Upload with huggingface_hub

README.md

@@ -1,12 +1,11 @@
+
 ---
-title: Bokeh Plot Main
-emoji: 🌖
-colorFrom: gray
-colorTo: gray
+title: bokeh_plot_main 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 3.18.0
-app_file: app.py
+sdk_version: 3.18.1b7
+app_file: run.py
 pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

@@ -0,0 +1,3 @@
+bokeh>=3.0
+xyzservices
+https://gradio-main-build.s3.amazonaws.com/9b2119f2979026163fc09a0212cdc6426644c0f6/gradio-3.18.1b7-py3-none-any.whl

run.ipynb

@@ -0,0 +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}

run.py

@@ -0,0 +1,94 @@
+import gradio as gr
+import xyzservices.providers as xyz
+from bokeh.plotting import figure
+from bokeh.tile_providers import get_provider
+from bokeh.models import ColumnDataSource, Whisker
+from bokeh.plotting import figure
+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":
+        tile_provider = get_provider(xyz.OpenStreetMap.Mapnik)
+        plot = figure(
+            x_range=(-2000000, 6000000),
+            y_range=(-1000000, 7000000),
+            x_axis_type="mercator",
+            y_axis_type="mercator",
+        )
+        plot.add_tile(tile_provider)
+        return plot
+    elif plot_type == "whisker":
+        classes = list(sorted(df["class"].unique()))
+
+        p = figure(
+            height=400,
+            x_range=classes,
+            background_fill_color="#efefef",
+            title="Car class vs HWY mpg with quintile ranges",
+        )
+        p.xgrid.grid_line_color = None
+
+        g = df.groupby("class")
+        upper = g.hwy.quantile(0.80)
+        lower = g.hwy.quantile(0.20)
+        source = ColumnDataSource(data=dict(base=classes, upper=upper, lower=lower))
+
+        error = Whisker(
+            base="base",
+            upper="upper",
+            lower="lower",
+            source=source,
+            level="annotation",
+            line_width=2,
+        )
+        error.upper_head.size = 20
+        error.lower_head.size = 20
+        p.add_layout(error)
+
+        p.circle(
+            jitter("class", 0.3, range=p.x_range),
+            "hwy",
+            source=df,
+            alpha=0.5,
+            size=13,
+            line_color="white",
+            color=factor_cmap("class", "Light6", classes),
+        )
+        return p
+    elif plot_type == "scatter":
+
+        SPECIES = sorted(data.species.unique())
+        MARKERS = ["hex", "circle_x", "triangle"]
+
+        p = figure(title="Penguin size", background_fill_color="#fafafa")
+        p.xaxis.axis_label = "Flipper Length (mm)"
+        p.yaxis.axis_label = "Body Mass (g)"
+
+        p.scatter(
+            "flipper_length_mm",
+            "body_mass_g",
+            source=data,
+            legend_group="species",
+            fill_alpha=0.4,
+            size=12,
+            marker=factor_mark("species", MARKERS, SPECIES),
+            color=factor_cmap("species", "Category10_3", SPECIES),
+        )
+
+        p.legend.location = "top_left"
+        p.legend.title = "Species"
+        return p
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        plot_type = gr.Radio(value="scatter", choices=["scatter", "whisker", "map"])
+        plot = gr.Plot()
+    plot_type.change(get_plot, inputs=[plot_type], outputs=[plot])
+    demo.load(get_plot, inputs=[plot_type], outputs=[plot])
+
+
+if __name__ == "__main__":
+    demo.launch()