""" Advanced UX Service This service provides enhanced user experience features including: - AI-powered search suggestions and recommendations - Enhanced search interface with smart filters - Download integration throughout the UI - Contextual suggestions based on user behavior - Personalized content discovery """ import datetime import logging import re from collections import defaultdict from dataclasses import dataclass from enum import Enum from typing import Any from sqlalchemy import or_, select from sqlalchemy.orm import Session from swingmusic.db import db from swingmusic.models.artist import Artist from swingmusic.models.track import Track from swingmusic.services.music_catalog import music_catalog_service from swingmusic.utils.ai_recommender import AIRecommender from swingmusic.utils.behavior_tracker import BehaviorTracker logger = logging.getLogger(__name__) class SuggestionType(Enum): """Types of suggestions""" SEARCH_QUERY = "search_query" TRACK = "track" ARTIST = "artist" ALBUM = "album" PLAYLIST = "playlist" GENRE = "genre" MOOD = "mood" ACTIVITY = "activity" class SearchContext(Enum): """Search context for suggestions""" GENERAL = "general" DISCOVERY = "discovery" DOWNLOAD = "download" PLAYLIST = "playlist" OFFLINE = "offline" SOCIAL = "social" @dataclass class SearchSuggestion: """Search suggestion with metadata""" id: str type: SuggestionType title: str subtitle: str | None image_url: str | None url: str | None metadata: dict[str, Any] relevance_score: float context: SearchContext created_at: datetime.datetime @dataclass class UserBehavior: """User behavior patterns for personalization""" user_id: int favorite_genres: list[str] favorite_artists: list[str] listening_patterns: dict[str, Any] search_history: list[dict[str, Any]] download_preferences: dict[str, Any] interaction_patterns: dict[str, Any] last_updated: datetime.datetime @dataclass class SearchFilter: """Enhanced search filter""" filter_id: str name: str type: str # genre, mood, year, quality, duration, etc. options: list[dict[str, Any]] is_active: bool is_multi_select: bool class AdvancedUXService: """Service for enhanced user experience features""" def __init__(self): self.ai_recommender = AIRecommender() self.behavior_tracker = BehaviorTracker() self.search_history = defaultdict(list) self.suggestion_cache = {} self.user_behaviors = {} # Search configuration self.max_suggestions = 10 self.search_history_limit = 100 self.behavior_update_interval = 3600 # 1 hour async def get_search_suggestions( self, user_id: int, query: str, context: SearchContext = SearchContext.GENERAL, limit: int = 10, ) -> list[SearchSuggestion]: """ Get intelligent search suggestions based on query and context Args: user_id: User ID query: Search query context: Search context limit: Maximum suggestions to return Returns: List of search suggestions """ try: # Clean and normalize query clean_query = self._clean_search_query(query) if len(clean_query) < 2: return await self._get_default_suggestions(user_id, context, limit) # Generate suggestions from multiple sources suggestions = [] # 1. Track suggestions from local library local_tracks = await self._get_local_track_suggestions( user_id, clean_query, limit // 3 ) suggestions.extend(local_tracks) # 2. Artist suggestions artists = await self._get_artist_suggestions( user_id, clean_query, limit // 4 ) suggestions.extend(artists) # 3. Album suggestions albums = await self._get_album_suggestions(user_id, clean_query, limit // 4) suggestions.extend(albums) # 4. Global catalog suggestions if context in [SearchContext.DISCOVERY, SearchContext.DOWNLOAD]: global_suggestions = await self._get_global_suggestions( user_id, clean_query, limit // 3 ) suggestions.extend(global_suggestions) # 5. Behavior-based suggestions behavior_suggestions = await self._get_behavior_suggestions( user_id, clean_query, context, limit // 4 ) suggestions.extend(behavior_suggestions) # Sort by relevance and limit suggestions.sort(key=lambda x: x.relevance_score, reverse=True) suggestions = suggestions[:limit] # Log search for learning await self._log_search_query(user_id, query, context, suggestions) return suggestions except Exception as e: logger.error(f"Error getting search suggestions: {e}") return [] async def get_discovery_recommendations( self, user_id: int, recommendation_type: str = "mixed", limit: int = 20 ) -> list[SearchSuggestion]: """ Get personalized discovery recommendations Args: user_id: User ID recommendation_type: Type of recommendations (tracks, artists, albums, mixed) limit: Maximum recommendations to return Returns: List of discovery recommendations """ try: # Get user behavior data behavior = await self._get_user_behavior(user_id) recommendations = [] # Generate recommendations based on type if recommendation_type in ["tracks", "mixed"]: track_recs = await self._get_track_recommendations( user_id, behavior, limit // 2 ) recommendations.extend(track_recs) if recommendation_type in ["artists", "mixed"]: artist_recs = await self._get_artist_recommendations( user_id, behavior, limit // 2 ) recommendations.extend(artist_recs) if recommendation_type in ["albums", "mixed"]: album_recs = await self._get_album_recommendations( user_id, behavior, limit // 2 ) recommendations.extend(album_recs) # Sort by relevance and limit recommendations.sort(key=lambda x: x.relevance_score, reverse=True) recommendations = recommendations[:limit] return recommendations except Exception as e: logger.error(f"Error getting discovery recommendations: {e}") return [] async def get_contextual_suggestions( self, user_id: int, current_track_id: str, context_type: str ) -> list[SearchSuggestion]: """ Get contextual suggestions based on current track Args: user_id: User ID current_track_id: Currently playing track ID context_type: Type of context (similar, same_artist, same_genre, etc.) Returns: List of contextual suggestions """ try: suggestions = [] # Get current track information current_track = await self._get_track_info(current_track_id) if not current_track: return [] # Generate suggestions based on context type if context_type == "similar": similar_suggestions = await self._get_similar_track_suggestions( user_id, current_track, 10 ) suggestions.extend(similar_suggestions) elif context_type == "same_artist": artist_suggestions = await self._get_same_artist_suggestions( user_id, current_track["artist"], 10 ) suggestions.extend(artist_suggestions) elif context_type == "same_genre": genre_suggestions = await self._get_same_genre_suggestions( user_id, current_track, 10 ) suggestions.extend(genre_suggestions) elif context_type == "popular": popular_suggestions = await self._get_popular_suggestions(user_id, 10) suggestions.extend(popular_suggestions) return suggestions[:10] except Exception as e: logger.error(f"Error getting contextual suggestions: {e}") return [] async def get_download_suggestions( self, user_id: int, query: str = "", limit: int = 15 ) -> list[SearchSuggestion]: """ Get download-specific suggestions with universal downloader integration Args: user_id: User ID query: Search query limit: Maximum suggestions to return Returns: List of download suggestions """ try: suggestions = [] # If query is provided, search for matching content if query: # Search global catalog catalog_results = await music_catalog_service.search_global_catalog( query, "all", limit ) # Convert to suggestions for track in catalog_results.tracks[: limit // 2]: suggestion = SearchSuggestion( id=f"track_{track.spotify_id}", type=SuggestionType.TRACK, title=track.title, subtitle=f"{track.artist} • {track.album}", image_url=track.image_url, url=f"/download/{track.spotify_id}", metadata={ "spotify_id": track.spotify_id, "artist": track.artist, "album": track.album, "duration": track.duration_ms, "popularity": track.popularity, "preview_url": track.preview_url, }, relevance_score=self._calculate_download_relevance( track, user_id ), context=SearchContext.DOWNLOAD, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) # Add artist suggestions for artist in catalog_results.artists[: limit // 4]: suggestion = SearchSuggestion( id=f"artist_{artist.spotify_id}", type=SuggestionType.ARTIST, title=artist.title, subtitle=f"{artist.popularity} popularity", image_url=artist.image_url, url=f"/artist/{artist.spotify_id}", metadata={ "spotify_id": artist.spotify_id, "popularity": artist.popularity, "followers": artist.data.get("followers", 0), }, relevance_score=self._calculate_download_relevance( artist, user_id ), context=SearchContext.DOWNLOAD, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) # Add album suggestions for album in catalog_results.albums[: limit // 4]: suggestion = SearchSuggestion( id=f"album_{album.spotify_id}", type=SuggestionType.ALBUM, title=album.title, subtitle=f"{album.artist} • {album.data.get('total_tracks', 0)} tracks", image_url=album.image_url, url=f"/album/{album.spotify_id}", metadata={ "spotify_id": album.spotify_id, "artist": album.artist, "total_tracks": album.data.get("total_tracks", 0), "release_date": album.release_date, "album_type": album.data.get("album_type", "album"), }, relevance_score=self._calculate_download_relevance( album, user_id ), context=SearchContext.DOWNLOAD, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) # Add trending/popular suggestions if no query if not query: trending_suggestions = await self._get_trending_download_suggestions( user_id, limit ) suggestions.extend(trending_suggestions) # Sort by relevance and limit suggestions.sort(key=lambda x: x.relevance_score, reverse=True) suggestions = suggestions[:limit] return suggestions except Exception as e: logger.error(f"Error getting download suggestions: {e}") return [] async def get_enhanced_search_filters(self, user_id: int) -> list[SearchFilter]: """ Get enhanced search filters with user personalization Args: user_id: User ID Returns: List of enhanced search filters """ try: filters = [] # Get user behavior for personalization behavior = await self._get_user_behavior(user_id) # Genre filter genre_options = [] popular_genres = await self._get_popular_genres(user_id) for genre in popular_genres: genre_options.append( { "value": genre, "label": genre.title(), "count": await self._get_genre_track_count(genre), "is_favorite": genre in behavior.favorite_genres, } ) filters.append( SearchFilter( filter_id="genre", name="Genre", type="genre", options=genre_options, is_active=False, is_multi_select=True, ) ) # Mood filter mood_options = [ {"value": "energetic", "label": "Energetic", "icon": "zap"}, {"value": "relaxed", "label": "Relaxed", "icon": "leaf"}, {"value": "happy", "label": "Happy", "icon": "smile"}, {"value": "sad", "label": "Sad", "icon": "frown"}, {"value": "focused", "label": "Focused", "icon": "brain"}, {"value": "workout", "label": "Workout", "icon": "dumbbell"}, ] filters.append( SearchFilter( filter_id="mood", name="Mood", type="mood", options=mood_options, is_active=False, is_multi_select=False, ) ) # Year filter current_year = datetime.datetime.now().year year_options = [] for year_offset in range(0, 10): year = current_year - year_offset year_options.append( { "value": str(year), "label": str(year), "count": await self._get_year_track_count(year), } ) filters.append( SearchFilter( filter_id="year", name="Year", type="year", options=year_options, is_active=False, is_multi_select=True, ) ) # Quality filter quality_options = [ {"value": "lossless", "label": "Lossless (FLAC)", "icon": "gem"}, {"value": "high", "label": "High (320kbps)", "icon": "star"}, {"value": "medium", "label": "Medium (256kbps)", "icon": "music"}, {"value": "low", "label": "Low (128kbps)", "icon": "headphones"}, ] filters.append( SearchFilter( filter_id="quality", name="Audio Quality", type="quality", options=quality_options, is_active=False, is_multi_select=False, ) ) # Duration filter duration_options = [ {"value": "short", "label": "Short (< 2 min)", "max_seconds": 120}, { "value": "medium", "label": "Medium (2-4 min)", "min_seconds": 120, "max_seconds": 240, }, {"value": "long", "label": "Long (> 4 min)", "min_seconds": 240}, ] filters.append( SearchFilter( filter_id="duration", name="Duration", type="duration", options=duration_options, is_active=False, is_multi_select=False, ) ) return filters except Exception as e: logger.error(f"Error getting enhanced search filters: {e}") return [] async def update_user_behavior( self, user_id: int, interaction_data: dict[str, Any] ): """ Update user behavior based on interactions Args: user_id: User ID interaction_data: Interaction data """ try: # Get existing behavior behavior = await self._get_user_behavior(user_id) # Update based on interaction type interaction_type = interaction_data.get("type") if interaction_type == "search": await self._update_search_behavior(behavior, interaction_data) elif interaction_type == "play": await self._update_play_behavior(behavior, interaction_data) elif interaction_type == "download": await self._update_download_behavior(behavior, interaction_data) elif interaction_type == "like": await self._update_like_behavior(behavior, interaction_data) # Save updated behavior behavior.last_updated = datetime.datetime.utcnow() self.user_behaviors[user_id] = behavior # Periodically save to database if ( datetime.datetime.utcnow().timestamp() % self.behavior_update_interval < 60 ): await self._save_user_behavior(behavior) except Exception as e: logger.error(f"Error updating user behavior: {e}") # Private helper methods def _clean_search_query(self, query: str) -> str: """Clean and normalize search query""" if not query: return "" # Remove special characters and normalize whitespace clean_query = re.sub(r"[^\w\s]", "", query.lower()) clean_query = re.sub(r"\s+", " ", clean_query).strip() return clean_query async def _get_default_suggestions( self, user_id: int, context: SearchContext, limit: int ) -> list[SearchSuggestion]: """Get default suggestions when query is too short""" suggestions = [] try: behavior = await self._get_user_behavior(user_id) # Add favorite artists for artist in behavior.favorite_artists[: limit // 3]: suggestion = SearchSuggestion( id=f"artist_{artist}", type=SuggestionType.ARTIST, title=artist, subtitle="Favorite Artist", image_url=None, url=f"/search?q={artist}", metadata={"source": "favorites"}, relevance_score=0.8, context=context, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) # Add trending content if context == SearchContext.DISCOVERY: trending = await self._get_trending_suggestions(user_id, limit // 3) suggestions.extend(trending) # Add popular genres for genre in behavior.favorite_genres[: limit // 3]: suggestion = SearchSuggestion( id=f"genre_{genre}", type=SuggestionType.GENRE, title=genre.title(), subtitle="Popular Genre", image_url=None, url=f"/search?genre={genre}", metadata={"source": "favorites"}, relevance_score=0.7, context=context, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) except Exception as e: logger.error(f"Error getting default suggestions: {e}") return suggestions[:limit] async def _get_local_track_suggestions( self, user_id: int, query: str, limit: int ) -> list[SearchSuggestion]: """Get track suggestions from local library""" suggestions = [] try: with Session(db.engine) as session: # Search tracks in local library search_pattern = f"%{query}%" tracks_query = ( select(Track) .where( or_( Track.title.ilike(search_pattern), Track.artist.ilike(search_pattern), Track.album.ilike(search_pattern), ) ) .limit(limit) ) tracks = session.execute(tracks_query).scalars().all() for track in tracks: suggestion = SearchSuggestion( id=f"track_{track.id}", type=SuggestionType.TRACK, title=track.title, subtitle=f"{track.artist} • {track.album}", image_url=track.image, url=f"/track/{track.id}", metadata={ "track_id": track.id, "artist": track.artist, "album": track.album, "duration": track.duration, "play_count": track.playcount, }, relevance_score=self._calculate_local_relevance(track, query), context=SearchContext.GENERAL, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) except Exception as e: logger.error(f"Error getting local track suggestions: {e}") return suggestions async def _get_artist_suggestions( self, user_id: int, query: str, limit: int ) -> list[SearchSuggestion]: """Get artist suggestions""" suggestions = [] try: with Session(db.engine) as session: # Search artists search_pattern = f"%{query}%" artists_query = ( select(Artist).where(Artist.name.ilike(search_pattern)).limit(limit) ) artists = session.execute(artists_query).scalars().all() for artist in artists: suggestion = SearchSuggestion( id=f"artist_{artist.id}", type=SuggestionType.ARTIST, title=artist.name, subtitle=f"{artist.trackcount} tracks", image_url=artist.image, url=f"/artist/{artist.id}", metadata={ "artist_id": artist.id, "track_count": artist.trackcount, "album_count": artist.albumcount, }, relevance_score=self._calculate_artist_relevance(artist, query), context=SearchContext.GENERAL, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) except Exception as e: logger.error(f"Error getting artist suggestions: {e}") return suggestions async def _get_album_suggestions( self, user_id: int, query: str, limit: int ) -> list[SearchSuggestion]: """Get album suggestions""" suggestions = [] try: # This would search albums in local library # For now, return empty list as placeholder pass except Exception as e: logger.error(f"Error getting album suggestions: {e}") return suggestions async def _get_global_suggestions( self, user_id: int, query: str, limit: int ) -> list[SearchSuggestion]: """Get suggestions from global catalog""" suggestions = [] try: # Search global catalog catalog_results = await music_catalog_service.search_global_catalog( query, "all", limit ) # Convert tracks to suggestions for track in catalog_results.tracks[: limit // 2]: suggestion = SearchSuggestion( id=f"global_track_{track.spotify_id}", type=SuggestionType.TRACK, title=track.title, subtitle=f"{track.artist} • {track.album}", image_url=track.image_url, url=f"/catalog/track/{track.spotify_id}", metadata={ "spotify_id": track.spotify_id, "source": "global_catalog", "popularity": track.popularity, }, relevance_score=self._calculate_global_relevance( track, query, user_id ), context=SearchContext.DISCOVERY, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) # Convert artists to suggestions for artist in catalog_results.artists[: limit // 2]: suggestion = SearchSuggestion( id=f"global_artist_{artist.spotify_id}", type=SuggestionType.ARTIST, title=artist.title, subtitle="Discover on Spotify", image_url=artist.image_url, url=f"/catalog/artist/{artist.spotify_id}", metadata={ "spotify_id": artist.spotify_id, "source": "global_catalog", "popularity": artist.popularity, }, relevance_score=self._calculate_global_relevance( artist, query, user_id ), context=SearchContext.DISCOVERY, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) except Exception as e: logger.error(f"Error getting global suggestions: {e}") return suggestions async def _get_behavior_suggestions( self, user_id: int, query: str, context: SearchContext, limit: int ) -> list[SearchSuggestion]: """Get behavior-based suggestions""" suggestions = [] try: behavior = await self._get_user_behavior(user_id) # Suggest based on favorite genres for genre in behavior.favorite_genres[: limit // 3]: if query.lower() in genre.lower(): suggestion = SearchSuggestion( id=f"genre_suggestion_{genre}", type=SuggestionType.GENRE, title=genre.title(), subtitle="Based on your preferences", image_url=None, url=f"/search?genre={genre}", metadata={"source": "behavior"}, relevance_score=0.9, context=context, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) # Suggest based on listening patterns if "search" in behavior.listening_patterns: recent_searches = behavior.listening_patterns["search"][-5:] for recent_search in recent_searches: if query.lower() in recent_search.get("query", "").lower(): suggestion = SearchSuggestion( id=f"recent_search_{recent_search.get('query', '')}", type=SuggestionType.SEARCH_QUERY, title=recent_search.get("query", ""), subtitle="Recent search", image_url=None, url=f"/search?q={recent_search.get('query', '')}", metadata={"source": "recent_searches"}, relevance_score=0.8, context=context, created_at=datetime.datetime.utcnow(), ) suggestions.append(suggestion) except Exception as e: logger.error(f"Error getting behavior suggestions: {e}") return suggestions def _calculate_local_relevance(self, track: Track, query: str) -> float: """Calculate relevance score for local track""" score = 0.0 query_lower = query.lower() # Title match if track.title and query_lower in track.title.lower(): score += 0.8 # Artist match if track.artist and query_lower in track.artist.lower(): score += 0.6 # Album match if track.album and query_lower in track.album.lower(): score += 0.4 # Play count boost if track.playcount > 0: score += min(track.playcount / 100, 0.3) return min(score, 1.0) def _calculate_artist_relevance(self, artist: Artist, query: str) -> float: """Calculate relevance score for artist""" score = 0.0 query_lower = query.lower() # Name match if artist.name and query_lower in artist.name.lower(): score += 0.9 # Track count boost if artist.trackcount > 0: score += min(artist.trackcount / 50, 0.3) return min(score, 1.0) def _calculate_global_relevance(self, item: Any, query: str, user_id: int) -> float: """Calculate relevance score for global catalog item""" score = 0.0 query_lower = query.lower() # Title/name match if hasattr(item, "title") and item.title and query_lower in item.title.lower(): score += 0.7 # Artist match if ( hasattr(item, "artist") and item.artist and query_lower in item.artist.lower() ): score += 0.5 # Popularity boost if hasattr(item, "popularity") and item.popularity: score += min(item.popularity / 100, 0.3) return min(score, 1.0) def _calculate_download_relevance(self, item: Any, user_id: int) -> float: """Calculate relevance score for download suggestions""" score = 0.0 # Base relevance from popularity if hasattr(item, "popularity") and item.popularity: score += min(item.popularity / 100, 0.5) # User behavior boost if user_id in self.user_behaviors: behavior = self.user_behaviors[user_id] # Favorite artist boost if hasattr(item, "artist") and item.artist in behavior.favorite_artists: score += 0.3 # Favorite genre boost # This would require genre information from the item return min(score, 1.0) async def _get_user_behavior(self, user_id: int) -> UserBehavior: """Get or create user behavior data""" if user_id in self.user_behaviors: return self.user_behaviors[user_id] # Create default behavior behavior = UserBehavior( user_id=user_id, favorite_genres=[], favorite_artists=[], listening_patterns={}, search_history=[], download_preferences={}, interaction_patterns={}, last_updated=datetime.datetime.utcnow(), ) self.user_behaviors[user_id] = behavior return behavior async def _log_search_query( self, user_id: int, query: str, context: SearchContext, suggestions: list[SearchSuggestion], ): """Log search query for learning""" try: search_data = { "query": query, "context": context.value, "suggestion_count": len(suggestions), "timestamp": datetime.datetime.utcnow().isoformat(), "suggestions": [ { "id": s.id, "type": s.type.value, "title": s.title, "relevance_score": s.relevance_score, } for s in suggestions[:5] # Log top 5 suggestions ], } # Add to search history behavior = await self._get_user_behavior(user_id) behavior.search_history.append(search_data) # Limit history size if len(behavior.search_history) > self.search_history_limit: behavior.search_history = behavior.search_history[ -self.search_history_limit : ] except Exception as e: logger.error(f"Error logging search query: {e}") async def _get_track_info(self, track_id: str) -> dict[str, Any] | None: """Get track information""" try: with Session(db.engine) as session: track = session.get(Track, track_id) if not track: return None return { "id": track.id, "title": track.title, "artist": track.artist, "album": track.album, "duration": track.duration, "playcount": track.playcount, "image": track.image, } except Exception as e: logger.error(f"Error getting track info: {e}") return None async def _get_popular_genres(self, user_id: int) -> list[str]: """Get popular genres for user""" try: with Session(db.engine): # This would query genre data from tracks # For now, return common genres return [ "rock", "pop", "electronic", "jazz", "classical", "hip-hop", "country", "blues", ] except Exception as e: logger.error(f"Error getting popular genres: {e}") return [] async def _get_genre_track_count(self, genre: str) -> int: """Get track count for genre""" try: with Session(db.engine): # This would count tracks by genre # For now, return placeholder return 100 except Exception as e: logger.error(f"Error getting genre track count: {e}") return 0 async def _get_year_track_count(self, year: int) -> int: """Get track count for year""" try: with Session(db.engine): # This would count tracks by year # For now, return placeholder return 50 except Exception as e: logger.error(f"Error getting year track count: {e}") return 0 async def _get_trending_suggestions( self, user_id: int, limit: int ) -> list[SearchSuggestion]: """Get trending suggestions""" # This would implement trending logic return [] async def _get_trending_download_suggestions( self, user_id: int, limit: int ) -> list[SearchSuggestion]: """Get trending download suggestions""" # This would implement trending download logic return [] async def _get_track_recommendations( self, user_id: int, behavior: UserBehavior, limit: int ) -> list[SearchSuggestion]: """Get track recommendations based on behavior""" # This would use AI recommender return [] async def _get_artist_recommendations( self, user_id: int, behavior: UserBehavior, limit: int ) -> list[SearchSuggestion]: """Get artist recommendations based on behavior""" # This would use AI recommender return [] async def _get_album_recommendations( self, user_id: int, behavior: UserBehavior, limit: int ) -> list[SearchSuggestion]: """Get album recommendations based on behavior""" # This would use AI recommender return [] async def _get_similar_track_suggestions( self, user_id: int, current_track: dict[str, Any], limit: int ) -> list[SearchSuggestion]: """Get similar track suggestions""" # This would implement similarity logic return [] async def _get_same_artist_suggestions( self, user_id: int, artist_name: str, limit: int ) -> list[SearchSuggestion]: """Get suggestions from same artist""" # This would query tracks by artist return [] async def _get_same_genre_suggestions( self, user_id: int, current_track: dict[str, Any], limit: int ) -> list[SearchSuggestion]: """Get suggestions from same genre""" # This would query tracks by genre return [] async def _get_popular_suggestions( self, user_id: int, limit: int ) -> list[SearchSuggestion]: """Get popular suggestions""" # This would implement popularity logic return [] async def _update_search_behavior( self, behavior: UserBehavior, interaction_data: dict[str, Any] ): """Update search behavior""" # This would update search patterns pass async def _update_play_behavior( self, behavior: UserBehavior, interaction_data: dict[str, Any] ): """Update play behavior""" # This would update listening patterns pass async def _update_download_behavior( self, behavior: UserBehavior, interaction_data: dict[str, Any] ): """Update download behavior""" # This would update download preferences pass async def _update_like_behavior( self, behavior: UserBehavior, interaction_data: dict[str, Any] ): """Update like behavior""" # This would update favorites pass async def _save_user_behavior(self, behavior: UserBehavior): """Save user behavior to database""" # This would save to database pass # Global service instance advanced_ux_service = AdvancedUXService()