Update app.py

app.py

@@ -1,36 +1,140 @@
-!pip install prophet
-
+import altair as alt
 import gradio as gr
+import numpy as np
 import pandas as pd
+from vega_datasets import data
+
+
+def make_plot(plot_type):
+    if plot_type == "scatter_plot":
+        cars = data.cars()
+        return alt.Chart(cars).mark_point().encode(
+            x='Horsepower',
+            y='Miles_per_Gallon',
+            color='Origin',
+        )
+    elif plot_type == "heatmap":
+        # Compute x^2 + y^2 across a 2D grid
+        x, y = np.meshgrid(range(-5, 5), range(-5, 5))
+        z = x ** 2 + y ** 2
+
+        # Convert this grid to columnar data expected by Altair
+        source = pd.DataFrame({'x': x.ravel(),
+                            'y': y.ravel(),
+                            'z': z.ravel()})
+        return alt.Chart(source).mark_rect().encode(
+            x='x:O',
+            y='y:O',
+            color='z:Q'
+        )
+    elif plot_type == "us_map":
+        states = alt.topo_feature(data.us_10m.url, 'states')
+        source = data.income.url
+
+        return alt.Chart(source).mark_geoshape().encode(
+            shape='geo:G',
+            color='pct:Q',
+            tooltip=['name:N', 'pct:Q'],
+            facet=alt.Facet('group:N', columns=2),
+        ).transform_lookup(
+            lookup='id',
+            from_=alt.LookupData(data=states, key='id'),
+            as_='geo'
+        ).properties(
+            width=300,
+            height=175,
+        ).project(
+            type='albersUsa'
+        )
+    elif plot_type == "interactive_barplot":
+        source = data.movies.url
+
+        pts = alt.selection(type="single", encodings=['x'])
+
+        rect = alt.Chart(data.movies.url).mark_rect().encode(
+            alt.X('IMDB_Rating:Q', bin=True),
+            alt.Y('Rotten_Tomatoes_Rating:Q', bin=True),
+            alt.Color('count()',
+                scale=alt.Scale(scheme='greenblue'),
+                legend=alt.Legend(title='Total Records')
+            )
+        )
+
+        circ = rect.mark_point().encode(
+            alt.ColorValue('grey'),
+            alt.Size('count()',
+                legend=alt.Legend(title='Records in Selection')
+            )
+        ).transform_filter(
+            pts
+        )
+
+        bar = alt.Chart(source).mark_bar().encode(
+            x='Major_Genre:N',
+            y='count()',
+            color=alt.condition(pts, alt.ColorValue("steelblue"), alt.ColorValue("grey"))
+        ).properties(
+            width=550,
+            height=200
+        ).add_selection(pts)
 
+        plot = alt.vconcat(
+            rect + circ,
+            bar
+        ).resolve_legend(
+            color="independent",
+            size="independent"
+        )
+        return plot
+    elif plot_type == "radial":
+        source = pd.DataFrame({"values": [12, 23, 47, 6, 52, 19]})
 
-from prophet import Prophet
+        base = alt.Chart(source).encode(
+            theta=alt.Theta("values:Q", stack=True),
+            radius=alt.Radius("values", scale=alt.Scale(type="sqrt", zero=True, rangeMin=20)),
+            color="values:N",
+        )
 
+        c1 = base.mark_arc(innerRadius=20, stroke="#fff")
 
-def plot_forecast(example_name, period):
-    df = pd.read_csv(f'https://raw.githubusercontent.com/facebook/prophet/main/examples/example_{example_name}.csv')
-    df.columns = ['ds','y']
+        c2 = base.mark_text(radiusOffset=10).encode(text="values:Q")
+
+        return c1 + c2
+    elif plot_type == "multiline":
+        source = data.stocks()
+
+        highlight = alt.selection(type='single', on='mouseover',
+                                fields=['symbol'], nearest=True)
+
+        base = alt.Chart(source).encode(
+            x='date:T',
+            y='price:Q',
+            color='symbol:N'
+        )
+
+        points = base.mark_circle().encode(
+            opacity=alt.value(0)
+        ).add_selection(
+            highlight
+        ).properties(
+            width=600
+        )
+
+        lines = base.mark_line().encode(
+            size=alt.condition(~highlight, alt.value(1), alt.value(3))
+        )
+
+        return points + lines
 
-    m = Prophet()
-    m.fit(df)
-    future = m.make_future_dataframe(periods=period)
-    forecast = m.predict(future)
-    fig = m.plot(forecast)
-    return fig
 
 with gr.Blocks() as demo:
-    gr.Markdown(
-    """
-    時系列予測モデルの結果
-    """)
-    with gr.Row():
-        example = gr.Dropdown(["air_passengers", "pedestrians_covid", "retail_sales"], label="データソース", value="air_passengers")
-        period = gr.Slider(25, 250, 25, step=25, label="予測期間")
- 
-    plt = gr.Plot()
-
-    example.change(plot_forecast, [example,period], plt, queue=False)
-    period.change(plot_forecast, [example,period], plt, queue=False)
-    demo.load(plot_forecast, [example,period], plt, queue=False)    
-
-demo.launch()
+    button = gr.Radio(label="Plot type",
+                      choices=['scatter_plot', 'heatmap', 'us_map',
+                               'interactive_barplot', "radial", "multiline"], value='scatter_plot')
+    plot = gr.Plot(label="Plot")
+    button.change(make_plot, inputs=button, outputs=[plot])
+    demo.load(make_plot, inputs=[button], outputs=[plot])
+
+
+if __name__ == "__main__":
+    demo.launch()