Update main.py

main.py

@@ -289,12 +289,12 @@ def validate_and_save_transaction(uid, user_data, data, file_hash, image_bytes,
 @app.route('/api/user/spending-overview', methods=['GET'])
 def get_spending_overview():
     try:
-        # Get UID from auth token.
+        # Extract the token and verify it.
         auth_header = request.headers.get('Authorization', '')
         token = auth_header.split(' ')[1] if len(auth_header.split(' ')) > 1 else ''
         uid = verify_token(token)
         
-        # Get transactions for this user.
+        # Get transactions for the user.
         transactions_ref = db.reference('transactions')
         transactions = transactions_ref.order_by_child('uid').equal_to(uid).get()
 
@@ -304,7 +304,7 @@ def get_spending_overview():
                 'recent_transactions': []
             })
         
-        # Create DataFrame from the transactions.
+        # Create a DataFrame from the transactions.
         df = pd.DataFrame.from_dict(transactions, orient='index')
         if df.empty:
             return jsonify({
@@ -312,7 +312,7 @@ def get_spending_overview():
                 'recent_transactions': []
             })
         
-        # Drop rows missing essential fields (image_url is optional).
+        # Drop rows missing essential fields.
         df.dropna(subset=['uid', 'total', 'items', 'date', 'receipt_number', 'timestamp', 'hash', 'manual_entry'], inplace=True)
         if df.empty:
             return jsonify({
@@ -320,7 +320,7 @@ def get_spending_overview():
                 'recent_transactions': []
             })
         
-        # Parse the date column without a fixed format (allows "Fri, 12 Jun 2020 00:00:00 GMT").
+        # Parse the date column without a fixed format and drop invalid dates.
         df['date'] = pd.to_datetime(df['date'], errors='coerce')
         df.dropna(subset=['date'], inplace=True)
         if df.empty:
@@ -329,24 +329,31 @@ def get_spending_overview():
                 'recent_transactions': []
             })
         
-        # Calculate daily spending.
-        daily_spending = df.groupby(df['date'].dt.date)['total'].sum().reset_index()
-        # Replace any NaN with None.
+        # Convert the date column to ISO formatted strings (e.g. "2025-01-02T00:00:00").
+        df['date'] = df['date'].apply(lambda d: d.isoformat())
+        
+        # For daily spending, group by just the date part (YYYY-MM-DD).
+        # Extract the date part from the ISO string.
+        df['date_only'] = df['date'].apply(lambda d: d.split("T")[0])
+        daily_spending = df.groupby('date_only')['total'].sum().reset_index()
+        # Rename the column to "date" and ensure it's a string.
+        daily_spending.rename(columns={'date_only': 'date'}, inplace=True)
+        daily_spending['date'] = daily_spending['date'].astype(str)
         daily_spending = daily_spending.replace({np.nan: None})
         
-        # Get the 10 most recent transactions.
+        # Sort the transactions by timestamp (assuming it's in a sortable string format).
         recent_transactions = df.sort_values(by='timestamp', ascending=False).head(10)
-        # Replace any NaN values (e.g. for image_url) with None.
         recent_transactions = recent_transactions.replace({np.nan: None})
         
         return jsonify({
             'daily_spending': daily_spending.to_dict(orient='records'),
-            'recent_transactions': recent_transactions.to_dict(orient='records')
+            'recent_transactions': recent_transactions.drop(columns=['date_only']).to_dict(orient='records')
         })
     except Exception as e:
         return jsonify({'error': str(e)}), 500
 
 
+
 # ========================================
 # Modified verify_admin function (now checks database is_admin flag)
 # ========================================