Event listener

app.py

@@ -17,7 +17,7 @@ def plot_validation_curve(x: np.array, ys: list[np.array], yerros: list[np.array
         fig.add_trace(
             go.Scatter(
                 x=x,
-                y=y,
+                y=np.round(y, 3),
                 name=name,
                 line_color=color
             )
@@ -39,23 +39,37 @@ def plot_validation_curve(x: np.array, ys: list[np.array], yerros: list[np.array
     if log_x:
         fig.update_xaxes(type="log")
 
-    fig.update_layout(title=title, xaxis_title="gamma", yaxis_title="Accuracy")
+    fig.update_layout(
+        title=title, 
+        xaxis_title="Hyperparameter", 
+        yaxis_title="Accuracy",
+        hovermode="x unified",
+    )
 
     return fig
 
 
 
-def app_fn(n_points: int):
+def app_fn(n_points: int, param_name: str):
     X, y = load_digits(return_X_y=True)
     subset_mask = np.isin(y, [1, 2])  # binary classification: 1 vs 2
     X, y = X[subset_mask], y[subset_mask]
 
-    param_range = np.logspace(-6, -1, n_points)
+    if param_name=="gamma":
+        param_range = np.logspace(-6, -1, n_points)
+        log_x = True
+    elif param_name=="C":
+        param_range = np.logspace(-2, 0, n_points)
+        log_x = True
+    elif param_name=="kernel":
+        param_range = np.array(["rbf", "linear", "poly", "sigmoid"])
+        log_x = False
+
     train_scores, test_scores = validation_curve(
         SVC(),
         X,
         y,
-        param_name="gamma",
+        param_name=param_name,
         param_range=param_range,
         scoring="accuracy",
         n_jobs=-1,
@@ -72,7 +86,8 @@ def app_fn(n_points: int):
         [train_scores_std, test_scores_std], 
         ["Training score", "Cross-validation score"], 
         ["orange", "navy"], 
-        title="Validation Curve with SVM for Gamma Hyperparameter"
+        title=f"Validation Curve with SVM for {param_name} Hyperparameter",
+        log_x=log_x
     )
 
     return fig
@@ -90,12 +105,21 @@ with gr.Blocks(title=title) as demo:
         [Original Example](https://scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py)
         """
     )
+    with gr.Row():
+        n_points = gr.inputs.Slider(5, 100, 5, 5,label="Number of points")
+        param_name = gr.inputs.Dropdown(["gamma", "C", "kernel"], label="Hyperparameter", default="gamma")
+
 
-    n_points = gr.inputs.Slider(5, 100, 5, 5,label="Number of points")
-    btn = gr.Button("Run")
     fig = gr.Plot(label="Validation Curve")
 
-    btn.click(fn=app_fn, inputs=[n_points], outputs=[fig])
-    demo.load(fn=app_fn, inputs=[n_points], outputs=[fig])
+    n_points.release(fn=app_fn, inputs=[n_points, param_name], outputs=[fig])
+    param_name.change(fn=app_fn, inputs=[n_points, param_name], outputs=[fig])
+    # C.change(fn=app_fn, inputs=[n_points, param_name, C, gamma, kernel, degree], outputs=[fig])
+    # gamma.change(fn=app_fn, inputs=[n_points, param_name, C, gamma, kernel, degree], outputs=[fig])
+    # kernel.change(fn=app_fn, inputs=[n_points, param_name, C, gamma, kernel, degree], outputs=[fig])
+    # degree.change(fn=app_fn, inputs=[n_points, param_name, C, gamma, kernel, degree], outputs=[fig])
+
+
+    demo.load(fn=app_fn, inputs=[n_points, param_name], outputs=[fig])
 
 demo.launch()