Gradio app to run example

app.py

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+import numpy as np
+import gradio as gr
+import plotly.graph_objects as go
+from sklearn.datasets import load_diabetes
+from sklearn.ensemble import GradientBoostingRegressor
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import VotingRegressor
+
+
+def plot_votes(preds: list[tuple[str, np.array]], markers: list[str]=None) -> go.Figure:
+    fig = go.Figure()
+
+    for idx, (name, pred) in enumerate(preds):
+        if not markers:
+            symbol = "diamond"
+        else: 
+            symbol = markers[idx]
+        fig.add_trace(
+            go.Scatter(
+                y=pred,
+                mode="markers",
+                name=name,
+                marker=dict(symbol=symbol, size=10, line=dict(width=2, color="DarkSlateGrey"))
+            )
+        )
+    fig.update_layout(
+        title="Regressor predictions and their average",
+        yaxis_title="Predicted",
+        xaxis_title="Training Samples",
+        height=500,
+        width=1000,
+        xaxis=dict(showticklabels=False),
+        hovermode="x unified"
+    )
+
+    return fig
+
+
+def app_fn(n: int) -> go.Figure:
+    X, y = load_diabetes(return_X_y=True)
+
+    # Train classifiers
+    reg1 = GradientBoostingRegressor(random_state=1)
+    reg2 = RandomForestRegressor(random_state=1)
+    reg3 = LinearRegression()
+
+    reg1.fit(X, y)
+    reg2.fit(X, y)
+    reg3.fit(X, y)
+
+    ereg = VotingRegressor([("gb", reg1), ("rf", reg2), ("lr", reg3)])
+    ereg.fit(X, y)
+
+    xt = X[:n]
+
+    pred1 = reg1.predict(xt)
+    pred2 = reg2.predict(xt)
+    pred3 = reg3.predict(xt)
+    pred4 = ereg.predict(xt)
+
+    preds = [
+        ("Gradient Boosting", pred1),
+        ("Random Forest", pred2),
+        ("Linear Regression", pred3),
+        ("Voting Regressor", pred4)
+    ]
+    markers = ["diamond-tall", "triangle-up", "square", "star"]
+    fig = plot_votes(preds, markers)
+
+    return fig
+
+with gr.Blocks() as demo:
+    n = gr.inputs.Slider(10, 30, 5, 20, "Number of training samples")
+    plot = gr.Plot(label="Individual & Voting Predictions")
+    button = gr.Button(label="Update Plot")
+    button.click(fn=app_fn, inputs=[n], outputs=[plot])
+    demo.load(fn=app_fn, inputs=[n], outputs=[plot])