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requirements.txt

@@ -1,4 +1,4 @@
-gradio-client @ git+https://github.com/gradio-app/gradio@de997e67c9a7feb9e2eccebf92969366dbd67eba#subdirectory=client/python
-https://gradio-builds.s3.amazonaws.com/de997e67c9a7feb9e2eccebf92969366dbd67eba/gradio-4.39.0-py3-none-any.whl
+gradio-client @ git+https://github.com/gradio-app/gradio@9b42ba8f1006c05d60a62450d3036ce0d6784f86#subdirectory=client/python
+https://gradio-builds.s3.amazonaws.com/9b42ba8f1006c05d60a62450d3036ce0d6784f86/gradio-4.39.0-py3-none-any.whl
 bokeh>=3.0
 xyzservices

run.ipynb

@@ -1 +1 @@
-{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "\n", "def get_plot(plot_type):\n", "    if plot_type == \"map\":\n", "        plot = figure(\n", "            x_range=(-2000000, 6000000),\n", "            y_range=(-1000000, 7000000),\n", "            x_axis_type=\"mercator\",\n", "            y_axis_type=\"mercator\",\n", "        )\n", "        plot.add_tile(xyz.OpenStreetMap.Mapnik)  # type: ignore\n", "        return plot\n", "    elif plot_type == \"whisker\":\n", "        classes = list(sorted(df[\"class\"].unique()))\n", "\n", "        p = figure(\n", "            height=400,\n", "            x_range=classes,\n", "            background_fill_color=\"#efefef\",\n", "            title=\"Car class vs HWY mpg with quintile ranges\",\n", "        )\n", "        p.xgrid.grid_line_color = None\n", "\n", "        g = df.groupby(\"class\")\n", "        upper = g.hwy.quantile(0.80)\n", "        lower = g.hwy.quantile(0.20)\n", "        source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", "        error = Whisker(\n", "            base=\"base\",\n", "            upper=\"upper\",\n", "            lower=\"lower\",\n", "            source=source,\n", "            level=\"annotation\",\n", "            line_width=2,\n", "        )\n", "        error.upper_head.size = 20\n", "        error.lower_head.size = 20\n", "        p.add_layout(error)\n", "\n", "        p.circle(\n", "            jitter(\"class\", 0.3, range=p.x_range),\n", "            \"hwy\",\n", "            source=df,\n", "            alpha=0.5,\n", "            size=13,\n", "            line_color=\"white\",\n", "            color=factor_cmap(\"class\", \"Light6\", classes),\n", "        )\n", "        return p\n", "    elif plot_type == \"scatter\":\n", "\n", "        SPECIES = sorted(data.species.unique())\n", "        MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", "        p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", "        p.xaxis.axis_label = \"Flipper Length (mm)\"\n", "        p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", "        p.scatter(\n", "            \"flipper_length_mm\",\n", "            \"body_mass_g\",\n", "            source=data,\n", "            legend_group=\"species\",\n", "            fill_alpha=0.4,\n", "            size=12,\n", "            marker=factor_mark(\"species\", MARKERS, SPECIES),\n", "            color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", "        )\n", "\n", "        p.legend.location = \"top_left\"\n", "        p.legend.title = \"Species\"\n", "        return p\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", "        plot = gr.Plot()\n", "    plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", "    demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: bokeh_plot"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio bokeh>=3.0 xyzservices"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import xyzservices.providers as xyz\n", "from bokeh.models import ColumnDataSource, Whisker\n", "from bokeh.plotting import figure\n", "from bokeh.sampledata.autompg2 import autompg2 as df\n", "from bokeh.sampledata.penguins import data\n", "from bokeh.transform import factor_cmap, jitter, factor_mark\n", "\n", "def get_plot(plot_type):\n", "    if plot_type == \"map\":\n", "        plot = figure(\n", "            x_range=(-2000000, 6000000),\n", "            y_range=(-1000000, 7000000),\n", "            x_axis_type=\"mercator\",\n", "            y_axis_type=\"mercator\",\n", "        )\n", "        plot.add_tile(xyz.OpenStreetMap.Mapnik)  # type: ignore\n", "        return plot\n", "    elif plot_type == \"whisker\":\n", "        classes = sorted(df[\"class\"].unique())\n", "\n", "        p = figure(\n", "            height=400,\n", "            x_range=classes,\n", "            background_fill_color=\"#efefef\",\n", "            title=\"Car class vs HWY mpg with quintile ranges\",\n", "        )\n", "        p.xgrid.grid_line_color = None\n", "\n", "        g = df.groupby(\"class\")\n", "        upper = g.hwy.quantile(0.80)\n", "        lower = g.hwy.quantile(0.20)\n", "        source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))\n", "\n", "        error = Whisker(\n", "            base=\"base\",\n", "            upper=\"upper\",\n", "            lower=\"lower\",\n", "            source=source,\n", "            level=\"annotation\",\n", "            line_width=2,\n", "        )\n", "        error.upper_head.size = 20\n", "        error.lower_head.size = 20\n", "        p.add_layout(error)\n", "\n", "        p.circle(\n", "            jitter(\"class\", 0.3, range=p.x_range),\n", "            \"hwy\",\n", "            source=df,\n", "            alpha=0.5,\n", "            size=13,\n", "            line_color=\"white\",\n", "            color=factor_cmap(\"class\", \"Light6\", classes),\n", "        )\n", "        return p\n", "    elif plot_type == \"scatter\":\n", "\n", "        SPECIES = sorted(data.species.unique())\n", "        MARKERS = [\"hex\", \"circle_x\", \"triangle\"]\n", "\n", "        p = figure(title=\"Penguin size\", background_fill_color=\"#fafafa\")\n", "        p.xaxis.axis_label = \"Flipper Length (mm)\"\n", "        p.yaxis.axis_label = \"Body Mass (g)\"\n", "\n", "        p.scatter(\n", "            \"flipper_length_mm\",\n", "            \"body_mass_g\",\n", "            source=data,\n", "            legend_group=\"species\",\n", "            fill_alpha=0.4,\n", "            size=12,\n", "            marker=factor_mark(\"species\", MARKERS, SPECIES),\n", "            color=factor_cmap(\"species\", \"Category10_3\", SPECIES),\n", "        )\n", "\n", "        p.legend.location = \"top_left\"\n", "        p.legend.title = \"Species\"\n", "        return p\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        plot_type = gr.Radio(value=\"scatter\", choices=[\"scatter\", \"whisker\", \"map\"])\n", "        plot = gr.Plot()\n", "    plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])\n", "    demo.load(get_plot, inputs=[plot_type], outputs=[plot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()\n"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

@@ -6,7 +6,6 @@ from bokeh.sampledata.autompg2 import autompg2 as df
 from bokeh.sampledata.penguins import data
 from bokeh.transform import factor_cmap, jitter, factor_mark
 
-
 def get_plot(plot_type):
     if plot_type == "map":
         plot = figure(
@@ -18,7 +17,7 @@ def get_plot(plot_type):
         plot.add_tile(xyz.OpenStreetMap.Mapnik)  # type: ignore
         return plot
     elif plot_type == "whisker":
-        classes = list(sorted(df["class"].unique()))
+        classes = sorted(df["class"].unique())
 
         p = figure(
             height=400,
@@ -86,6 +85,5 @@ with gr.Blocks() as demo:
     plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])
     demo.load(get_plot, inputs=[plot_type], outputs=[plot])
 
-
 if __name__ == "__main__":
     demo.launch()