update gradio syntax for latest versions

app.py

@@ -16,9 +16,8 @@ X_train, X_test, y_train, y_test = train_test_split(
     X, y, test_size=0.3, random_state=42
 )
 
-# 2. Define a function that takes hyperparameters and returns model accuracy + confusion matrix
+# 2. Define a function that trains & evaluates a model given hyperparameters
 def train_and_evaluate(learning_rate, n_estimators, max_depth):
-    # Train model
     clf = GradientBoostingClassifier(
         learning_rate=learning_rate,
         n_estimators=n_estimators,
@@ -26,25 +25,17 @@ def train_and_evaluate(learning_rate, n_estimators, max_depth):
         random_state=42
     )
     clf.fit(X_train, y_train)
-
-    # Predict on test data
     y_pred = clf.predict(X_test)
 
-    # Calculate metrics
     accuracy = accuracy_score(y_test, y_pred)
     cm = confusion_matrix(y_test, y_pred)
-
-    # Convert confusion matrix to a more display-friendly format
-    cm_display = ""
-    for row in cm:
-        cm_display += str(row) + "\n"
+    cm_display = "\n".join([str(row) for row in cm])
 
     return f"Accuracy: {accuracy:.3f}\nConfusion Matrix:\n{cm_display}"
 
 # 3. Define a prediction function for user-supplied feature values
 def predict_species(sepal_length, sepal_width, petal_length, petal_width,
                     learning_rate, n_estimators, max_depth):
-    # Train a new model using same hyperparams
     clf = GradientBoostingClassifier(
         learning_rate=learning_rate,
         n_estimators=n_estimators,
@@ -53,47 +44,54 @@ def predict_species(sepal_length, sepal_width, petal_length, petal_width,
     )
     clf.fit(X_train, y_train)
 
-    # Predict species
     user_sample = np.array([[sepal_length, sepal_width, petal_length, petal_width]])
     prediction = clf.predict(user_sample)[0]
     return f"Predicted species: {class_names[prediction]}"
 
 # 4. Build the Gradio interface
+with gr.Blocks() as demo:
+    with gr.Tab("Train & Evaluate"):
+        gr.Markdown("## Train a GradientBoostingClassifier on the Iris dataset")
+        learning_rate_slider = gr.Slider(0.01, 1.0, value=0.1, step=0.01, label="learning_rate")
+        n_estimators_slider = gr.Slider(50, 300, value=100, step=50, label="n_estimators")
+        max_depth_slider = gr.Slider(1, 10, value=3, step=1, label="max_depth")
+
+        train_button = gr.Button("Train & Evaluate")
+        output_text = gr.Textbox(label="Results")
+
+        train_button.click(
+            fn=train_and_evaluate,
+            inputs=[learning_rate_slider, n_estimators_slider, max_depth_slider],
+            outputs=output_text,
+        )
+
+    with gr.Tab("Predict"):
+        gr.Markdown("## Predict Iris Species with GradientBoostingClassifier")
+        sepal_length_input = gr.Number(value=5.1, label=feature_names[0])
+        sepal_width_input = gr.Number(value=3.5, label=feature_names[1])
+        petal_length_input = gr.Number(value=1.4, label=feature_names[2])
+        petal_width_input = gr.Number(value=0.2, label=feature_names[3])
+        
+        # Hyperparams for the model that will do the prediction
+        learning_rate_slider2 = gr.Slider(0.01, 1.0, value=0.1, step=0.01, label="learning_rate")
+        n_estimators_slider2 = gr.Slider(50, 300, value=100, step=50, label="n_estimators")
+        max_depth_slider2 = gr.Slider(1, 10, value=3, step=1, label="max_depth")
+
+        predict_button = gr.Button("Predict")
+        prediction_text = gr.Textbox(label="Prediction")
+
+        predict_button.click(
+            fn=predict_species,
+            inputs=[
+                sepal_length_input,
+                sepal_width_input,
+                petal_length_input,
+                petal_width_input,
+                learning_rate_slider2,
+                n_estimators_slider2,
+                max_depth_slider2,
+            ],
+            outputs=prediction_text
+        )
 
-# Inputs to tune hyperparameters
-hyperparam_inputs = [
-    gr.inputs.Slider(0.01, 1.0, step=0.01, default=0.1, label="learning_rate"),
-    gr.inputs.Slider(50, 300, step=50, default=100, label="n_estimators"),
-    gr.inputs.Slider(1, 10, step=1, default=3, label="max_depth")
-]
-
-# Button or automatic “live” updates
-training_interface = gr.Interface(
-    fn=train_and_evaluate,
-    inputs=hyperparam_inputs,
-    outputs="text",
-    title="Gradient Boosting Training and Evaluation",
-    description="Train a GradientBoostingClassifier on the Iris dataset with different hyperparameters."
-)
-
-# Inputs for real-time prediction
-feature_inputs = [
-    gr.inputs.Number(default=5.1, label=feature_names[0]),
-    gr.inputs.Number(default=3.5, label=feature_names[1]),
-    gr.inputs.Number(default=1.4, label=feature_names[2]),
-    gr.inputs.Number(default=0.2, label=feature_names[3])
-] + hyperparam_inputs
-
-prediction_interface = gr.Interface(
-    fn=predict_species,
-    inputs=feature_inputs,
-    outputs="text",
-    title="Iris Species Prediction",
-    description="Use a GradientBoostingClassifier to predict Iris species from user input."
-)
-
-demo = gr.TabbedInterface([training_interface, prediction_interface],
-                          ["Train & Evaluate", "Predict"])
-
-# Launch the Gradio app
 demo.launch()