Update app.py

app.py

@@ -17,14 +17,17 @@ def generate_2d_regression(n_points, noise):
 
 def generate_3d_regression(n_points, noise):
     n_points = int(n_points)
+
     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()
-    return X, X1, X2, X_flat, Z_flat
+
+    return X1, X2, X_flat, Z_flat
 
 
 def generate_classification(n_points, noise):
@@ -52,7 +55,6 @@ def rf_2d_view(n_points, noise, n_estimators, max_depth):
     mse = mean_squared_error(y, y_pred)
 
     fig = go.Figure()
-
     fig.add_scatter(x=X.flatten(), y=y, mode="markers", name="Data")
     fig.add_scatter(x=X.flatten(), y=y_pred, mode="lines", name="RF Prediction")
 
@@ -67,14 +69,14 @@ def rf_2d_view(n_points, noise, n_estimators, max_depth):
 
 
 # ------------------------------------------------
-# 3D RANDOM FOREST REGRESSION (ROTATING CAMERA)
+# 3D RANDOM FOREST REGRESSION (ROTATING)
 # ------------------------------------------------
 
 def rf_3d_view(n_points, noise, n_estimators, max_depth):
     n_estimators = int(n_estimators)
     max_depth = int(max_depth)
 
-    _, X1, X2, X_flat, Z_flat = generate_3d_regression(n_points, noise)
+    X1, X2, X_flat, Z_flat = generate_3d_regression(n_points, noise)
 
     rf = RandomForestRegressor(n_estimators=n_estimators, max_depth=max_depth, random_state=42)
     rf.fit(X_flat, Z_flat)
@@ -83,18 +85,18 @@ def rf_3d_view(n_points, noise, n_estimators, max_depth):
     mse = mean_squared_error(Z_flat, Z_pred.ravel())
 
     fig = go.Figure()
-
     fig.add_surface(x=X1, y=X2, z=Z_pred, colorscale="Blues", opacity=0.9)
 
-    # Smooth rotating camera animation
+    # Smooth rotation frames
     frames = []
     for angle in range(0, 360, 10):
-        frames.append(go.Frame(layout=dict(scene_camera=dict(eye=dict(
-            x=np.cos(np.radians(angle)) * 2,
-            y=np.sin(np.radians(angle)) * 2,
-            z=1.2
-        )))))
-
+        frames.append(go.Frame(layout=dict(scene_camera=dict(
+            eye=dict(
+                x=np.cos(np.radians(angle)) * 2,
+                y=np.sin(np.radians(angle)) * 2,
+                z=1.2
+            )
+        ))))
     fig.frames = frames
 
     fig.update_layout(
@@ -105,10 +107,11 @@ def rf_3d_view(n_points, noise, n_estimators, max_depth):
         updatemenus=[dict(
             type="buttons",
             showactive=False,
-            buttons=[dict(label="Rotate 3D",
-                          method="animate",
-                          args=[None, {"frame": {"duration": 60, "redraw": True},
-                                       "fromcurrent": True}])]
+            buttons=[dict(
+                label="Rotate 3D",
+                method="animate",
+                args=[None, {"frame": {"duration": 60, "redraw": True}, "fromcurrent": True}]
+            )]
         )]
     )
 
@@ -116,7 +119,7 @@ def rf_3d_view(n_points, noise, n_estimators, max_depth):
 
 
 # ------------------------------------------------
-# CLASSIFICATION VIEW (CLEAR DECISION REGIONS)
+# CLASSIFICATION VIEW
 # ------------------------------------------------
 
 def classification_view(n_points, noise, n_estimators, max_depth):
@@ -128,21 +131,19 @@ def classification_view(n_points, noise, n_estimators, max_depth):
     rf = RandomForestClassifier(n_estimators=n_estimators, max_depth=max_depth, random_state=42)
     rf.fit(X, y)
 
-    # Mesh grid for decision boundary
+    # decision boundary
     xx, yy = np.meshgrid(
         np.linspace(X[:, 0].min() - 1, X[:, 0].max() + 1, 100),
         np.linspace(X[:, 1].min() - 1, X[:, 1].max() + 1, 100)
     )
 
     Z = rf.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(xx.shape)
-
     acc = accuracy_score(y, rf.predict(X))
 
     fig = go.Figure()
-
     fig.add_contour(x=xx[0], y=yy[:, 0], z=Z, showscale=False, opacity=0.4, colorscale="Blues")
-
-    fig.add_scatter(x=X[:, 0], y=X[:, 1], mode="markers", marker=dict(color=y, colorscale="Blues"), name="Data")
+    fig.add_scatter(x=X[:, 0], y=X[:, 1], mode="markers",
+                    marker=dict(color=y, colorscale="Blues"), name="Data")
 
     fig.update_layout(
         title="Random Forest Classification Boundary",
@@ -155,7 +156,7 @@ def classification_view(n_points, noise, n_estimators, max_depth):
 
 
 # ------------------------------------------------
-# GRADIO UI (AUTO-RUN, BEGINNER FRIENDLY)
+# GRADIO UI
 # ------------------------------------------------
 
 with gr.Blocks() as demo:
@@ -195,4 +196,4 @@ with gr.Blocks() as demo:
               [plot2d, mse2d, plot3d, mse3d, plot_cls, acc_cls])
 
 
-demo.launch(theme=gr.themes.Soft(primary_hue="blue"))
+demo.launch(theme=gr.themes.Soft(primary_hue="blue"))