Update main.py

main.py

@@ -116,15 +116,17 @@ def predict_metric():
 
         for doc in income_query:
             transaction = doc.to_dict()
-            date_str = transaction["date"]
+            date_str = transaction["date"].toDate()  # Convert Firestore Timestamp to DateTime
             amount = transaction["amountDue"]
             income_data[date_str] = income_data.get(date_str, 0) + amount
+            print(f"Income transaction - Date: {date_str}, Amount: {amount}")
 
         for doc in expense_query:
             transaction = doc.to_dict()
-            date_str = transaction["date"]
+            date_str = transaction["date"].toDate()  # Convert Firestore Timestamp to DateTime
             amount = transaction["amountDue"]
             expense_data[date_str] = expense_data.get(date_str, 0) + amount
+            print(f"Expense transaction - Date: {date_str}, Amount: {amount}")
 
         # Calculate net profit for each date
         for date, income in income_data.items():
@@ -138,8 +140,9 @@ def predict_metric():
         engagement_data = {}
         for doc in income_query:
             transaction = doc.to_dict()
-            date_str = transaction["date"]
+            date_str = transaction["date"].toDate()  # Convert Firestore Timestamp to DateTime
             engagement_data[date_str] = engagement_data.get(date_str, 0) + 1
+            print(f"Engagement transaction - Date: {date_str}")
 
         for date, count in engagement_data.items():
             data.append({"date": date, "amountDue": count})
@@ -147,20 +150,33 @@ def predict_metric():
     # Create DataFrame from the aggregated data
     df = pd.DataFrame(data)
 
+    # Log the DataFrame before further processing
+    print("Data before processing:", df)
+
     # Ensure 'date' column is datetime
-    df['date'] = pd.to_datetime(df['date'])
+    df['date'] = pd.to_datetime(df['date'], errors='coerce')
     df['date'] = df['date'].dt.tz_localize(None)
 
+    # Log DataFrame after date conversion
+    print("Data after date conversion:", df)
+
+    # Drop rows where 'date' could not be parsed
+    df = df.dropna(subset=['date'])
+
     # Set 'date' as index
     df = df.sort_values("date").set_index("date")
 
     # Resample daily to ensure regular intervals (fill missing dates)
     df = df.resample("D").sum().reset_index()
 
+    # Log DataFrame after resampling
+    print("Data after resampling:", df)
+
     df.columns = ["ds", "y"]  # ds: date, y: target
 
     # Check if there's enough data to train the model
     if df.shape[0] < 10:
+        print("Not enough data for prediction")
         return jsonify({"error": "Not enough data for prediction"})
 
     # Initialize and fit the Prophet model
@@ -175,6 +191,9 @@ def predict_metric():
     forecast_data = forecast[['ds', 'yhat']].tail(interval)
     predictions = [{"date": row['ds'].strftime('%Y-%m-%d'), "value": row['yhat']} for _, row in forecast_data.iterrows()]
 
+    # Log the predictions before returning
+    print("Predictions:", predictions)
+
     # Return predictions in JSON format
     return jsonify({"predictedData": predictions})