a video:"},
{"role": "assistant", "content": gr.Video("https://github.com/gradio-app/gradio/raw/main/gradio/media_assets/videos/world.mp4")}
]
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
button = gr.Button("Load | Examples | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
deo("https://github.com/gradio-app/gradio/raw/main/gradio/media_assets/videos/world.mp4")}
]
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
button = gr.Button("Load audio and video")
button.click(load, None, chatbot)
demo.launch()
| Examples | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
chatbot_simplechatbot_streamingchatbot_with_toolschatbot_core_components
| Demos | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
Description
Event listeners allow you to respond to user interactions with the UI
components you've defined in a Gradio Blocks app. When a user interacts with
an element, such as changing a slider value or uploading an image, a function
is called.
Supported Event Listeners
The Chatbot component supports the following event listeners. Each event
listener takes the same parameters, which are listed in the Event Parameters
table below.
Listeners
Chatbot.change(fn, ···)
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.
Chatbot.select(fn, ···)
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
<https://www.gradio.app/main/docs/gradio/eventdata> for more details.
Chatbot.like(fn, ···)
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.
Chatbot.retry(fn, ···)
This listener is triggered when the user clicks the retry button in the
chatbot message.
Chatbot.undo(fn, ···)
This listener is triggered when the user clicks the undo button in the chatbot
message.
Chatbot.example_select(fn, ···)
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.
| Event Listeners | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
s 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.
Chatbot.option_select(fn, ···)
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.
Chatbot.clear(fn, ···)
This listener is triggered when the user clears the Chatbot using the clear
button for the component.
Chatbot.copy(fn, ···)
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
Chatbot.edit(fn, ···)
This listener is triggered when the user edits the Chatbot (e.g. image) using
the built-in editor.
Event Parameters
Parameters ▼
fn: Callable | None | Literal['decorator']
default `= "decorator"`
the function to call when this event is triggered. Often a machine learning
model's prediction function. Each parameter of the function corresponds to one
input component, and the function should return a single value or a tuple of
values, with each element in the tuple corresponding to one output component.
inputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as inputs. If the function takes no inputs,
this should be an empty list.
outputs: Component | BlockContext | list[Component | BlockContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as outputs. If the function returns no
outputs, this should be an empty list.
api_ | Event Listeners | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
ckContext] | Set[Component | BlockContext] | None
default `= None`
List of gradio.components to use as outputs. If the function returns no
outputs, this should be an empty list.
api_name: str | None
default `= None`
defines how the endpoint appears in the API docs. Can be a string or None. If
set to a string, the endpoint will be exposed in the API docs with the given
name. If None (default), the name of the function will be used as the API
endpoint.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
scroll_to_output: bool
default `= False`
If True, will scroll to output component on completion
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
show_progress_on: Component | list[Component] | None
default `= None`
Component or list of components to show the progress animation on. If None,
will show the progress animation on all of the output components.
queue: bool
default `= True`
If True, will place the request on the queue, if the queue has been enabled.
If False, will not put this event on the queue, even if the queue has been
enabled. If None, will use the queue setting of the gradio app.
batch: bool
default `= False`
If True, then the function should process a batch of inputs, meaning that it
should accept a list of input values for each parameter. | Event Listeners | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
f the gradio app.
batch: bool
default `= False`
If True, then the function should process a batch of inputs, meaning that it
should accept a list of input values for each parameter. The lists should be
of equal length (and be up to length `max_batch_size`). The function is then
*required* to return a tuple of lists (even if there is only 1 output
component), with each list in the tuple corresponding to one output component.
max_batch_size: int
default `= 4`
Maximum number of inputs to batch together if this is called from the queue
(only relevant if batch=True)
preprocess: bool
default `= True`
If False, will not run preprocessing of component data before running 'fn'
(e.g. leaving it as a base64 string if this method is called with the `Image`
component).
postprocess: bool
default `= True`
If False, will not run postprocessing of component data before returning 'fn'
output to the browser.
cancels: dict[str, Any] | list[dict[str, Any]] | None
default `= None`
A list of other events to cancel when this listener is triggered. For example,
setting cancels=[click_event] will cancel the click_event, where click_event
is the return value of another components .click method. Functions that have
not yet run (or generators that are iterating) will be cancelled, but
functions that are currently running will be allowed to finish.
trigger_mode: Literal['once', 'multiple', 'always_last'] | None
default `= None`
If "once" (default for all events except `.change()`) would not allow any
submissions while an event is pending. If set to "multiple", unlimited
submissions are allowed while pending, and "always_last" (default for
`.change()` and `.key_up()` events) would allow a second submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
metho | Event Listeners | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
d submission after the
pending event is complete.
js: str | Literal[True] | None
default `= None`
Optional frontend js method to run before running 'fn'. Input arguments for js
method are values of 'inputs' and 'outputs', return should be a list of values
for output components.
concurrency_limit: int | None | Literal['default']
default `= "default"`
If set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`Blocks.queue()`, which itself is 1 by default).
concurrency_id: str | None
default `= None`
If set, this is the id of the concurrency group. Events with the same
concurrency_id will be limited by the lowest set concurrency_limit.
api_visibility: Literal['public', 'private', 'undocumented']
default `= "public"`
controls the visibility and accessibility of this endpoint. Can be "public"
(shown in API docs and callable by clients), "private" (hidden from API docs
and not callable by clients), or "undocumented" (hidden from API docs but
callable by clients and via gr.load). If fn is None, api_visibility will
automatically be set to "private".
time_limit: int | None
default `= None`
stream_every: float
default `= 0.5`
key: int | str | tuple[int | str, ...] | None
default `= None`
A unique key for this event listener to be used in @gr.render(). If set, this
value identifies an event as identical across re-renders when the key is
identical.
validator: Callable | None
default `= None`
Optional validation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The valid | Event Listeners | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
lidation function to run before the main function. If provided,
this function will be executed first with queue=False, and only if it
completes successfully will the main function be called. The validator
receives the same inputs as the main function and should return a
`gr.validate()` for each input value.
| Event Listeners | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
Helper Classes | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs | |
gradio.ChatMessage(···)
Description
A dataclass that represents a message in the Chatbot component (with
type="messages"). The only required field is `content`. The value of
`gr.Chatbot` is a list of these dataclasses.
Parameters ▼
content: MessageContent | list[MessageContent]
The content of the message. Can be a string, a file dict, a gradio component,
or a list of these types to group these messages together.
role: Literal['user', 'assistant', 'system']
default `= "assistant"`
The role of the message, which determines the alignment of the message in the
chatbot. Can be "user", "assistant", or "system". Defaults to "assistant".
metadata: MetadataDict
default `= _HAS_DEFAULT_FACTORY_CLASS()`
The metadata of the message, which is used to display intermediate thoughts /
tool usage. Should be a dictionary with the following keys: "title" (required
to display the thought), and optionally: "id" and "parent_id" (to nest
thoughts), "duration" (to display the duration of the thought), "status" (to
display the status of the thought).
options: list[OptionDict]
default `= _HAS_DEFAULT_FACTORY_CLASS()`
The options of the message. A list of Option objects, which are dictionaries
with the following keys: "label" (the text to display in the option), and
optionally "value" (the value to return when the option is selected if
different from the label).
| ChatMessage | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
A typed dictionary to represent metadata for a message in the Chatbot
component. An instance of this dictionary is used for the `metadata` field in
a ChatMessage when the chat message should be displayed as a thought.
Keys ▼
title: str
The title of the 'thought' message. Only required field.
id: int | str
The ID of the message. Only used for nested thoughts. Nested thoughts can be
nested by setting the parent_id to the id of the parent thought.
parent_id: int | str
The ID of the parent message. Only used for nested thoughts.
log: str
A string message to display next to the thought title in a subdued font.
duration: float
The duration of the message in seconds. Appears next to the thought title in a
subdued font inside a parentheses.
status: Literal['pending', 'done']
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.
| MetadataDict | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
A typed dictionary to represent an option in a ChatMessage. A list of these
dictionaries is used for the `options` field in a ChatMessage.
Keys ▼
value: str
The value to return when the option is selected.
label: str
The text to display in the option, if different from the value.
[Chatbot Specific Events](../../guides/chatbot-specific-
events/)[Conversational Chatbot](../../guides/conversational-
chatbot/)[Creating A Chatbot Fast](../../guides/creating-a-chatbot-
fast/)[Creating A Custom Chatbot With Blocks](../../guides/creating-a-custom-
chatbot-with-blocks/)[Agents And Tool Usage](../../guides/agents-and-tool-
usage/)
| OptionDict | https://gradio.app/docs/gradio/chatbot | Gradio - Chatbot Docs |
Interface is Gradio's main high-level class, and allows you to create a
web-based GUI / demo around a machine learning model (or any Python function)
in a few lines of code. You must specify three parameters: (1) the function to
create a GUI for (2) the desired input components and (3) the desired output
components. Additional parameters can be used to control the appearance and
behavior of the demo.
| Description | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
import gradio as gr
def image_classifier(inp):
return {'cat': 0.3, 'dog': 0.7}
demo = gr.Interface(fn=image_classifier, inputs="image", outputs="label")
demo.launch()
| Example Usage | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
Parameters ▼
fn: Callable
the function to wrap an interface around. Often a machine learning model's
prediction function. Each parameter of the function corresponds to one input
component, and the function should return a single value or a tuple of values,
with each element in the tuple corresponding to one output component.
inputs: str | Component | list[str | Component] | None
a single Gradio component, or list of Gradio components. Components can either
be passed as instantiated objects, or referred to by their string shortcuts.
The number of input components should match the number of parameters in fn. If
set to None, then only the output components will be displayed.
outputs: str | Component | list[str | Component] | None
a single Gradio component, or list of Gradio components. Components can either
be passed as instantiated objects, or referred to by their string shortcuts.
The number of output components should match the number of values returned by
fn. If set to None, then only the input components will be displayed.
examples: list[Any] | list[list[Any]] | str | None
default `= None`
sample inputs for the function; if provided, appear below the UI components
and can be clicked to populate the interface. Should be nested list, in which
the outer list consists of samples and each inner list consists of an input
corresponding to each input component. A string path to a directory of
examples can also be provided, but it should be within the directory with the
python file running the gradio app. If there are multiple input components and
a directory is provided, a log.csv file must be present in the directory to
link corresponding inputs.
cache_examples: bool | None
default `= None`
If True, caches examples in the server for fast runtime in examples. If
"lazy", then examples are cached (for all users of the app) after their first
use (by any user of the app). If None, will use | Initialization | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
If True, caches examples in the server for fast runtime in examples. If
"lazy", then examples are cached (for all users of the app) after their first
use (by any user of the app). If None, will use the GRADIO_CACHE_EXAMPLES
environment variable, which should be either "true" or "false". In HuggingFace
Spaces, this parameter defaults to True (as long as `fn` and `outputs` are
also provided). Note that examples are cached separately from Gradio's queue()
so certain features, such as gr.Progress(), gr.Info(), gr.Warning(), etc. will
not be displayed in Gradio's UI for cached examples.
cache_mode: Literal['eager', 'lazy'] | None
default `= None`
if "lazy", examples are cached after their first use. If "eager", all examples
are cached at app launch. If None, will use the GRADIO_CACHE_MODE environment
variable if defined, or default to "eager". In HuggingFace Spaces, this
parameter defaults to "eager" except for ZeroGPU Spaces, in which case it
defaults to "lazy".
examples_per_page: int
default `= 10`
if examples are provided, how many to display per page.
example_labels: list[str] | None
default `= None`
a list of labels for each example. If provided, the length of this list should
be the same as the number of examples, and these labels will be used in the UI
instead of rendering the example values.
preload_example: int | Literal[False]
default `= 0`
If an integer is provided (and examples are being cached eagerly and none of
the input components have a developer-provided `value`), the example at that
index in the examples list will be preloaded when the Gradio app is first
loaded. If False, no example will be preloaded.
live: bool
default `= False`
whether the interface should automatically rerun if any of the inputs change.
title: str | I18nData | None
default `= None`
a title for the interface; if provided, appears above the input and output
components in larg | Initialization | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
tically rerun if any of the inputs change.
title: str | I18nData | None
default `= None`
a title for the interface; if provided, appears above the input and output
components in large font. Also used as the tab title when opened in a browser
window.
description: str | None
default `= None`
a description for the interface; if provided, appears above the input and
output components and beneath the title in regular font. Accepts Markdown and
HTML content.
article: str | None
default `= None`
an expanded article explaining the interface; if provided, appears below the
input and output components in regular font. Accepts Markdown and HTML
content. If it is an HTTP(S) link to a downloadable remote file, the content
of this file is displayed.
flagging_mode: Literal['never'] | Literal['auto'] | Literal['manual'] | None
default `= None`
one of "never", "auto", or "manual". If "never" or "auto", users will not see
a button to flag an input and output. If "manual", users will see a button to
flag. If "auto", every input the user submits will be automatically flagged,
along with the generated output. If "manual", both the input and outputs are
flagged when the user clicks flag button. This parameter can be set with
environmental variable GRADIO_FLAGGING_MODE; otherwise defaults to "manual".
flagging_options: list[str] | list[tuple[str, str]] | None
default `= None`
if provided, allows user to select from the list of options when flagging.
Only applies if flagging_mode is "manual". Can either be a list of tuples of
the form (label, value), where label is the string that will be displayed on
the button and value is the string that will be stored in the flagging CSV; or
it can be a list of strings ["X", "Y"], in which case the values will be the
list of strings and the labels will ["Flag as X", "Flag as Y"], etc.
flagging_dir: str
default `= ".gradio/flagged"`
path to the di | Initialization | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
s ["X", "Y"], in which case the values will be the
list of strings and the labels will ["Flag as X", "Flag as Y"], etc.
flagging_dir: str
default `= ".gradio/flagged"`
path to the directory where flagged data is stored. If the directory does not
exist, it will be created.
flagging_callback: FlaggingCallback | None
default `= None`
either None or an instance of a subclass of FlaggingCallback which will be
called when a sample is flagged. If set to None, an instance of
gradio.flagging.CSVLogger will be created and logs will be saved to a local
CSV file in flagging_dir. Default to None.
analytics_enabled: bool | None
default `= None`
whether to allow basic telemetry. If None, will use GRADIO_ANALYTICS_ENABLED
environment variable if defined, or default to True.
batch: bool
default `= False`
if True, then the function should process a batch of inputs, meaning that it
should accept a list of input values for each parameter. The lists should be
of equal length (and be up to length `max_batch_size`). The function is then
*required* to return a tuple of lists (even if there is only 1 output
component), with each list in the tuple corresponding to one output component.
max_batch_size: int
default `= 4`
the maximum number of inputs to batch together if this is called from the
queue (only relevant if batch=True)
api_visibility: Literal['public', 'private', 'undocumented']
default `= "public"`
Controls the visibility of the prediction endpoint. Can be "public" (shown in
API docs and callable), "private" (hidden from API docs and not callable), or
"undocumented" (hidden from API docs but callable).
api_name: str | None
default `= None`
defines how the prediction endpoint appears in the API docs. Can be a string
or None. If set to a string, the endpoint will be exposed in the API docs with
the given name. If None, the name of the function will be used.
| Initialization | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
ion endpoint appears in the API docs. Can be a string
or None. If set to a string, the endpoint will be exposed in the API docs with
the given name. If None, the name of the function will be used.
api_description: str | None | Literal[False]
default `= None`
Description of the API endpoint. Can be a string, None, or False. If set to a
string, the endpoint will be exposed in the API docs with the given
description. If None, the function's docstring will be used as the API
endpoint description. If False, then no description will be displayed in the
API docs.
allow_duplication: bool
default `= False`
if True, then will show a 'Duplicate Spaces' button on Hugging Face Spaces.
concurrency_limit: int | None | Literal['default']
default `= "default"`
if set, this is the maximum number of this event that can be running
simultaneously. Can be set to None to mean no concurrency_limit (any number of
this event can be running simultaneously). Set to "default" to use the default
concurrency limit (defined by the `default_concurrency_limit` parameter in
`.queue()`, which itself is 1 by default).
additional_inputs: str | Component | list[str | Component] | None
default `= None`
a single Gradio component, or list of Gradio components. Components can either
be passed as instantiated objects, or referred to by their string shortcuts.
These components will be rendered in an accordion below the main input
components. By default, no additional input components will be displayed.
additional_inputs_accordion: str | Accordion | None
default `= None`
if a string is provided, this is the label of the `gr.Accordion` to use to
contain additional inputs. A `gr.Accordion` object can be provided as well to
configure other properties of the container holding the additional inputs.
Defaults to a `gr.Accordion(label="Additional Inputs", open=False)`. This
parameter is only used if `additional_inputs` is provide | Initialization | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
gure other properties of the container holding the additional inputs.
Defaults to a `gr.Accordion(label="Additional Inputs", open=False)`. This
parameter is only used if `additional_inputs` is provided.
submit_btn: str | Button
default `= "Submit"`
the button to use for submitting inputs. Defaults to a `gr.Button("Submit",
variant="primary")`. This parameter does not apply if the Interface is output-
only, in which case the submit button always displays "Generate". Can be set
to a string (which becomes the button label) or a `gr.Button` object (which
allows for more customization).
stop_btn: str | Button
default `= "Stop"`
the button to use for stopping the interface. Defaults to a `gr.Button("Stop",
variant="stop", visible=False)`. Can be set to a string (which becomes the
button label) or a `gr.Button` object (which allows for more customization).
clear_btn: str | Button | None
default `= "Clear"`
the button to use for clearing the inputs. Defaults to a `gr.Button("Clear",
variant="secondary")`. Can be set to a string (which becomes the button label)
or a `gr.Button` object (which allows for more customization). Can be set to
None, which hides the button.
delete_cache: tuple[int, int] | None
default `= None`
a tuple corresponding [frequency, age] both expressed in number of seconds.
Every `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. For example, setting this to (86400, 86400) will delete temporary
files every day. The cache will be deleted entirely when the server restarts.
If None, no cache deletion will occur.
show_progress: Literal['full', 'minimal', 'hidden']
default `= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" o | Initialization | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
`= "full"`
how to show the progress animation while event is running: "full" shows a
spinner which covers the output component area as well as a runtime display in
the upper right corner, "minimal" only shows the runtime display, "hidden"
shows no progress animation at all
fill_width: bool
default `= False`
whether to horizontally expand to fill container fully. If False, centers and
constrains app to a maximum width.
time_limit: int | None
default `= 30`
The time limit for the stream to run. Default is 30 seconds. Parameter only
used for streaming images or audio if the interface is live and the input
components are set to "streaming=True".
stream_every: float
default `= 0.5`
The latency (in seconds) at which stream chunks are sent to the backend.
Defaults to 0.5 seconds. Parameter only used for streaming images or audio if
the interface is live and the input components are set to "streaming=True".
deep_link: str | DeepLinkButton | bool | None
default `= None`
a string or `gr.DeepLinkButton` object that creates a unique URL you can use
to share your app and all components **as they currently are** with others.
Automatically enabled on Hugging Face Spaces unless explicitly set to False.
validator: Callable | None
default `= None`
a function that takes in the inputs and can optionally return a gr.validate()
object for each input.
| Initialization | https://gradio.app/docs/gradio/interface | Gradio - Interface Docs |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.