Update app.py

app.py

@@ -1040,46 +1040,48 @@ elif app_mode == "Model Training":
     use_grid_search = st.checkbox("Use Grid Search for Hyperparameter Tuning")
 
     # In Model Training section - Fix indentation for training logic
-    if st.button("Train Model"):
-        if not features:
-            st.error("Please select at least one feature.")
-            st.stop()
-    
-        # INDENT ALL THIS CODE UNDER THE BUTTON CLICK
-        # Call the training function
-        model, scaler, label_encoder, imputer_numerical, metrics, column_order, importance, X_train, y_train = train_model(
-            df.copy(), target, features, problem_type, test_size, model_type, model_params, use_grid_search
-        )
+   if st.button("Train Model"):
+    if not features:
+        st.error("Please select at least one feature.")
+        st.stop()
+
+    # INDENT ALL THIS CODE UNDER THE BUTTON CLICK
+    # Call the training function
+    model, scaler, label_encoder, imputer_numerical, metrics, column_order, importance, X_train, y_train = train_model(
+        df.copy(), target, features, problem_type, test_size, model_type, model_params, use_grid_search
+    )
 
     if model:  # Only proceed if training was successful
         st.success("Model trained successfully!")
-        # ... rest of model display code ...
-
-            # Display Metrics
-            st.subheader("Model Evaluation Metrics")
-            if problem_type in ["Classification", "Multiclass"]: #Combined here
-                st.metric("Accuracy", f"{metrics['accuracy']:.2%}")
-
-                # Confusion Matrix Visualization
-                st.subheader("Confusion Matrix")
-                cm = metrics['confusion_matrix']
-                class_names = [str(i) for i in np.unique(df[target])] #Get original class names
-                fig_cm = px.imshow(cm,
-                                    labels=dict(x="Predicted", y="Actual"),
-                                    x=class_names,
-                                    y=class_names,
-                                    color_continuous_scale="Viridis")
-                st.plotly_chart(fig_cm, use_container_width=True)
-
-                # Classification Report
-                st.subheader("Classification Report")
-                report = metrics['classification_report']
-                report_df = pd.DataFrame(report).transpose()
-                st.dataframe(report_df)
 
-            else:
-                st.metric("MSE", f"{metrics['mse']:.2f}")
-                st.metric("R2", f"{metrics['r2']:.2f}")
+        # Display Metrics
+        st.subheader("Model Evaluation Metrics")
+        if problem_type in ["Classification", "Multiclass"]:  # Combined here
+            st.metric("Accuracy", f"{metrics['accuracy']:.2%}")
+
+            # Confusion Matrix Visualization
+            st.subheader("Confusion Matrix")
+            cm = metrics['confusion_matrix']
+            class_names = [str(i) for i in np.unique(df[target])]  # Get original class names
+            fig_cm = px.imshow(cm,
+                               labels=dict(x="Predicted", y="Actual"),
+                               x=class_names,
+                               y=class_names,
+                               color_continuous_scale="Viridis")
+            st.plotly_chart(fig_cm, use_container_width=True)
+
+            # Classification Report
+            st.subheader("Classification Report")
+            report = metrics['classification_report']
+            report_df = pd.DataFrame(report).transpose()
+            st.dataframe(report_df)
+
+        else:
+            st.metric("MSE", f"{metrics['mse']:.2f}")
+            st.metric("R2", f"{metrics['r2']:.2f}")
+
+        # Additional model display code...
+
 
             # Feature Importance
             st.subheader("Feature Importance")