Update main.py

main.py

@@ -129,7 +129,12 @@ def predict_metric():
         # Calculate net profit for each date
         for date, income in income_data.items():
             expense = expense_data.get(date, 0)
-            data.append({"date": date, "amountDue": income - expense})
+            profit_or_loss = income - expense
+            data.append({
+                "date": date, 
+                "amountDue": abs(profit_or_loss), 
+                "isLoss": profit_or_loss < 0  # Indicate if it's a loss
+            })
 
     elif metric_type == "Customer Engagement":
         # Use count of Income transactions per day as Customer Engagement
@@ -142,7 +147,7 @@ def predict_metric():
             engagement_data[date_str] = engagement_data.get(date_str, 0) + 1
 
         for date, count in engagement_data.items():
-            data.append({"date": date, "amountDue": count})
+            data.append({"date": date, "amountDue": count, "isLoss": False})
 
     # Create DataFrame from the aggregated data
     df = pd.DataFrame(data)
@@ -173,7 +178,11 @@ def predict_metric():
 
     # Extract the forecast for the requested interval
     forecast_data = forecast[['ds', 'yhat']].tail(interval)
-    predictions = [{"date": row['ds'].strftime('%Y-%m-%d'), "value": row['yhat']} for _, row in forecast_data.iterrows()]
+    predictions = [{
+        "date": row['ds'].strftime('%Y-%m-%d'),
+        "value": abs(row['yhat']),  # Use absolute value to remove negatives
+        "isLoss": row['yhat'] < 0  # Indicate if prediction is a loss
+    } for _, row in forecast_data.iterrows()]
 
     # Return predictions in JSON format
     return jsonify({"predictedData": predictions})