Update app.py

app.py

@@ -348,39 +348,26 @@ with gr.Blocks() as demo:
             diff_output = gr.Image(label="Differentiating Features")
 
         with gr.TabItem("Interactive"):
-            gr.Markdown("### Interactive Prediction")
-            gr.Markdown("Enter values for each feature to get a prediction based on the trained model.")
-            with gr.Column():
-                input_components = gr.State(value=[])
-                dynamic_inputs = gr.Column(visible=True)
-                predict_btn = gr.Button("Predict")
-                prediction_output = gr.Textbox(label="Prediction Result")
+            gr.Markdown("### interactive prediction")
+            gr.Markdown("enter values for each feature to get a prediction based on the trained model.")
+            with gr.Form() as interactive_form:
+                dynamic_inputs = gr.Column()  # container for the dynamic input components
+                predict_btn = gr.Button("predict")  # now inside the form
+            prediction_output = gr.Textbox(label="prediction result")
             
             def update_inputs(file, label_col):
-                print(f"Updating inputs with file: {file}, label_col: {label_col}")  # Debug logging
+                print(f"updating inputs with file: {file}, label_col: {label_col}")
+                if file is None or label_col is None:
+                    return gr.Column.update(visible=False)
                 components = create_interactive_inputs(file, label_col)
-                # Return the components and update the Column's visibility
-                return components, gr.update(visible=True)  # Only update visibility, components are rendered in Blocks
-                # Use Blocks to render components dynamically
-                with dynamic_inputs:
-                    for component in components:
-                        component.render()
+                return gr.Column.update(visible=True, children=components)
 
-            file_input.change(
-                fn=update_inputs,
-                inputs=[file_input, label_dropdown],
-                outputs=[input_components, dynamic_inputs]
-            )
-            label_dropdown.change(
-                fn=update_inputs,
-                inputs=[file_input, label_dropdown],
-                outputs=[input_components, dynamic_inputs]
-            )
-            predict_btn.click(
-                fn=predict_interactive,
-                inputs=input_components,
-                outputs=prediction_output
-            )
+            # bind changes to update dynamic_inputs:
+            file_input.change(fn=update_inputs, inputs=[file_input, label_dropdown], outputs=dynamic_inputs)
+            label_dropdown.change(fn=update_inputs, inputs=[file_input, label_dropdown], outputs=dynamic_inputs)
+            
+            # bind the form submit to prediction (this collects values from all inputs inside the form):
+            interactive_form.submit(fn=predict_interactive, inputs=dynamic_inputs, outputs=prediction_output)
 
     analyze_btn.click(fn=analyze_file, inputs=[file_input, label_dropdown, clusters_slider],
                       outputs=[results_textbox, model_img_output, fi_output, kmeans_output, agg_output, diff_output])