Update app.py

app.py

@@ -20,7 +20,7 @@ df.fillna(df.select_dtypes(include=['number']).mean(), inplace=True)
 
 # Handle missing categorical values with mode
 for col in df.select_dtypes(include=['object']).columns:
-    df[col].fillna(df[col].mode()[0], inplace=True)
+    df[col] = df[col].fillna(df[col].mode()[0])
 
 # One-hot encoding for categorical variables
 categorical_cols = [
@@ -60,10 +60,11 @@ with gr.Blocks() as demo:
     row_number_input.change(explore_data, inputs=[row_number_input], outputs=[data_output])
 
     gr.Markdown("## Make a Prediction")
-    input_data = {col: gr.Number(label=col) for col in X.columns}  # Adjust based on features
+    input_components = [gr.Number(label=col) for col in X.columns]  # Unpack the input components
     output = gr.Textbox(label="Prediction")
     
     submit_button = gr.Button("Predict")
-    submit_button.click(predict, inputs=[input_data], outputs=[output])
+    submit_button.click(predict, inputs=input_components, outputs=[output])  # Pass as separate inputs
 
 demo.launch()
+