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Enhanced import functions

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nngo
2025-10-10 14:17:19 -04:00
parent 16f7b3f68d
commit b95b614ac9
2 changed files with 144 additions and 44 deletions
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templates/time_attendance_import.html
+1 -1
View File
@@ -401,7 +401,7 @@
</div>
<div class="checkbox-item">
<input type="checkbox" id="validate_only" name="validate_only" value="true" checked>
<input type="checkbox" id="validate_only" name="validate_only" value="true">
<label for="validate_only" class="checkbox-label">
<strong>Validate Only (Don't Import)</strong>
<span>Check file for errors without actually importing the data</span>
time_attendance_import_service.py
+138 -38
View File
@@ -39,12 +39,73 @@ class TimeAttendanceImportService:
'status': status
})
def _read_excel_with_formulas(self, file_path: str) -> pd.DataFrame:
"""
Read Excel file preserving HYPERLINK formulas in Recorded Address column
Uses openpyxl to extract formulas, then creates DataFrame
Args:
file_path: Path to the Excel file
Returns:
DataFrame with formulas preserved
"""
from openpyxl import load_workbook
# Load workbook with openpyxl to get formulas (data_only=False preserves formulas)
wb = load_workbook(file_path, data_only=False)
ws = wb.active
# Get header row
headers = []
for cell in ws[1]:
headers.append(cell.value)
# Find the index of 'Recorded Address' column
recorded_address_idx = None
try:
recorded_address_idx = headers.index('Recorded Address')
except ValueError:
pass # Column doesn't exist
# Read all data rows
data_rows = []
for row in ws.iter_rows(min_row=2, values_only=False):
row_data = []
for col_idx, cell in enumerate(row):
# For Recorded Address column, preserve the formula if it exists
if col_idx == recorded_address_idx and cell.value:
# Check if cell contains a formula
if isinstance(cell.value, str) and cell.value.startswith('='):
# This is a formula, keep it as-is
row_data.append(cell.value)
if self.logger:
self.logger.logger.debug(f"Found formula in Recorded Address: {cell.value[:60]}...")
else:
# Regular value
row_data.append(cell.value)
else:
# For other columns, just get the value
row_data.append(cell.value)
# Skip completely empty rows
if any(val is not None for val in row_data):
data_rows.append(row_data)
# Create DataFrame
df = pd.DataFrame(data_rows, columns=headers)
if self.logger:
self.logger.logger.info(f"Read Excel with formulas preserved: {len(df)} rows, {len(headers)} columns")
return df
def _parse_excel_hyperlink(self, cell_value: str) -> str:
"""
Parse Excel HYPERLINK formula to extract the display text (address)
Handles formats like:
- =HYPERLINK("http://maps.google.com/maps?q=[lat],[long]","123 Maint Street")
- =HYPERLINK("http://maps.google.com/maps?q=38.8769894000,-77.2220616000","2815 Hartland Road, Falls Church, VA 22043")
- Regular text (no formula)
Args:
@@ -56,42 +117,45 @@ class TimeAttendanceImportService:
if not cell_value or not isinstance(cell_value, str):
return cell_value
cell_value = cell_value.strip()
# Check if it's a HYPERLINK formula
if cell_value.strip().startswith('=HYPERLINK('):
if cell_value.startswith('=HYPERLINK('):
try:
# Extract content between HYPERLINK parentheses
# Pattern: =HYPERLINK("url","display_text")
import re
# Match the display text (second quoted string)
# Pattern: =HYPERLINK("url","display_text")
pattern = r'=HYPERLINK\s*\(\s*"[^"]*"\s*,\s*"([^"]*)"\s*\)'
match = re.search(pattern, cell_value)
if match:
address_text = match.group(1)
address_text = match.group(1).strip()
if self.logger:
self.logger.logger.debug(f"Parsed HYPERLINK: {cell_value[:50]}... -> {address_text}")
return address_text.strip()
self.logger.logger.debug(f"Parsed HYPERLINK: '{cell_value[:60]}...' -> '{address_text}'")
return address_text
else:
# If pattern doesn't match, try to extract any quoted text after comma
parts = cell_value.split(',', 1)
if len(parts) > 1:
# Get text between last quotes
text_part = parts[1].strip().rstrip(')')
if '"' in text_part:
# Extract text between quotes
address_text = text_part.split('"')[1] if text_part.count('"') >= 2 else text_part
if self.logger:
self.logger.logger.debug(f"Parsed HYPERLINK (fallback): {cell_value[:50]}... -> {address_text}")
return address_text.strip()
# Fallback: try to extract text between last pair of quotes
# Find all quoted strings
quoted_strings = re.findall(r'"([^"]*)"', cell_value)
if len(quoted_strings) >= 2:
# The address is typically the last quoted string
address_text = quoted_strings[-1].strip()
if self.logger:
self.logger.logger.debug(f"Parsed HYPERLINK (fallback): '{cell_value[:60]}...' -> '{address_text}'")
return address_text
else:
if self.logger:
self.logger.logger.warning(f"Could not parse HYPERLINK formula: {cell_value[:60]}...")
return None
except Exception as e:
if self.logger:
self.logger.logger.warning(f"Failed to parse HYPERLINK formula: {cell_value[:50]}... Error: {e}")
# Return original if parsing fails
return cell_value
self.logger.logger.error(f"Failed to parse HYPERLINK formula: {cell_value[:60]}... Error: {e}")
return None
# Not a hyperlink formula, return as-is
return cell_value.strip() if isinstance(cell_value, str) else cell_value
return cell_value if cell_value else None
def _process_recorded_address(self, row):
"""
@@ -103,13 +167,28 @@ class TimeAttendanceImportService:
Returns:
Parsed address string or None
"""
recorded_address_value = row.get('Recorded Address', '')
recorded_address_value = row.get('Recorded Address')
# Check if value exists and is not NaN
if pd.notna(recorded_address_value):
# Convert to string and parse if it's a HYPERLINK formula
# Convert to string
address_str = str(recorded_address_value).strip()
# Skip if empty or the string "nan"
if not address_str or address_str.lower() == 'nan':
return None
# Parse the value (handles both formulas and plain text)
parsed_address = self._parse_excel_hyperlink(address_str)
return parsed_address if parsed_address else None
if parsed_address and parsed_address.strip():
if self.logger:
self.logger.logger.debug(f"Processed Recorded Address: '{address_str[:60]}...' -> '{parsed_address}'")
return parsed_address.strip()
else:
if self.logger:
self.logger.logger.warning(f"Empty result after parsing Recorded Address: '{address_str[:60]}...'")
return None
return None
@@ -134,9 +213,7 @@ class TimeAttendanceImportService:
try:
# Read Excel file
excel_file = pd.ExcelFile(file_path)
sheet_name = excel_file.sheet_names[0]
df = pd.read_excel(file_path, sheet_name=sheet_name)
df = self._read_excel_with_formulas(file_path)
# Validate required columns
required_columns = ['ID', 'Name', 'Date', 'Time', 'Location Name', 'Action Description']
@@ -248,9 +325,7 @@ class TimeAttendanceImportService:
try:
# Read Excel file
excel_file = pd.ExcelFile(file_path)
sheet_name = excel_file.sheet_names[0]
df = pd.read_excel(file_path, sheet_name=sheet_name)
df = self._read_excel_with_formulas(file_path)
# Validate required columns
required_columns = ['ID', 'Name', 'Date', 'Time', 'Location Name', 'Action Description']
@@ -406,12 +481,10 @@ class TimeAttendanceImportService:
# Read Excel file
try:
excel_file = pd.ExcelFile(file_path)
sheet_name = excel_file.sheet_names[0]
df = pd.read_excel(file_path, sheet_name=sheet_name)
df = self._read_excel_with_formulas(file_path)
if self.logger:
self.logger.logger.info(f"Reading sheet: {sheet_name} with {len(df)} rows")
self.logger.logger.info(f"Read Excel file with {len(df)} rows and formulas preserved")
except Exception as e:
error_msg = f"Failed to read Excel file: {str(e)}"
import_results['errors'].append(error_msg)
@@ -709,7 +782,8 @@ class TimeAttendanceImportService:
sample_rows = df.head(5).to_dict('records')
validation_results['sample_data'] = sample_rows
required_columns = ['ID', 'Date', 'Time', 'Location Name', 'Action Description', 'Event Description', 'Recorded Address']
# Define ONLY truly required columns (ID, Name, Date, Time, Location Name, Action Description)
required_columns = ['ID', 'Name', 'Date', 'Time', 'Location Name', 'Action Description']
missing_columns = [col for col in required_columns if col not in df.columns]
if missing_columns:
@@ -717,25 +791,51 @@ class TimeAttendanceImportService:
f"Missing required columns: {', '.join(missing_columns)}"
)
# Count valid rows by checking if ALL required fields have values
valid_row_count = 0
invalid_row_details = []
for index, row in df.iterrows():
is_valid = True
missing_fields = []
# Check each required column
for col in required_columns:
if col in df.columns and pd.isna(row[col]):
if col in df.columns:
if pd.isna(row[col]):
is_valid = False
missing_fields.append(col)
else:
# Column doesn't exist in file
is_valid = False
break
missing_fields.append(f"{col} (column not found)")
if is_valid:
valid_row_count += 1
else:
# Track invalid row for detailed reporting
invalid_row_details.append({
'row': index + 2, # +2 for header and 0-based index
'missing': missing_fields
})
validation_results['valid_rows'] = valid_row_count
validation_results['invalid_rows'] = len(df) - valid_row_count
if validation_results['invalid_rows'] > 0:
# Provide detailed warning about invalid rows
validation_results['warnings'].append(
f"{validation_results['invalid_rows']} rows have missing required data"
)
# Add details about first few invalid rows for debugging
if invalid_row_details:
sample_invalid = invalid_row_details[:3] # Show first 3 invalid rows
details_msg = "Examples: "
for detail in sample_invalid:
details_msg += f"Row {detail['row']} (missing: {', '.join(detail['missing'])}); "
validation_results['warnings'].append(details_msg.rstrip('; '))
if 'Date' in df.columns:
invalid_dates = 0
for idx, date_val in df['Date'].items():