Update app.py

app.py

@@ -42,21 +42,21 @@ model = xgb.XGBClassifier()
 model.fit(X_train, y_train)
 
 def predict(input_data):
-    # Remove features the user wants to omit (e.g., 'Gender')
+    # Handle missing values or intentionally omitted fields
     for col in X.columns:
         if input_data.get(col) is None:
-            input_data.pop(col, None)
-
-    # Keep only the features the user provided
-    data = pd.DataFrame([input_data], columns=input_data.keys())
-
-    # Align with model columns and fill missing required columns with defaults
-    data = data.reindex(columns=X.columns, fill_value=X.mean())
+            if X[col].dtype == 'float64':  # For numerical features
+                input_data[col] = X[col].mean()  # Use the mean for missing numerical values
+            else:  # For categorical features
+                input_data[col] = X[col].mode()[0]  # Use the mode for missing categorical values
 
+    # Convert input data to a DataFrame
+    data = pd.DataFrame([input_data], columns=X.columns)
     prediction = model.predict(data)
     return prediction[0]
 
 
+
 # Set up Gradio interface for data exploration
 def explore_data(row_number):
     return df.iloc[row_number].to_dict()
@@ -70,11 +70,14 @@ with gr.Blocks() as demo:
     row_number_input.change(explore_data, inputs=[row_number_input], outputs=[data_output])
 
     gr.Markdown("## Make a Prediction")
-    input_components = [gr.Number(label=col) for col in X.columns]  # Unpack the input components
+
+    # Create a dictionary for input components
+    input_components = {col: gr.Number(label=col) for col in X.columns}  # Generate number inputs for each column
+    
     output = gr.Textbox(label="Prediction")
     
     submit_button = gr.Button("Predict")
-    submit_button.click(predict, inputs=input_components, outputs=[output])  # Pass as separate inputs
+    submit_button.click(predict, inputs=[input_components], outputs=[output])  # Pass the dictionary of inputs
 
 demo.launch()