mirror of
https://github.com/DRYTRIX/TimeTracker.git
synced 2026-01-05 19:20:21 -06:00
b353184a4f
Implement complete Advanced Expense Management feature set with UI templates, database schema fixes, and reorganized navigation structure. Features: - Expense Categories: Full CRUD with budget tracking and visualization - Mileage Tracking: Vehicle mileage entries with approval workflow - Per Diem Management: Daily allowance claims with location-based rates - Receipt OCR: Infrastructure for receipt scanning (utilities ready) Database: - Migration 037: Create expense_categories, mileage, per_diem_rates, per_diems tables - Migration 038: Fix schema column name mismatches (trip_purpose→purpose, etc.) - Add missing columns (description, odometer, rates, reimbursement tracking) - Fix circular foreign key dependencies Templates (11 new files): - expense_categories/: list, form, view - mileage/: list, form, view - per_diem/: list, form, view, rates_list, rate_form Navigation: - Move Mileage and Per Diem to Expenses sub-pages (header buttons) - Move Expense Categories to Admin menu only - Remove expense management items from Finance menu Fixes: - Fix NoneType comparison error in expense categories utilization - Handle None values safely in budget progress bars - Resolve database column name mismatches UI/UX: - Responsive design with Tailwind CSS and dark mode support - Real-time calculations for mileage amounts - Color-coded budget utilization progress bars - Status badges for approval workflow states - Advanced filtering on all list views Default data: - 7 expense categories (Travel, Meals, Accommodation, etc.) - 4 per diem rates (US, GB, DE, FR)
345 lines
11 KiB
Python
345 lines
11 KiB
Python
"""
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OCR utilities for receipt scanning and text extraction.
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This module provides functionality to extract text and data from receipt images
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using Tesseract OCR and parse common receipt information.
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"""
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import os
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import re
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from decimal import Decimal
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from datetime import datetime
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import logging
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logger = logging.getLogger(__name__)
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# Check if Tesseract is available
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try:
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import pytesseract
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from PIL import Image
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TESSERACT_AVAILABLE = True
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except ImportError:
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TESSERACT_AVAILABLE = False
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logger.warning("pytesseract or PIL not installed. Receipt OCR will not be available.")
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def is_ocr_available():
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"""Check if OCR functionality is available"""
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return TESSERACT_AVAILABLE
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def extract_text_from_image(image_path, lang='eng'):
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"""
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Extract text from an image using Tesseract OCR.
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Args:
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image_path: Path to the image file
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lang: OCR language (default: 'eng', can be 'eng+deu' for multilingual)
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Returns:
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Extracted text as string
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"""
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if not TESSERACT_AVAILABLE:
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raise RuntimeError("Tesseract OCR is not available. Install pytesseract and PIL.")
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try:
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# Open and preprocess image
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image = Image.open(image_path)
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# Convert to RGB if necessary
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if image.mode != 'RGB':
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image = image.convert('RGB')
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# Extract text
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text = pytesseract.image_to_string(image, lang=lang)
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return text
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except Exception as e:
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logger.error(f"Error extracting text from image {image_path}: {e}")
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raise
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def parse_receipt_data(text):
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"""
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Parse common receipt information from extracted text.
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Args:
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text: Extracted text from receipt
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Returns:
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Dictionary with parsed data (vendor, date, total, items, etc.)
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"""
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data = {
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'vendor': None,
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'date': None,
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'total': None,
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'tax': None,
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'subtotal': None,
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'items': [],
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'currency': 'EUR',
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'raw_text': text
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}
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lines = text.split('\n')
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# Try to extract vendor (usually first few lines)
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vendor_lines = []
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for line in lines[:5]:
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line = line.strip()
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if line and len(line) > 3:
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vendor_lines.append(line)
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if vendor_lines:
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data['vendor'] = vendor_lines[0]
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# Extract amounts
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amounts = extract_amounts(text)
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if amounts:
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# Try to identify total (usually largest amount or labeled as total)
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total_candidates = []
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for amount_info in amounts:
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label = amount_info.get('label', '').lower()
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if any(keyword in label for keyword in ['total', 'gesamt', 'suma', 'totale']):
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data['total'] = amount_info['amount']
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elif any(keyword in label for keyword in ['tax', 'vat', 'mwst', 'iva', 'tva']):
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data['tax'] = amount_info['amount']
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elif any(keyword in label for keyword in ['subtotal', 'zwischensumme', 'sous-total']):
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data['subtotal'] = amount_info['amount']
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else:
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total_candidates.append(amount_info['amount'])
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# If no labeled total found, use the largest amount
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if not data['total'] and total_candidates:
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data['total'] = max(total_candidates)
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# Extract date
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date = extract_date(text)
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if date:
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data['date'] = date
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# Extract currency
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currency = extract_currency(text)
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if currency:
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data['currency'] = currency
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return data
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def extract_amounts(text):
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"""
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Extract monetary amounts from text.
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Returns:
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List of dictionaries with 'amount' and 'label' keys
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"""
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amounts = []
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# Patterns for amounts (supports various formats)
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# Examples: 12.34, 12,34, $12.34, €12,34, 12.34 EUR
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patterns = [
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r'([A-Za-z\s]*?)\s*([$€£¥]?)\s*(\d{1,3}(?:[.,]\d{3})*[.,]\d{2})\s*([A-Z]{3})?',
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]
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for pattern in patterns:
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matches = re.finditer(pattern, text, re.IGNORECASE | re.MULTILINE)
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for match in matches:
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label = match.group(1).strip() if match.group(1) else ''
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symbol = match.group(2) if match.group(2) else ''
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amount_str = match.group(3)
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currency = match.group(4) if match.group(4) else ''
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# Normalize amount (convert comma to dot if needed)
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# Determine if comma or dot is decimal separator
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if ',' in amount_str and '.' in amount_str:
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# Has both, assume European format (1.234,56)
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amount_str = amount_str.replace('.', '').replace(',', '.')
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elif ',' in amount_str:
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# Only comma, check if it's thousands separator or decimal
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parts = amount_str.split(',')
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if len(parts) == 2 and len(parts[1]) == 2:
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# Likely decimal separator
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amount_str = amount_str.replace(',', '.')
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else:
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# Likely thousands separator
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amount_str = amount_str.replace(',', '')
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try:
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amount = Decimal(amount_str)
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amounts.append({
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'amount': amount,
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'label': label,
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'symbol': symbol,
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'currency': currency
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})
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except (ValueError, Decimal.InvalidOperation):
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continue
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return amounts
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def extract_date(text):
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"""
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Extract date from receipt text.
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Returns:
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datetime.date object or None
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"""
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# Common date patterns
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patterns = [
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r'(\d{1,2})[./\-](\d{1,2})[./\-](\d{2,4})', # DD/MM/YYYY or MM/DD/YYYY
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r'(\d{4})[./\-](\d{1,2})[./\-](\d{1,2})', # YYYY-MM-DD
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r'(\d{1,2})\s+(Jan|Feb|Mar|Apr|May|Jun|Jul|Aug|Sep|Oct|Nov|Dec)[a-z]*\s+(\d{2,4})', # DD Month YYYY
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]
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for pattern in patterns:
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match = re.search(pattern, text, re.IGNORECASE)
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if match:
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try:
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groups = match.groups()
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if len(groups) == 3:
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if pattern == patterns[0]: # DD/MM/YYYY or MM/DD/YYYY
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# Try DD/MM/YYYY first (European format)
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try:
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day, month, year = int(groups[0]), int(groups[1]), int(groups[2])
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if year < 100:
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year += 2000
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return datetime(year, month, day).date()
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except ValueError:
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# Try MM/DD/YYYY (US format)
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try:
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month, day, year = int(groups[0]), int(groups[1]), int(groups[2])
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if year < 100:
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year += 2000
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return datetime(year, month, day).date()
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except ValueError:
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continue
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elif pattern == patterns[1]: # YYYY-MM-DD
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year, month, day = int(groups[0]), int(groups[1]), int(groups[2])
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return datetime(year, month, day).date()
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elif pattern == patterns[2]: # DD Month YYYY
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day = int(groups[0])
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month_str = groups[1].lower()
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year = int(groups[2])
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if year < 100:
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year += 2000
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months = {
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'jan': 1, 'feb': 2, 'mar': 3, 'apr': 4,
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'may': 5, 'jun': 6, 'jul': 7, 'aug': 8,
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'sep': 9, 'oct': 10, 'nov': 11, 'dec': 12
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}
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month = months.get(month_str[:3])
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if month:
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return datetime(year, month, day).date()
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except (ValueError, TypeError):
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continue
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return None
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def extract_currency(text):
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"""
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Extract currency code from receipt text.
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Returns:
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3-letter currency code (ISO 4217) or 'EUR' as default
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"""
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# Currency symbols and their codes
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currency_symbols = {
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'$': 'USD',
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'€': 'EUR',
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'£': 'GBP',
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'¥': 'JPY',
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'₹': 'INR',
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'Fr': 'CHF'
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}
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# Look for currency symbols
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for symbol, code in currency_symbols.items():
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if symbol in text:
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return code
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# Look for currency codes (3 uppercase letters)
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currency_pattern = r'\b([A-Z]{3})\b'
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matches = re.findall(currency_pattern, text)
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# Common currency codes
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common_currencies = ['USD', 'EUR', 'GBP', 'JPY', 'CHF', 'CAD', 'AUD', 'INR']
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for match in matches:
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if match in common_currencies:
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return match
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return 'EUR' # Default
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def scan_receipt(image_path, lang='eng'):
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"""
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Scan a receipt image and extract structured data.
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Args:
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image_path: Path to the receipt image
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lang: OCR language(s) to use (e.g., 'eng', 'eng+deu')
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Returns:
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Dictionary with extracted receipt data
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"""
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if not is_ocr_available():
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return {
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'error': 'OCR not available',
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'message': 'Please install pytesseract and Pillow: pip install pytesseract pillow'
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}
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try:
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# Extract text
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text = extract_text_from_image(image_path, lang=lang)
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# Parse data
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data = parse_receipt_data(text)
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return data
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except Exception as e:
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logger.error(f"Error scanning receipt {image_path}: {e}")
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return {
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'error': str(e),
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'message': 'Failed to scan receipt'
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}
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def get_suggested_expense_data(receipt_data):
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"""
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Convert receipt data to expense form data suggestions.
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Args:
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receipt_data: Dictionary returned by scan_receipt()
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Returns:
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Dictionary with suggested expense data
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"""
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suggestions = {}
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if receipt_data.get('vendor'):
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suggestions['vendor'] = receipt_data['vendor']
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suggestions['title'] = f"Receipt from {receipt_data['vendor']}"
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if receipt_data.get('total'):
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suggestions['amount'] = float(receipt_data['total'])
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if receipt_data.get('tax'):
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suggestions['tax_amount'] = float(receipt_data['tax'])
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if receipt_data.get('date'):
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suggestions['expense_date'] = receipt_data['date'].isoformat()
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if receipt_data.get('currency'):
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suggestions['currency_code'] = receipt_data['currency']
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return suggestions
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