Update app.py

app.py

@@ -264,14 +264,14 @@ def create_interactive_inputs(file, label_col):
             return []
         
         X = df.drop(columns=[label_col])
-        inputs = []
+        components = []
         for col in X.columns:
             examples = X[col].dropna().sample(min(3, len(X[col].dropna()))).tolist()
             if pd.api.types.is_numeric_dtype(X[col]):
-                inputs.append(gr.Number(label=f"{col} (e.g., {', '.join(map(str, examples))})"))
+                components.append(gr.Number(label=f"{col} (e.g., {', '.join(map(str, examples))})"))
             else:
-                inputs.append(gr.Textbox(label=f"{col} (e.g., {', '.join(map(str, examples))})"))
-        return inputs
+                components.append(gr.Textbox(label=f"{col} (e.g., {', '.join(map(str, examples))})"))
+        return components
     except Exception:
         return []
 
@@ -325,15 +325,25 @@ with gr.Blocks() as demo:
         with gr.TabItem("Interactive"):
             gr.Markdown("### Interactive Prediction")
             gr.Markdown("Enter values for each feature to get a prediction based on the trained model.")
-            interactive_inputs = gr.State(value=[])
             with gr.Column():
-                input_components = gr.DynamicLayout(fn=create_interactive_inputs, inputs=[file_input, label_dropdown], outputs=interactive_inputs)
+                interactive_inputs = gr.State(value=[])
+                input_components = gr.Column()
                 predict_btn = gr.Button("Predict")
                 prediction_output = gr.Textbox(label="Prediction Result")
             
+            file_input.change(
+                fn=create_interactive_inputs,
+                inputs=[file_input, label_dropdown],
+                outputs=input_components
+            )
+            label_dropdown.change(
+                fn=create_interactive_inputs,
+                inputs=[file_input, label_dropdown],
+                outputs=input_components
+            )
             predict_btn.click(
                 fn=predict_interactive,
-                inputs=interactive_inputs,
+                inputs=input_components,
                 outputs=prediction_output
             )