bar plot

app.py

@@ -386,32 +386,42 @@ def predict(
     fig_in.update_layout(width=1200, height=400)
 
     # 8) Bar chart for predicted labels
-    label_counts = {}
+    label_df_list = []
     for lbl_col, (pred_val, _) in label_prediction_info.items():
         if lbl_col in df.columns:
-            label_counts[lbl_col] = len(df[df[lbl_col] == pred_val])
-    if label_counts:
-        bar_lbl_df = pd.DataFrame({
-            "Label": list(label_counts.keys()),
-            "Count": list(label_counts.values())
-        })
+            # 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}",
+                "Count": predicted_count
+            })
+            label_df_list.append({
+                "Label": lbl_col,
+                "Class": f"Opposite_{other_val}",
+                "Count": other_count
+            })
+    
+    if label_df_list:
+        bar_lbl_df = pd.DataFrame(label_df_list)
         fig_lbl = px.bar(
-            bar_lbl_df, x="Label", y="Count",
-            title="Number of Patients with the Same Predicted Label"
+            bar_lbl_df,
+            x="Label",
+            y="Count",
+            color="Class",
+            barmode="group",
+            title="Number of Patients with the Predicted vs. Opposite Label"
         )
         fig_lbl.update_layout(width=1200, height=400)
     else:
         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