Initial commit

app/auto_tagger.py

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+"""
+Smart Auto-Tagging Utility
+Automatically generates tags for expenses based on OCR text and description
+"""
+import re
+from typing import List, Dict
+
+# Tag patterns for auto-detection
+TAG_PATTERNS = {
+    # Food & Dining
+    'dining': {
+        'keywords': ['restaurant', 'cafe', 'bistro', 'diner', 'eatery', 'pizz', 'burger', 'sushi', 
+                    'food court', 'takeout', 'delivery', 'uber eats', 'doordash', 'grubhub', 'postmates',
+                    'restaurante', 'pizzeria', 'fast food', 'kfc', 'mcdonald', 'subway', 'starbucks'],
+        'color': '#10b981',
+        'icon': 'restaurant'
+    },
+    'groceries': {
+        'keywords': ['supermarket', 'grocery', 'market', 'walmart', 'kroger', 'whole foods', 'trader joe',
+                    'safeway', 'costco', 'aldi', 'lidl', 'carrefour', 'tesco', 'fresh', 'produce',
+                    'kaufland', 'mega image', 'penny'],
+        'color': '#22c55e',
+        'icon': 'shopping_cart'
+    },
+    'coffee': {
+        'keywords': ['coffee', 'starbucks', 'cafe', 'caffè', 'espresso', 'latte', 'cappuccino'],
+        'color': '#92400e',
+        'icon': 'coffee'
+    },
+    
+    # Transportation
+    'gas': {
+        'keywords': ['gas station', 'fuel', 'petrol', 'shell', 'bp', 'exxon', 'chevron', 'mobil', 
+                    'texaco', 'station', 'benzinarie', 'combustibil', 'petrom', 'omv', 'lukoil'],
+        'color': '#ef4444',
+        'icon': 'local_gas_station'
+    },
+    'parking': {
+        'keywords': ['parking', 'garage', 'parcare', 'parcomat'],
+        'color': '#f97316',
+        'icon': 'local_parking'
+    },
+    'transport': {
+        'keywords': ['uber', 'lyft', 'taxi', 'cab', 'bus', 'metro', 'train', 'subway', 'transit',
+                    'bolt', 'autobuz', 'metrou', 'tren', 'ratb'],
+        'color': '#3b82f6',
+        'icon': 'directions_car'
+    },
+    
+    # Shopping
+    'online-shopping': {
+        'keywords': ['amazon', 'ebay', 'aliexpress', 'online', 'emag', 'altex', 'flanco'],
+        'color': '#a855f7',
+        'icon': 'shopping_bag'
+    },
+    'clothing': {
+        'keywords': ['clothing', 'fashion', 'apparel', 'zara', 'h&m', 'nike', 'adidas', 
+                    'levi', 'gap', 'imbracaminte', 'haine'],
+        'color': '#ec4899',
+        'icon': 'checkroom'
+    },
+    'electronics': {
+        'keywords': ['electronics', 'apple', 'samsung', 'sony', 'best buy', 'media markt'],
+        'color': '#6366f1',
+        'icon': 'devices'
+    },
+    
+    # Entertainment
+    'entertainment': {
+        'keywords': ['movie', 'cinema', 'theater', 'concert', 'show', 'ticket', 'netflix', 'spotify',
+                    'hbo', 'disney', 'entertainment', 'cinema city', 'teatru', 'concert'],
+        'color': '#8b5cf6',
+        'icon': 'movie'
+    },
+    'gym': {
+        'keywords': ['gym', 'fitness', 'workout', 'sport', 'sala', 'world class'],
+        'color': '#14b8a6',
+        'icon': 'fitness_center'
+    },
+    
+    # Bills & Utilities
+    'electricity': {
+        'keywords': ['electric', 'power', 'energie', 'enel', 'electrica'],
+        'color': '#fbbf24',
+        'icon': 'bolt'
+    },
+    'water': {
+        'keywords': ['water', 'apa', 'aqua'],
+        'color': '#06b6d4',
+        'icon': 'water_drop'
+    },
+    'internet': {
+        'keywords': ['internet', 'broadband', 'wifi', 'fiber', 'digi', 'upc', 'telekom', 'orange', 'vodafone'],
+        'color': '#3b82f6',
+        'icon': 'wifi'
+    },
+    'phone': {
+        'keywords': ['phone', 'mobile', 'cellular', 'telefon', 'abonament'],
+        'color': '#8b5cf6',
+        'icon': 'phone_iphone'
+    },
+    'subscription': {
+        'keywords': ['subscription', 'abonament', 'monthly', 'recurring'],
+        'color': '#f59e0b',
+        'icon': 'repeat'
+    },
+    
+    # Healthcare
+    'pharmacy': {
+        'keywords': ['pharmacy', 'farmacie', 'drug', 'cvs', 'walgreens', 'catena', 'help net', 'sensiblu'],
+        'color': '#ef4444',
+        'icon': 'local_pharmacy'
+    },
+    'medical': {
+        'keywords': ['doctor', 'hospital', 'clinic', 'medical', 'health', 'dental', 'spital', 'clinica'],
+        'color': '#dc2626',
+        'icon': 'medical_services'
+    },
+    
+    # Other
+    'insurance': {
+        'keywords': ['insurance', 'asigurare', 'policy'],
+        'color': '#64748b',
+        'icon': 'shield'
+    },
+    'education': {
+        'keywords': ['school', 'university', 'course', 'tuition', 'book', 'educatie', 'scoala', 'universitate'],
+        'color': '#06b6d4',
+        'icon': 'school'
+    },
+    'pet': {
+        'keywords': ['pet', 'vet', 'veterinar', 'animal'],
+        'color': '#f97316',
+        'icon': 'pets'
+    },
+}
+
+
+def extract_tags_from_text(text: str, max_tags: int = 5) -> List[Dict[str, str]]:
+    """
+    Extract relevant tags from OCR text or description
+    
+    Args:
+        text: The text to analyze (OCR text or expense description)
+        max_tags: Maximum number of tags to return
+        
+    Returns:
+        List of tag dictionaries with name, color, and icon
+    """
+    if not text:
+        return []
+    
+    # Normalize text: lowercase and remove special characters
+    normalized_text = text.lower()
+    normalized_text = re.sub(r'[^\w\s]', ' ', normalized_text)
+    
+    detected_tags = []
+    
+    # Check each pattern
+    for tag_name, pattern_info in TAG_PATTERNS.items():
+        for keyword in pattern_info['keywords']:
+            # Use word boundary matching for better accuracy
+            if re.search(r'\b' + re.escape(keyword.lower()) + r'\b', normalized_text):
+                detected_tags.append({
+                    'name': tag_name,
+                    'color': pattern_info['color'],
+                    'icon': pattern_info['icon']
+                })
+                break  # Don't add the same tag multiple times
+    
+    # Remove duplicates and limit to max_tags
+    unique_tags = []
+    seen = set()
+    for tag in detected_tags:
+        if tag['name'] not in seen:
+            seen.add(tag['name'])
+            unique_tags.append(tag)
+            if len(unique_tags) >= max_tags:
+                break
+    
+    return unique_tags
+
+
+def suggest_tags_for_expense(description: str, ocr_text: str = None, category_name: str = None) -> List[Dict[str, str]]:
+    """
+    Suggest tags for an expense based on description, OCR text, and category
+    
+    Args:
+        description: The expense description
+        ocr_text: OCR text from receipt (if available)
+        category_name: The category name (if available)
+        
+    Returns:
+        List of suggested tag dictionaries
+    """
+    all_text = description
+    
+    # Combine all available text
+    if ocr_text:
+        all_text += " " + ocr_text
+    if category_name:
+        all_text += " " + category_name
+    
+    return extract_tags_from_text(all_text, max_tags=3)
+
+
+def get_tag_suggestions() -> Dict[str, List[str]]:
+    """
+    Get all available tag patterns for UI display
+    
+    Returns:
+        Dictionary of tag names to their keywords
+    """
+    suggestions = {}
+    for tag_name, pattern_info in TAG_PATTERNS.items():
+        suggestions[tag_name] = {
+            'keywords': pattern_info['keywords'][:5],  # Show first 5 keywords
+            'color': pattern_info['color'],
+            'icon': pattern_info['icon']
+        }
+    return suggestions