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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 5.42.0
+sdk_version: 5.43.0
 app_file: run.py
 pinned: false
 hf_oauth: true

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: plot_guide_filters"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/plot_guide_filters/data.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from data import df\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        origin = gr.Dropdown([\"All\", \"DFW\", \"DAL\", \"HOU\"], value=\"All\", label=\"Origin\")\n", "        destination = gr.Dropdown([\"All\", \"JFK\", \"LGA\", \"EWR\"], value=\"All\", label=\"Destination\")\n", "        max_price = gr.Slider(0, 1000, value=1000, label=\"Max Price\")\n", "\n", "    def filtered_data(origin, destination, max_price):\n", "        _df = df[df[\"price\"] <= max_price]\n", "        if origin != \"All\":\n", "            _df = _df[_df[\"origin\"] == origin]\n", "        if destination != \"All\":\n", "            _df = _df[_df[\"destination\"] == destination]\n", "        return _df\n", "\n", "    gr.ScatterPlot(filtered_data, x=\"time\", y=\"price\", inputs=[origin, destination, max_price])\n", "    \n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: plot_guide_filters"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["# Downloading files from the demo repo\n", "import os\n", "!wget -q https://github.com/gradio-app/gradio/raw/main/demo/plot_guide_filters/data.py"]}, {"cell_type": "code", "execution_count": null, "id": "44380577570523278879349135829904343037", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "from data import df  # type: ignore\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        origin = gr.Dropdown([\"All\", \"DFW\", \"DAL\", \"HOU\"], value=\"All\", label=\"Origin\")\n", "        destination = gr.Dropdown([\"All\", \"JFK\", \"LGA\", \"EWR\"], value=\"All\", label=\"Destination\")\n", "        max_price = gr.Slider(0, 1000, value=1000, label=\"Max Price\")\n", "\n", "    def filtered_data(origin, destination, max_price):\n", "        _df = df[df[\"price\"] <= max_price]\n", "        if origin != \"All\":\n", "            _df = _df[_df[\"origin\"] == origin]\n", "        if destination != \"All\":\n", "            _df = _df[_df[\"destination\"] == destination]\n", "        return _df\n", "\n", "    gr.ScatterPlot(filtered_data, x=\"time\", y=\"price\", inputs=[origin, destination, max_price])\n", "    \n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from data import df
+from data import df  # type: ignore
 
 with gr.Blocks() as demo:
     with gr.Row():