run.ipynb

{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: plot_guide_interactive"]}, {"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_interactive/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", "        ethnicity = gr.Dropdown([\"all\", \"white\", \"black\", \"asian\"], value=\"all\")\n", "        max_age = gr.Slider(18, 65, value=65)\n", "\n", "    def filtered_df(ethnic, age):\n", "        _df = df if ethnic == \"all\" else df[df[\"ethnicity\"] == ethnic]\n", "        _df = _df[_df[\"age\"] < age]\n", "        return _df\n", "\n", "    gr.ScatterPlot(filtered_df, inputs=[ethnicity, max_age], x=\"weight\", y=\"height\", title=\"Weight x Height\")\n", "    gr.LinePlot(filtered_df, inputs=[ethnicity, max_age], x=\"age\", y=\"height\", title=\"Age x Height\")\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}