Update app.py

app.py

@@ -146,7 +146,7 @@ def generate_xor(n_samples=400):  # Reduced from 800 for performance
     y = np.logical_xor(X[:, 0] > 0, X[:, 1] > 0).astype(int)
     return X, y
 
-def generate_sine_wave(n_samples=400, noise):  # Reduced from 800
+def generate_sine_wave(noise, n_samples=400):  # Reordered: non-default before default
     X = np.linspace(-3, 3, n_samples).reshape(-1, 1)
     y = np.sin(X) + np.random.normal(0, noise / 100, X.shape)
     return np.hstack([X, X**2]), y.ravel()
@@ -161,7 +161,7 @@ if problem_type == "Classification":
     elif dataset_type == "XOR":
         fv, cv = generate_xor(400)
 else:
-    fv, cv = generate_sine_wave(400, noise_level)
+    fv, cv = generate_sine_wave(noise_level, 400)
 
 # Feature preprocessing
 std = StandardScaler()