Marius Mutu
4f83b8e73c
Major Changes: - Migrated from prototype to production architecture - Implemented modular Flask app with models/services/web layers - Added Docker containerization with docker-compose - Switched to Pipenv for dependency management - Built advanced parser extracting 63 real activities from INDEX_MASTER - Implemented SQLite FTS5 full-text search - Created minimalist, responsive web interface - Added comprehensive documentation and deployment guides Technical Improvements: - Clean separation of concerns (models, services, web) - Enhanced database schema with FTS5 indexing - Dynamic filters populated from real data - Production-ready configuration management - Security best practices implementation - Health monitoring and API endpoints Removed Legacy Files: - Old src/ directory structure - Static requirements.txt (replaced by Pipfile) - Test and debug files - Temporary cache files Current Status: - 63 activities indexed across 8 categories - Full-text search operational - Docker deployment ready - Production documentation complete 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
319 lines
13 KiB
Python
319 lines
13 KiB
Python
"""
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Search service for INDEX-SISTEM-JOCURI v2.0
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Enhanced search with FTS5 and intelligent filtering
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"""
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from typing import List, Dict, Any, Optional
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from app.models.database import DatabaseManager
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import re
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class SearchService:
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"""Enhanced search service with intelligent query processing"""
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def __init__(self, db_manager: DatabaseManager):
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"""Initialize search service with database manager"""
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self.db = db_manager
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def search_activities(self,
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search_text: Optional[str] = None,
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filters: Optional[Dict[str, str]] = None,
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limit: int = 100) -> List[Dict[str, Any]]:
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"""
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Enhanced search with intelligent filter mapping and query processing
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"""
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if filters is None:
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filters = {}
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# Process and normalize search text
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processed_search = self._process_search_text(search_text)
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# Map web filters to database fields
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db_filters = self._map_filters_to_db_fields(filters)
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# Perform database search
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results = self.db.search_activities(
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search_text=processed_search,
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**db_filters,
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limit=limit
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)
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# Post-process results for relevance and ranking
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return self._post_process_results(results, processed_search, filters)
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def _process_search_text(self, search_text: Optional[str]) -> Optional[str]:
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"""Process and enhance search text for better FTS5 results"""
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if not search_text or not search_text.strip():
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return None
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# Clean the search text
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cleaned = search_text.strip()
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# Handle Romanian diacritics and common variations
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replacements = {
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'ă': 'a', 'â': 'a', 'î': 'i', 'ș': 's', 'ț': 't',
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'Ă': 'A', 'Â': 'A', 'Î': 'I', 'Ș': 'S', 'Ț': 'T'
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}
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# Create both original and normalized versions for search
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normalized = cleaned
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for old, new in replacements.items():
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normalized = normalized.replace(old, new)
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# If different, search for both versions
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if normalized != cleaned and len(cleaned.split()) == 1:
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return f'"{cleaned}" OR "{normalized}"'
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# For multi-word queries, use phrase search with fallback
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if len(cleaned.split()) > 1:
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# Try exact phrase first, then individual words
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words = cleaned.split()
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individual_terms = ' OR '.join(f'"{word}"' for word in words)
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return f'"{cleaned}" OR ({individual_terms})'
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return f'"{cleaned}"'
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def _map_filters_to_db_fields(self, filters: Dict[str, str]) -> Dict[str, Any]:
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"""Map web interface filters to database query parameters"""
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db_filters = {}
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for filter_key, filter_value in filters.items():
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if not filter_value or not filter_value.strip():
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continue
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# Map filter types to database fields
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if filter_key == 'category':
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db_filters['category'] = filter_value
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elif filter_key == 'age_group':
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# Parse age range (e.g., "5-8 ani", "12+ ani")
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age_match = re.search(r'(\d+)(?:-(\d+))?\s*ani?', filter_value)
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if age_match:
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min_age = int(age_match.group(1))
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max_age = int(age_match.group(2)) if age_match.group(2) else None
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if max_age:
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# Range like "5-8 ani"
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db_filters['age_group_min'] = min_age
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db_filters['age_group_max'] = max_age
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else:
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# Open range like "12+ ani"
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db_filters['age_group_min'] = min_age
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elif filter_key == 'participants':
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# Parse participant range (e.g., "5-10 persoane", "30+ persoane")
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part_match = re.search(r'(\d+)(?:-(\d+))?\s*persoan[eă]?', filter_value)
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if part_match:
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min_part = int(part_match.group(1))
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max_part = int(part_match.group(2)) if part_match.group(2) else None
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if max_part:
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db_filters['participants_min'] = min_part
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db_filters['participants_max'] = max_part
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else:
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db_filters['participants_min'] = min_part
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elif filter_key == 'duration':
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# Parse duration (e.g., "15-30 minute", "60+ minute")
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dur_match = re.search(r'(\d+)(?:-(\d+))?\s*minut[eă]?', filter_value)
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if dur_match:
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min_dur = int(dur_match.group(1))
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max_dur = int(dur_match.group(2)) if dur_match.group(2) else None
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if max_dur:
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db_filters['duration_min'] = min_dur
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db_filters['duration_max'] = max_dur
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else:
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db_filters['duration_min'] = min_dur
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elif filter_key == 'materials':
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db_filters['materials_category'] = filter_value
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elif filter_key == 'difficulty':
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db_filters['difficulty_level'] = filter_value
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# Handle any other custom filters
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else:
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# Generic filter handling - try to match against keywords or tags
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if 'keywords' not in db_filters:
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db_filters['keywords'] = []
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db_filters['keywords'].append(filter_value)
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return db_filters
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def _post_process_results(self,
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results: List[Dict[str, Any]],
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search_text: Optional[str],
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filters: Dict[str, str]) -> List[Dict[str, Any]]:
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"""Post-process results for better ranking and relevance"""
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if not results:
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return results
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# If we have search text, boost results based on relevance
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if search_text:
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results = self._boost_search_relevance(results, search_text)
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# Apply secondary ranking based on filters
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if filters:
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results = self._apply_filter_boost(results, filters)
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# Ensure variety in categories if no specific category filter
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if 'category' not in filters:
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results = self._ensure_category_variety(results)
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return results
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def _boost_search_relevance(self,
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results: List[Dict[str, Any]],
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search_text: str) -> List[Dict[str, Any]]:
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"""Boost results based on search text relevance"""
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search_terms = search_text.lower().replace('"', '').split()
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for result in results:
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boost_score = 0
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# Check name matches (highest priority)
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name_lower = result.get('name', '').lower()
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for term in search_terms:
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if term in name_lower:
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boost_score += 10
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if name_lower.startswith(term):
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boost_score += 5 # Extra boost for name starts with term
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# Check description matches
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desc_lower = result.get('description', '').lower()
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for term in search_terms:
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if term in desc_lower:
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boost_score += 3
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# Check keywords matches
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keywords_lower = result.get('keywords', '').lower()
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for term in search_terms:
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if term in keywords_lower:
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boost_score += 5
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# Store boost score for sorting
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result['_boost_score'] = boost_score
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# Sort by boost score, then by existing search rank
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results.sort(key=lambda x: (
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x.get('_boost_score', 0),
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x.get('search_rank', 0),
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x.get('popularity_score', 0)
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), reverse=True)
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# Remove boost score from final results
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for result in results:
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result.pop('_boost_score', None)
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return results
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def _apply_filter_boost(self,
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results: List[Dict[str, Any]],
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filters: Dict[str, str]) -> List[Dict[str, Any]]:
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"""Apply additional ranking based on filter preferences"""
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# If user filtered by materials, boost activities with detailed material lists
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if 'materials' in filters:
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for result in results:
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if result.get('materials_list') and len(result['materials_list']) > 50:
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result['popularity_score'] = result.get('popularity_score', 0) + 1
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# If user filtered by age, boost activities with specific age ranges
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if 'age_group' in filters:
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for result in results:
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if result.get('age_group_min') and result.get('age_group_max'):
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result['popularity_score'] = result.get('popularity_score', 0) + 1
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return results
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def _ensure_category_variety(self, results: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""Ensure variety in categories when no specific category is filtered"""
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if len(results) <= 10:
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return results
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# Group results by category
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category_groups = {}
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for result in results:
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category = result.get('category', 'Unknown')
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if category not in category_groups:
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category_groups[category] = []
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category_groups[category].append(result)
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# If we have multiple categories, ensure balanced representation
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if len(category_groups) > 1:
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balanced_results = []
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max_per_category = max(3, len(results) // len(category_groups))
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# Take up to max_per_category from each category
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for category, category_results in category_groups.items():
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balanced_results.extend(category_results[:max_per_category])
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# Add remaining results to reach original count
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remaining_slots = len(results) - len(balanced_results)
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if remaining_slots > 0:
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remaining_results = []
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for category_results in category_groups.values():
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remaining_results.extend(category_results[max_per_category:])
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# Sort remaining by relevance and add top ones
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remaining_results.sort(key=lambda x: (
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x.get('search_rank', 0),
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x.get('popularity_score', 0)
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), reverse=True)
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balanced_results.extend(remaining_results[:remaining_slots])
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return balanced_results
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return results
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def get_search_suggestions(self, partial_query: str, limit: int = 5) -> List[str]:
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"""Get search suggestions based on partial query"""
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if not partial_query or len(partial_query) < 2:
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return []
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try:
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# Search for activities that match the partial query
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results = self.db.search_activities(
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search_text=f'"{partial_query}"',
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limit=limit * 2
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)
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suggestions = []
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seen = set()
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for result in results:
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# Extract potential suggestions from name and keywords
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name = result.get('name', '')
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keywords = result.get('keywords', '')
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# Add name if it contains the partial query
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if partial_query.lower() in name.lower() and name not in seen:
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suggestions.append(name)
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seen.add(name)
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# Add individual keywords that start with partial query
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if keywords:
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for keyword in keywords.split(','):
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keyword = keyword.strip()
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if (keyword.lower().startswith(partial_query.lower()) and
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len(keyword) > len(partial_query) and
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keyword not in seen):
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suggestions.append(keyword)
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seen.add(keyword)
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if len(suggestions) >= limit:
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break
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return suggestions[:limit]
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except Exception as e:
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print(f"Error getting search suggestions: {e}")
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return [] |