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5f238b6
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Files changed (3) hide show
  1. requirements.txt +1 -1
  2. run.ipynb +1 -1
  3. run.py +1 -2
requirements.txt CHANGED
@@ -1,3 +1,3 @@
1
  bokeh>=3.0
2
  xyzservices
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- https://gradio-main-build.s3.amazonaws.com/96ef802fbde6b6060f6e26f30b70955533acda4b/gradio-3.24.1-py3-none-any.whl
 
1
  bokeh>=3.0
2
  xyzservices
3
+ https://gradio-main-build.s3.amazonaws.com/ef3862e075441c1131bd9e07a20dbb8622ad0a1e/gradio-3.24.1-py3-none-any.whl
run.ipynb CHANGED
@@ -1 +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.plotting import figure\n", "from bokeh.tile_providers import get_provider\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", " tile_provider = get_provider(xyz.OpenStreetMap.Mapnik)\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(tile_provider)\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()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}
 
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.tile_providers import get_provider\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", " tile_provider = get_provider(xyz.OpenStreetMap.Mapnik)\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(tile_provider)\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}
run.py CHANGED
@@ -1,6 +1,5 @@
1
  import gradio as gr
2
  import xyzservices.providers as xyz
3
- from bokeh.plotting import figure
4
  from bokeh.tile_providers import get_provider
5
  from bokeh.models import ColumnDataSource, Whisker
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  from bokeh.plotting import figure
@@ -91,4 +90,4 @@ with gr.Blocks() as demo:
91
 
92
 
93
  if __name__ == "__main__":
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- demo.launch()
 
1
  import gradio as gr
2
  import xyzservices.providers as xyz
 
3
  from bokeh.tile_providers import get_provider
4
  from bokeh.models import ColumnDataSource, Whisker
5
  from bokeh.plotting import figure
 
90
 
91
 
92
  if __name__ == "__main__":
93
+ demo.launch()