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README.md

@@ -5,7 +5,7 @@ emoji: 🔥
 colorFrom: indigo
 colorTo: indigo
 sdk: gradio
-sdk_version: 4.44.1
+sdk_version: 5.0.0
 app_file: run.py
 pinned: false
 hf_oauth: true

requirements.txt

@@ -1,2 +1,3 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@a15381b23d3f6b59180e83a94a5279feccbf79a2#subdirectory=client/python
-https://gradio-pypi-previews.s3.amazonaws.com/a15381b23d3f6b59180e83a94a5279feccbf79a2/gradio-4.44.1-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@bbf9ba7e997022960c621f72baa891185bd03732#subdirectory=client/python
+https://gradio-pypi-previews.s3.amazonaws.com/bbf9ba7e997022960c621f72baa891185bd03732/gradio-5.0.0-py3-none-any.whl
+pandas

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: scatter_plot_demo"]}, {"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": ["import pandas as pd\n", "from random import randint, random\n", "import gradio as gr\n", "\n", "\n", "temp_sensor_data = pd.DataFrame(\n", "    {\n", "        \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n", "        \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n", "        \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n", "        \"location\": [\"indoor\", \"outdoor\"] * 100,\n", "    }\n", ")\n", "\n", "food_rating_data = pd.DataFrame(\n", "    {\n", "        \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n", "        \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n", "        \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n", "        \"wait\": [random() for i in range(100)],\n", "    }\n", ")\n", "\n", "with gr.Blocks() as scatter_plots:\n", "    with gr.Row():\n", "        start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n", "        end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n", "        apply_btn = gr.Button(\"Apply\", scale=0)\n", "    with gr.Row():\n", "        group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n", "        aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n", "\n", "    temp_by_time = gr.ScatterPlot(\n", "        temp_sensor_data,\n", "        x=\"time\",\n", "        y=\"temperature\",\n", "    )\n", "    temp_by_time_location = gr.ScatterPlot(\n", "        temp_sensor_data,\n", "        x=\"time\",\n", "        y=\"temperature\",\n", "        color=\"location\",\n", "    )\n", "\n", "    time_graphs = [temp_by_time, temp_by_time_location]\n", "    group_by.change(\n", "        lambda group: [gr.ScatterPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n", "        group_by,\n", "        time_graphs\n", "    )\n", "    aggregate.change(\n", "        lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),\n", "        aggregate,\n", "        time_graphs\n", "    )\n", "\n", "    price_by_cuisine = gr.ScatterPlot(\n", "        food_rating_data,\n", "        x=\"cuisine\",\n", "        y=\"price\",\n", "    )\n", "    with gr.Row():\n", "        price_by_rating = gr.ScatterPlot(\n", "            food_rating_data,\n", "            x=\"rating\",\n", "            y=\"price\",\n", "            color=\"wait\",\n", "            show_actions_button=True,\n", "        )\n", "        price_by_rating_color = gr.ScatterPlot(\n", "            food_rating_data,\n", "            x=\"rating\",\n", "            y=\"price\",\n", "            color=\"cuisine\",\n", "        )\n", "\n", "if __name__ == \"__main__\":\n", "    scatter_plots.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: scatter_plot_demo"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio pandas "]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import pandas as pd\n", "from random import randint, random\n", "import gradio as gr\n", "\n", "\n", "temp_sensor_data = pd.DataFrame(\n", "    {\n", "        \"time\": pd.date_range(\"2021-01-01\", end=\"2021-01-05\", periods=200),\n", "        \"temperature\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n", "        \"humidity\": [randint(50 + 10 * (i % 2), 65 + 15 * (i % 2)) for i in range(200)],\n", "        \"location\": [\"indoor\", \"outdoor\"] * 100,\n", "    }\n", ")\n", "\n", "food_rating_data = pd.DataFrame(\n", "    {\n", "        \"cuisine\": [[\"Italian\", \"Mexican\", \"Chinese\"][i % 3] for i in range(100)],\n", "        \"rating\": [random() * 4 + 0.5 * (i % 3) for i in range(100)],\n", "        \"price\": [randint(10, 50) + 4 * (i % 3) for i in range(100)],\n", "        \"wait\": [random() for i in range(100)],\n", "    }\n", ")\n", "\n", "with gr.Blocks() as scatter_plots:\n", "    with gr.Row():\n", "        start = gr.DateTime(\"2021-01-01 00:00:00\", label=\"Start\")\n", "        end = gr.DateTime(\"2021-01-05 00:00:00\", label=\"End\")\n", "        apply_btn = gr.Button(\"Apply\", scale=0)\n", "    with gr.Row():\n", "        group_by = gr.Radio([\"None\", \"30m\", \"1h\", \"4h\", \"1d\"], value=\"None\", label=\"Group by\")\n", "        aggregate = gr.Radio([\"sum\", \"mean\", \"median\", \"min\", \"max\"], value=\"sum\", label=\"Aggregation\")\n", "\n", "    temp_by_time = gr.ScatterPlot(\n", "        temp_sensor_data,\n", "        x=\"time\",\n", "        y=\"temperature\",\n", "    )\n", "    temp_by_time_location = gr.ScatterPlot(\n", "        temp_sensor_data,\n", "        x=\"time\",\n", "        y=\"temperature\",\n", "        color=\"location\",\n", "    )\n", "\n", "    time_graphs = [temp_by_time, temp_by_time_location]\n", "    group_by.change(\n", "        lambda group: [gr.ScatterPlot(x_bin=None if group == \"None\" else group)] * len(time_graphs),\n", "        group_by,\n", "        time_graphs\n", "    )\n", "    aggregate.change(\n", "        lambda aggregate: [gr.ScatterPlot(y_aggregate=aggregate)] * len(time_graphs),\n", "        aggregate,\n", "        time_graphs\n", "    )\n", "\n", "    price_by_cuisine = gr.ScatterPlot(\n", "        food_rating_data,\n", "        x=\"cuisine\",\n", "        y=\"price\",\n", "    )\n", "    with gr.Row():\n", "        price_by_rating = gr.ScatterPlot(\n", "            food_rating_data,\n", "            x=\"rating\",\n", "            y=\"price\",\n", "            color=\"wait\",\n", "            show_actions_button=True,\n", "        )\n", "        price_by_rating_color = gr.ScatterPlot(\n", "            food_rating_data,\n", "            x=\"rating\",\n", "            y=\"price\",\n", "            color=\"cuisine\",\n", "        )\n", "\n", "if __name__ == \"__main__\":\n", "    scatter_plots.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}