Create app.py

app.py

@@ -0,0 +1,119 @@
+import gradio as gr
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.metrics import mean_squared_error
+from mpl_toolkits.mplot3d import Axes3D
+
+# ------------------------------------------------
+# Random Forest 2D
+# ------------------------------------------------
+def rf_2d(n_points, noise, n_estimators, max_depth):
+    X = np.linspace(0, 10, n_points).reshape(-1, 1)
+    y = 2.5 * X.flatten() + 5 + np.random.randn(n_points) * noise
+
+    model = RandomForestRegressor(
+        n_estimators=n_estimators,
+        max_depth=max_depth,
+        random_state=42
+    )
+    model.fit(X, y)
+    y_pred = model.predict(X)
+
+    mse = mean_squared_error(y, y_pred)
+
+    fig, ax = plt.subplots(figsize=(5, 4))
+    ax.scatter(X, y, s=20, color="orange", label="Data")
+    ax.plot(X, y_pred, color="blue", linewidth=2, label="RF Prediction")
+    ax.set_title("2D Random Forest Regression")
+    ax.legend()
+
+    return fig, f"MSE: {mse:.4f}"
+
+
+# ------------------------------------------------
+# Random Forest 3D
+# ------------------------------------------------
+def rf_3d(n_points, noise, n_estimators, max_depth):
+    x1 = np.linspace(0, 10, n_points)
+    x2 = np.linspace(0, 10, n_points)
+    X1, X2 = np.meshgrid(x1, x2)
+
+    Z = 3 * X1 + 2 * X2 + 10 + np.random.randn(*X1.shape) * noise
+
+    X_flat = np.column_stack((X1.ravel(), X2.ravel()))
+    Z_flat = Z.ravel()
+
+    model = RandomForestRegressor(
+        n_estimators=n_estimators,
+        max_depth=max_depth,
+        random_state=42
+    )
+    model.fit(X_flat, Z_flat)
+
+    Z_pred = model.predict(X_flat).reshape(X1.shape)
+    mse = mean_squared_error(Z_flat, Z_pred.ravel())
+
+    fig = plt.figure(figsize=(5, 4))
+    ax = fig.add_subplot(111, projection="3d")
+
+    idx = np.random.choice(len(Z_flat), 400, replace=False)
+    ax.scatter(
+        X_flat[idx, 0],
+        X_flat[idx, 1],
+        Z_flat[idx],
+        s=8,
+        alpha=0.3,
+        color="orange"
+    )
+
+    ax.plot_surface(X1, X2, Z_pred, alpha=0.7, color="blue")
+    ax.set_title("3D Random Forest Surface")
+
+    return fig, f"MSE: {mse:.4f}"
+
+
+# ------------------------------------------------
+# Gradio UI
+# ------------------------------------------------
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        # 🌲 Random Forest Regression Visualizer  
+        Interactive **2D & 3D Random Forest** playground  
+        """
+    )
+
+    with gr.Row():
+        mode = gr.Radio(
+            ["2D Regression", "3D Regression"],
+            value="2D Regression",
+            label="Mode"
+        )
+
+    with gr.Row():
+        n_points = gr.Slider(20, 200, value=80, step=10, label="Data Points")
+        noise = gr.Slider(0.0, 5.0, value=1.0, label="Noise Level")
+
+    with gr.Row():
+        n_estimators = gr.Slider(10, 200, value=50, step=10, label="Trees")
+        max_depth = gr.Slider(2, 20, value=8, step=1, label="Max Depth")
+
+    run_btn = gr.Button("🌲 Train Random Forest")
+
+    plot = gr.Plot()
+    metric = gr.Markdown()
+
+    def run(mode, n_points, noise, n_estimators, max_depth):
+        if mode == "2D Regression":
+            return rf_2d(n_points, noise, n_estimators, max_depth)
+        else:
+            return rf_3d(n_points, noise, n_estimators, max_depth)
+
+    run_btn.click(
+        run,
+        inputs=[mode, n_points, noise, n_estimators, max_depth],
+        outputs=[plot, metric]
+    )
+
+demo.launch()