diff --git a/templates/time_attendance_dashboard.html b/templates/time_attendance_dashboard.html index b28469d..9853270 100644 --- a/templates/time_attendance_dashboard.html +++ b/templates/time_attendance_dashboard.html @@ -94,7 +94,6 @@ - @@ -110,9 +109,6 @@ {% for record in recent_records %} - - -
# ID Name Platform
{{ loop.index }}
diff --git a/templates/time_attendance_import.html b/templates/time_attendance_import.html index 0d74e5e..a39a743 100644 --- a/templates/time_attendance_import.html +++ b/templates/time_attendance_import.html @@ -275,7 +275,7 @@

Required Columns

-

ID, Name, Date, Time, Location Name, Action Description

+

ID, Date, Time, Location Name, Action Description

diff --git a/time_attendance_import_service.py b/time_attendance_import_service.py index 20d7f98..0832155 100644 --- a/time_attendance_import_service.py +++ b/time_attendance_import_service.py @@ -216,7 +216,7 @@ class TimeAttendanceImportService: df = self._read_excel_with_formulas(file_path) # Validate required columns - required_columns = ['ID', 'Name', 'Date', 'Time', 'Location Name', 'Action Description'] + required_columns = ['ID', 'Date', 'Time', 'Location Name', 'Action Description'] missing_columns = [col for col in required_columns if col not in df.columns] if missing_columns: @@ -245,18 +245,29 @@ class TimeAttendanceImportService: ) try: - # Skip empty rows - if pd.isna(row['ID']) or pd.isna(row['Name']): + # Skip empty rows - only ID is required + if pd.isna(row['ID']): continue + # Clean the employee ID first (handles float issues like '1234.0') + clean_id = self._clean_employee_id(row['ID']) + + # Get employee name - either from Excel or lookup from employee table + employee_name = None + if 'Name' in df.columns and pd.notna(row.get('Name')): + employee_name = str(row['Name']).strip() + else: + # Lookup employee name from employee table using cleaned ID + employee_name = self._get_employee_name(clean_id) + # Parse date and time attendance_date = pd.to_datetime(row['Date']).date() attendance_time = self._parse_time_field(row['Time']) # Prepare record data record_data = { - 'employee_id': str(row['ID']).strip(), - 'employee_name': str(row['Name']).strip(), + 'employee_id': clean_id, + 'employee_name': employee_name, 'platform': str(row.get('Platform', '')).strip() if pd.notna(row.get('Platform')) else None, 'attendance_date': attendance_date, 'attendance_time': attendance_time, @@ -328,7 +339,7 @@ class TimeAttendanceImportService: df = self._read_excel_with_formulas(file_path) # Validate required columns - required_columns = ['ID', 'Name', 'Date', 'Time', 'Location Name', 'Action Description'] + required_columns = ['ID', 'Date', 'Time', 'Location Name', 'Action Description'] missing_columns = [col for col in required_columns if col not in df.columns] if missing_columns: @@ -348,26 +359,25 @@ class TimeAttendanceImportService: row_data = { 'employee_id': None, 'employee_name': None, - 'platform': None, 'attendance_date': None, 'attendance_time': None, 'location_name': None, - 'action_description': None, - 'event_description': None, - 'recorded_address': None + 'action_description': None } - # Check ID + # Validate ID (required) if pd.isna(row['ID']): - row_errors.append("Missing Employee ID") + row_errors.append('Missing ID') else: - row_data['employee_id'] = str(row['ID']).strip() + # Clean the employee ID (handles float issues) + row_data['employee_id'] = self._clean_employee_id(row['ID']) - # Check Name - if pd.isna(row['Name']): - row_errors.append("Missing Employee Name") - else: + # Get Name (optional - lookup if not provided) + if 'Name' in df.columns and pd.notna(row.get('Name')): row_data['employee_name'] = str(row['Name']).strip() + elif row_data['employee_id']: + # Lookup employee name from employee table using cleaned ID + row_data['employee_name'] = self._get_employee_name(row_data['employee_id']) # Check and parse Date if pd.isna(row['Date']): @@ -493,7 +503,7 @@ class TimeAttendanceImportService: return import_results # Validate required columns - required_columns = ['ID', 'Name', 'Date', 'Time', 'Location Name', 'Action Description'] + required_columns = ['ID', 'Date', 'Time', 'Location Name', 'Action Description'] missing_columns = [col for col in required_columns if col not in df.columns] if missing_columns: @@ -520,12 +530,23 @@ class TimeAttendanceImportService: # Process each row with enhanced validation for index, row in df.iterrows(): try: - # Skip empty rows - if pd.isna(row['ID']) or pd.isna(row['Name']): + # Skip empty rows - only ID is required + if pd.isna(row['ID']): import_results['skipped_records'] += 1 - import_results['warnings'].append(f"Row {index + 2}: Skipped due to missing ID or Name") + import_results['warnings'].append(f"Row {index + 2}: Skipped due to missing ID") continue + # Clean the employee ID first (handles float issues like '1234.0') + clean_id = self._clean_employee_id(row['ID']) + + # Get employee name - either from Excel or lookup from employee table + employee_name = None + if 'Name' in df.columns and pd.notna(row.get('Name')): + employee_name = str(row['Name']).strip() + else: + # Lookup employee name from employee table using cleaned ID + employee_name = self._get_employee_name(clean_id) + # Validate and parse date try: attendance_date = pd.to_datetime(row['Date']).date() @@ -544,8 +565,8 @@ class TimeAttendanceImportService: # Prepare record data record_data = { - 'employee_id': str(row['ID']).strip(), - 'employee_name': str(row['Name']).strip(), + 'employee_id': clean_id, + 'employee_name': employee_name, 'platform': str(row.get('Platform', '')).strip() if pd.notna(row.get('Platform')) else None, 'attendance_date': attendance_date, 'attendance_time': attendance_time, @@ -668,6 +689,58 @@ class TimeAttendanceImportService: raise ValueError(f"Unable to parse time value: {time_value}") + def _get_employee_name(self, employee_id: str) -> str: + try: + from models.employee import Employee + + # CRITICAL: Clean the employee_id to handle float values like '1234.0' + # Remove '.0' suffix if present and convert to integer + cleaned_id = str(employee_id).strip() + + # If it's a float string like '1234.0', remove the decimal part + if '.' in cleaned_id: + try: + # Convert to float first, then to int to handle '1234.0' -> 1234 + cleaned_id = str(int(float(cleaned_id))) + except (ValueError, TypeError): + pass # Keep original if conversion fails + + # Now lookup the employee + employee = Employee.get_by_employee_id(int(cleaned_id)) + if employee: + return employee.full_name + else: + if self.logger: + self.logger.logger.warning(f"Employee ID {cleaned_id} not found in employee table") + return f"Employee {cleaned_id}" + except Exception as e: + if self.logger: + self.logger.logger.warning(f"Could not lookup employee name for ID {employee_id}: {e}") + # Return cleaned ID in error message too + try: + cleaned_id = str(int(float(str(employee_id).strip()))) + return f"Employee {cleaned_id}" + except: + return f"Employee {employee_id}" + + def _clean_employee_id(self, employee_id) -> str: + try: + # Convert to string first + id_str = str(employee_id).strip() + + # Handle float values like 1234.0 or '1234.0' + if '.' in id_str: + # Convert to float, then to int, then back to string + # This removes the decimal part: 1234.0 -> 1234 + id_str = str(int(float(id_str))) + + return id_str + except (ValueError, TypeError) as e: + # If conversion fails, return original string + if self.logger: + self.logger.logger.warning(f"Could not clean employee ID '{employee_id}': {e}") + return str(employee_id).strip() + def _generate_record_hash(self, record_data: Dict) -> str: """Generate unique hash for a record to detect duplicates""" hash_string = ( @@ -784,7 +857,7 @@ class TimeAttendanceImportService: validation_results['sample_data'] = sample_rows # Define ONLY truly required columns (ID, Name, Date, Time, Location Name, Action Description) - required_columns = ['ID', 'Name', 'Date', 'Time', 'Location Name', 'Action Description'] + required_columns = ['ID', 'Date', 'Time', 'Location Name', 'Action Description'] missing_columns = [col for col in required_columns if col not in df.columns] if missing_columns: