Update main.py

main.py

@@ -105,20 +105,28 @@ def predict_revenue():
     # Fetch transaction data based on user and transaction type
     transactions_ref = db.collection("system_users").document(user_id).collection("transactions")
     query = transactions_ref.where("transactionType", "==", transaction_type).stream()
-
+    
     data = []
     for doc in query:
         transaction = doc.to_dict()
         data.append({
-            "date": transaction["date"].date(),  
+            "date": transaction["date"],  # Assuming transaction["date"] is already a datetime object
             "amountDue": transaction["amountDue"]
         })
 
     # Create DataFrame from transaction data
     df = pd.DataFrame(data)
+
+    # Ensure 'date' column is datetime
+    df['date'] = pd.to_datetime(df['date'])
+
+    # Set 'date' as index
     df = df.sort_values("date").set_index("date")
-    df = df.resample("D").sum().reset_index()  # Resample daily to ensure regular intervals
-    df.columns = ["ds", "y"]  # Rename columns for Prophet (ds: date, y: target)
+
+    # Resample daily to ensure regular intervals
+    df = df.resample("D").sum().reset_index()
+
+    df.columns = ["ds", "y"]  # ds: date, y: target
 
     # Check if there's enough data to train the model
     if df.shape[0] < 10: