v7 plot

app.py

@@ -385,17 +385,17 @@ def predict(
     )
     fig_in.update_layout(width=1200, height=400)
 
-    # 8) Bar chart for predicted labels
+    # 8) Bar chart for predicted labels (UPDATED)
     label_df_list = []
     for lbl_col, (pred_val, _) in label_prediction_info.items():
         if lbl_col in df.columns:
             # Count how many patients in df have the predicted value
             predicted_count = len(df[df[lbl_col] == pred_val])
-            
+
             # Determine the "other" class (0 ↔ 1)
             other_val = 1 - pred_val
             other_count = len(df[df[lbl_col] == other_val])
-            
+
             label_df_list.append({
                 "Label": lbl_col,
                 "Class": f"Predicted_{pred_val}",
@@ -406,7 +406,7 @@ def predict(
                 "Class": f"Opposite_{other_val}",
                 "Count": other_count
             })
-    
+
     if label_df_list:
         bar_lbl_df = pd.DataFrame(label_df_list)
         fig_lbl = px.bar(
@@ -422,6 +422,14 @@ def predict(
         fig_lbl = px.bar(title="No valid predicted labels to display.")
         fig_lbl.update_layout(width=1200, height=400)
 
+    return (
+        final_str,         # 1) Prediction Results
+        severity_msg,      # 2) Mental Health Severity
+        total_count_md,    # 3) Total Patient Count
+        nn_md,             # 4) Nearest Neighbors Summary
+        fig_in,            # 5) Bar Chart (input features)
+        fig_lbl            # 6) Bar Chart (labels)
+    )
 
 ######################################
 # 6) UNIFIED DISTRIBUTION/CO-OCCURRENCE