Update app.py

app.py

@@ -25,26 +25,24 @@ The demo is based on the [scikit-learn docs](https://scikit-learn.org/stable/aut
 def func(x):
     return np.sin(2 * np.pi * x)
 
+def curve_fit(size, alpha, lam):
+    rng = np.random.RandomState(1234)
+    x_train = rng.uniform(0.0, 1.0, size)
+    y_train = func(x_train) + rng.normal(scale=0.1, size=size)
+    x_test = np.linspace(0.0, 1.0, 100)
 
-size = 25
-rng = np.random.RandomState(1234)
-x_train = rng.uniform(0.0, 1.0, size)
-y_train = func(x_train) + rng.normal(scale=0.1, size=size)
-x_test = np.linspace(0.0, 1.0, 100)
+    n_order = 3
+    X_train = np.vander(x_train, n_order + 1, increasing=True)
+    X_test = np.vander(x_test, n_order + 1, increasing=True)
+    reg = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True)
 
-n_order = 3
-X_train = np.vander(x_train, n_order + 1, increasing=True)
-X_test = np.vander(x_test, n_order + 1, increasing=True)
-reg = BayesianRidge(tol=1e-6, fit_intercept=False, compute_score=True)
-
-def curve_fit():
     fig, axes = plt.subplots(1, 2, figsize=(8, 4))
     for i, ax in enumerate(axes):
     # Bayesian ridge regression with different initial value pairs
         if i == 0:
             init = [1 / np.var(y_train), 1.0]  # Default values
         elif i == 1:
-            init = [1.0, 1e-3]
+            init = [alpha, lam]
             reg.set_params(alpha_init=init[0], lambda_init=init[1])
         reg.fit(X_train, y_train)
         ymean, ystd = reg.predict(X_test, return_std=True)
@@ -73,11 +71,15 @@ with gr.Blocks(theme=theme) as demo:
             <h1 style='text-align: center'>Curve Fitting with Bayesian Ridge Regression 📈</h1>
         ''')
     gr.Markdown(description)
-
+    
+    with gr.Row():
+        size = gr.Slider(minimum=10, maximum=100, step=5, value=25, label="Number of Data Points")
+        alpha = gr.Slider(minimum=1e-2, maximum=2, step=0.1, value=1, label="Initial Alpha")
+        lam = gr.Slider(minimum=1e-5, maximum=1, step=1e-4, value=1e-3, label="Initial Lambda")
     with gr.Row():
         run_button = gr.Button('Fit the Curve')
     with gr.Row():
         plot_result = gr.Plot()
-    run_button.click(fn=curve_fit, inputs=[], outputs=[plot_result])
+    run_button.click(fn=curve_fit, inputs=[size, alpha, lam], outputs=[plot_result])
 
 demo.launch()