Create app.py

app.py

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+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])
+
+
+if __name__ == "__main__":
+    demo.launch()