Update main.py

main.py

@@ -129,12 +129,7 @@ def predict_metric():
         # Calculate net profit for each date
         for date, income in income_data.items():
             expense = expense_data.get(date, 0)
-            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
-            })
+            data.append({"date": date, "amountDue": income - expense})
 
     elif metric_type == "Customer Engagement":
         # Use count of Income transactions per day as Customer Engagement
@@ -147,7 +142,7 @@ 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, "isLoss": False})
+            data.append({"date": date, "amountDue": count})
 
     # Create DataFrame from the aggregated data
     df = pd.DataFrame(data)
@@ -178,16 +173,13 @@ 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": 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()]
+    predictions = [{"date": row['ds'].strftime('%Y-%m-%d'), "value": row['yhat']} for _, row in forecast_data.iterrows()]
 
     # Return predictions in JSON format
     return jsonify({"predictedData": predictions})
 
 
 
+
 if __name__ == "__main__":
     app.run(debug=True, host="0.0.0.0", port=7860)