"""Auto-Import Worker — watches staging folder, identifies music, and processes automatically. Scans the staging folder for audio files and album folders, identifies them using tags/filenames/AcoustID, matches to metadata source tracklists, and processes high-confidence matches through the post-processing pipeline. Lower-confidence matches are queued for user review. Supports both album folders (directories containing audio files) and single loose audio files in the staging root. """ import hashlib import json import os import re import threading import time from dataclasses import dataclass, field from datetime import datetime from difflib import SequenceMatcher from typing import Any, Callable, Dict, List, Optional from utils.logging_config import get_logger logger = get_logger("auto_import") AUDIO_EXTENSIONS = {'.mp3', '.flac', '.ogg', '.opus', '.m4a', '.aac', '.wav', '.wma', '.aiff', '.aif', '.ape'} DISC_FOLDER_RE = re.compile(r'^(?:disc|cd|disk)\s*(\d+)$', re.IGNORECASE) @dataclass class FolderCandidate: path: str name: str audio_files: List[str] = field(default_factory=list) disc_structure: Dict[int, List[str]] = field(default_factory=dict) # disc_num -> files folder_hash: str = '' is_single: bool = False # True for loose files in staging root def _compute_folder_hash(audio_files: List[str]) -> str: """Deterministic hash of folder contents for change detection.""" items = [] for f in sorted(audio_files): try: items.append(f"{os.path.basename(f)}:{os.path.getsize(f)}") except OSError: items.append(os.path.basename(f)) return hashlib.md5('|'.join(items).encode()).hexdigest() def _read_file_tags(file_path: str) -> Dict[str, Any]: """Read embedded tags from an audio file. Returns dict with title, artist, album, track_number, disc_number, year.""" result = {'title': '', 'artist': '', 'album': '', 'track_number': 0, 'disc_number': 1, 'year': ''} try: from mutagen import File as MutagenFile audio = MutagenFile(file_path, easy=True) if audio and audio.tags: tags = audio.tags result['title'] = (tags.get('title', [''])[0] or '').strip() result['artist'] = (tags.get('artist', [''])[0] or tags.get('albumartist', [''])[0] or '').strip() result['album'] = (tags.get('album', [''])[0] or '').strip() # Date/year — try 'date' first, fall back to 'year' date_str = (tags.get('date', [''])[0] or tags.get('year', [''])[0] or '').strip() if date_str and len(date_str) >= 4: result['year'] = date_str[:4] tn = tags.get('tracknumber', ['0'])[0] try: result['track_number'] = int(str(tn).split('/')[0]) except (ValueError, TypeError): pass dn = tags.get('discnumber', ['1'])[0] try: result['disc_number'] = int(str(dn).split('/')[0]) except (ValueError, TypeError): pass except Exception as e: logger.debug(f"Could not read tags from {os.path.basename(file_path)}: {e}") return result def _parse_folder_name(folder_name: str): """Try to extract artist and album from folder name. Returns (artist, album) or (None, folder_name).""" # Pattern: "Artist - Album" if ' - ' in folder_name: parts = folder_name.split(' - ', 1) return parts[0].strip(), parts[1].strip() # Pattern: just the folder name as album return None, folder_name.strip() def _normalize(text: str) -> str: if not text: return '' t = text.lower().strip() t = re.sub(r'\(.*?\)', '', t) t = re.sub(r'\[.*?\]', '', t) t = re.sub(r'[^\w\s]', '', t) return ' '.join(t.split()) def _similarity(a: str, b: str) -> float: if not a or not b: return 0.0 return SequenceMatcher(None, _normalize(a), _normalize(b)).ratio() def _quality_rank(ext: str) -> int: """Higher = better quality.""" ranks = {'.flac': 10, '.wav': 9, '.aiff': 9, '.aif': 9, '.ape': 8, '.m4a': 7, '.ogg': 6, '.opus': 6, '.mp3': 5, '.wma': 3, '.aac': 5} return ranks.get(ext.lower(), 1) class AutoImportWorker: """Background worker that watches the staging folder and auto-imports music.""" def __init__(self, database, staging_path: str = './Staging', transfer_path: str = './Transfer', process_callback: Optional[Callable] = None, config_manager: Any = None, automation_engine: Any = None): self.database = database self.staging_path = staging_path self.transfer_path = transfer_path self._process_callback = process_callback self._config_manager = config_manager self._automation_engine = automation_engine self.running = False self.paused = False self.should_stop = False self._thread = None self._stop_event = threading.Event() # State self._folder_snapshots: Dict[str, float] = {} # path -> mtime_sum self._processing_paths: set = set() # Paths currently being processed (skip on rescan) self._current_folder = '' self._current_status = 'idle' self._stats = {'scanned': 0, 'auto_processed': 0, 'pending_review': 0, 'failed': 0} self._last_scan_time = None def start(self): if self.running: return self.should_stop = False self._stop_event.clear() self.running = True self._thread = threading.Thread(target=self._run, daemon=True, name='AutoImportWorker') self._thread.start() logger.info("Auto-import worker started") def stop(self): self.should_stop = True self._stop_event.set() self.running = False if self._thread and self._thread.is_alive(): self._thread.join(timeout=5) logger.info("Auto-import worker stopped") def pause(self): self.paused = True logger.info("Auto-import worker paused") def resume(self): self.paused = False logger.info("Auto-import worker resumed") def get_status(self) -> dict: return { 'running': self.running, 'paused': self.paused, 'current_folder': self._current_folder, 'current_status': self._current_status, 'stats': self._stats.copy(), 'last_scan_time': self._last_scan_time, } def _interruptible_sleep(self, seconds: float) -> bool: """Sleep in small increments. Returns True if should stop.""" return self._stop_event.wait(seconds) def _run(self): """Main worker loop.""" interval = 60 if self._config_manager: interval = self._config_manager.get('auto_import.scan_interval', 60) # Initial delay to let the app start up if self._interruptible_sleep(10): return while not self.should_stop: if not self.paused: enabled = True if self._config_manager: enabled = self._config_manager.get('auto_import.enabled', False) if enabled: try: self._current_status = 'scanning' self._scan_cycle() self._last_scan_time = datetime.now().isoformat() except Exception as e: logger.error(f"Auto-import scan cycle error: {e}") finally: self._current_status = 'idle' self._current_folder = '' if self._interruptible_sleep(interval): break def _scan_cycle(self): """One full scan of the staging folder.""" staging = self._resolve_staging_path() if not staging or not os.path.isdir(staging): logger.warning(f"[Auto-Import] Staging path not found or invalid: {self.staging_path}") return # Find folder candidates candidates = self._enumerate_folders(staging) logger.info(f"[Auto-Import] Scan cycle: {len(candidates)} candidates in {staging}") if not candidates: return threshold = 0.9 if self._config_manager: threshold = self._config_manager.get('auto_import.confidence_threshold', 0.9) auto_process = True if self._config_manager: auto_process = self._config_manager.get('auto_import.auto_process', True) for candidate in candidates: if self.should_stop or self.paused: break self._current_folder = candidate.name # Skip folders currently being processed by a previous scan cycle if candidate.path in self._processing_paths: logger.debug(f"[Auto-Import] Skipping {candidate.name} — still processing from previous cycle") continue # Check if already processed if self._is_already_processed(candidate.folder_hash): continue # Check stability (files not changing) if not self._is_folder_stable(candidate): continue self._stats['scanned'] += 1 logger.info(f"[Auto-Import] Processing folder: {candidate.name} ({len(candidate.audio_files)} files)") # Mark as in-progress so next scan cycle skips this folder self._processing_paths.add(candidate.path) try: # Phase 3: Identify identification = self._identify_folder(candidate) if not identification: self._record_result(candidate, 'needs_identification', 0.0, error_message='Could not identify album from tags, folder name, or fingerprint') self._stats['failed'] += 1 continue # Phase 4: Match tracks match_result = self._match_tracks(candidate, identification) if not match_result: self._record_result(candidate, 'needs_identification', 0.0, album_id=identification.get('album_id'), album_name=identification.get('album_name'), artist_name=identification.get('artist_name'), image_url=identification.get('image_url'), error_message='Could not match tracks to album tracklist') self._stats['failed'] += 1 continue confidence = match_result['confidence'] status = 'matched' # Check if individual track matches are strong even if overall confidence # is low (e.g. only 2 of 18 album tracks present → low coverage kills # overall score, but the 2 tracks match perfectly and should still import) high_conf_matches = [m for m in match_result.get('matches', []) if m['confidence'] >= 0.8] has_strong_individual_matches = len(high_conf_matches) > 0 if (confidence >= threshold or has_strong_individual_matches) and auto_process: # Phase 5: Auto-process — process all tracks that matched effective_conf = max(confidence, min(m['confidence'] for m in high_conf_matches) if high_conf_matches else 0) logger.info(f"[Auto-Import] Processing {candidate.name} — " f"overall: {confidence:.0%}, {len(high_conf_matches)} strong matches, " f"{match_result.get('matched_count', 0)}/{match_result.get('total_tracks', '?')} tracks") success = self._process_matches(candidate, identification, match_result) status = 'completed' if success else 'failed' confidence = max(confidence, effective_conf) if success: self._stats['auto_processed'] += 1 else: self._stats['failed'] += 1 elif confidence >= 0.7: status = 'pending_review' self._stats['pending_review'] += 1 logger.info(f"[Auto-Import] Medium confidence ({confidence:.0%}) — pending review: {candidate.name}") else: status = 'needs_identification' self._stats['failed'] += 1 logger.info(f"[Auto-Import] Low confidence ({confidence:.0%}) — needs manual ID: {candidate.name}") self._record_result(candidate, status, confidence, album_id=identification.get('album_id'), album_name=identification.get('album_name'), artist_name=identification.get('artist_name'), image_url=identification.get('image_url'), identification_method=identification.get('method'), match_data=match_result) except Exception as e: logger.error(f"[Auto-Import] Error processing {candidate.name}: {e}") self._record_result(candidate, 'failed', 0.0, error_message=str(e)) self._stats['failed'] += 1 finally: self._processing_paths.discard(candidate.path) # Rate limit between folders if self._interruptible_sleep(2): break # ── Scanning ── def _resolve_staging_path(self) -> Optional[str]: path = self.staging_path if self._config_manager: path = self._config_manager.get('import.staging_path', path) # Docker path resolution if os.path.isdir(path): return path for candidate in ['./Staging', '/app/Staging']: if os.path.isdir(candidate): return candidate return None def _enumerate_folders(self, staging: str) -> List[FolderCandidate]: """Find album folder and single file candidates in staging directory (recursive).""" candidates = [] self._scan_directory(staging, candidates) return candidates def _scan_directory(self, directory: str, candidates: List[FolderCandidate]): """Recursively scan a directory for album folders and loose audio files.""" try: entries = sorted(os.listdir(directory)) except OSError: return # Collect loose audio files at this level loose_files = [] subdirs = [] for entry in entries: full_path = os.path.join(directory, entry) if os.path.isfile(full_path) and os.path.splitext(entry)[1].lower() in AUDIO_EXTENSIONS: loose_files.append(full_path) elif os.path.isdir(full_path): subdirs.append((entry, full_path)) if loose_files: # This directory has audio files — treat it as an album folder candidate audio_files = loose_files disc_structure = {} # Check if any subdirs are disc folders has_disc_folders = False for sub_name, sub_path in subdirs: disc_match = DISC_FOLDER_RE.match(sub_name) if disc_match: has_disc_folders = True disc_num = int(disc_match.group(1)) disc_files = [os.path.join(sub_path, f) for f in sorted(os.listdir(sub_path)) if os.path.isfile(os.path.join(sub_path, f)) and os.path.splitext(f)[1].lower() in AUDIO_EXTENSIONS] if disc_files: disc_structure[disc_num] = disc_files audio_files.extend(disc_files) if has_disc_folders: disc_structure[0] = loose_files # Top-level files are disc 0 # Determine if this is a single or album is_single = len(audio_files) == 1 and not has_disc_folders folder_name = os.path.basename(directory) folder_hash = _compute_folder_hash(audio_files) if is_single: candidates.append(FolderCandidate( path=audio_files[0], name=os.path.basename(audio_files[0]), audio_files=audio_files, folder_hash=folder_hash, is_single=True )) else: candidates.append(FolderCandidate( path=directory, name=folder_name, audio_files=audio_files, disc_structure=disc_structure, folder_hash=folder_hash )) else: # No audio files here — recurse into subdirectories for sub_name, sub_path in subdirs: # Skip disc folders at this level (they'll be handled by the parent album) if DISC_FOLDER_RE.match(sub_name): continue self._scan_directory(sub_path, candidates) def _is_folder_stable(self, candidate: FolderCandidate) -> bool: """Check if folder contents have stopped changing.""" try: current_mtime = sum(os.path.getmtime(f) for f in candidate.audio_files if os.path.exists(f)) except OSError: return False prev = self._folder_snapshots.get(candidate.path) self._folder_snapshots[candidate.path] = current_mtime if prev is None: return False # First scan — wait for next cycle to confirm stability return abs(current_mtime - prev) < 0.01 # Unchanged def _is_already_processed(self, folder_hash: str) -> bool: """Check if this folder was already processed.""" try: conn = self.database._get_connection() cursor = conn.cursor() cursor.execute("SELECT status FROM auto_import_history WHERE folder_hash = ? ORDER BY created_at DESC LIMIT 1", (folder_hash,)) row = cursor.fetchone() conn.close() return row and row['status'] in ('completed', 'pending_review', 'needs_identification', 'failed', 'rejected') except Exception: return False # ── Identification ── def _identify_folder(self, candidate: FolderCandidate) -> Optional[Dict]: """Identify what album/track a folder or single file contains.""" if candidate.is_single: return self._identify_single(candidate) # Strategy 1: Read tags tag_result = self._identify_from_tags(candidate) if tag_result: return tag_result # Strategy 2: Parse folder name folder_result = self._identify_from_folder_name(candidate) if folder_result: return folder_result # Strategy 3: AcoustID fingerprint acoustid_result = self._identify_from_acoustid(candidate) if acoustid_result: return acoustid_result return None def _identify_single(self, candidate: FolderCandidate) -> Optional[Dict]: """Identify a single audio file from tags, filename, or AcoustID.""" file_path = candidate.audio_files[0] tags = _read_file_tags(file_path) artist = tags.get('artist', '') title = tags.get('title', '') album = tags.get('album', '') # Fallback: parse filename (Artist - Title.ext) if not artist or not title: basename = os.path.splitext(os.path.basename(file_path))[0] parts = re.split(r'\s*[-–—]\s*', basename, maxsplit=1) if len(parts) == 2: artist = artist or parts[0].strip() title = title or parts[1].strip() elif not title: title = basename.strip() if not title: return None # Search metadata source for track result = self._search_single_track(artist, title, album) if result and result.get('identification_confidence', 0) >= 0.8: return result # Fallback: AcoustID fingerprint (also used when metadata match is weak) try: from core.acoustid_client import AcoustIDClient client = AcoustIDClient() fp_result = client.fingerprint_and_lookup(file_path) if fp_result and fp_result.get('recordings'): best = fp_result['recordings'][0] # AcoustID can return None for artist/title on new releases — # fall back to tag data we already have fp_artist = best.get('artist') or artist fp_title = best.get('title') or title if fp_artist and fp_title: fp_result2 = self._search_single_track(fp_artist, fp_title, '') if fp_result2 and fp_result2.get('identification_confidence', 0) >= 0.8: fp_result2['method'] = 'acoustid' return fp_result2 # Keep weak AcoustID result as fallback if fp_result2 and (not result or fp_result2.get('identification_confidence', 0) > result.get('identification_confidence', 0)): result = fp_result2 except Exception: pass # If we have good tag data (artist + title), prefer tag-based identification # over a weak metadata/AcoustID result — tags from post-processed files are reliable if artist and title and tags.get('artist'): tag_conf = 0.85 # High confidence for files with proper embedded tags # Use the metadata result's image/album data if available, but trust tag identity tag_result = { 'album_id': result.get('album_id') if result else None, 'album_name': album or (result.get('album_name') if result else None) or title, 'artist_name': artist, 'track_name': title, 'image_url': result.get('image_url', '') if result else '', 'release_date': tags.get('year', '') or (result.get('release_date', '') if result else ''), 'track_number': tags.get('track_number', 1), 'total_tracks': result.get('total_tracks', 1) if result else 1, 'source': result.get('source', 'tags') if result else 'tags', 'method': 'tags', 'identification_confidence': tag_conf, 'is_single': True, 'track_id': result.get('track_id', '') if result else '', } return tag_result # If AcoustID didn't help but we had a weak metadata match, use it if result: return result # Last resort: filename-only identification if title: return { 'album_id': None, 'album_name': title, 'artist_name': artist or 'Unknown Artist', 'track_name': title, 'image_url': '', 'release_date': '', 'track_number': 1, 'total_tracks': 1, 'source': 'tags', 'method': 'filename', 'identification_confidence': 0.5, 'is_single': True, } return None def _search_single_track(self, artist: str, title: str, album: str) -> Optional[Dict]: """Search metadata source for a single track match.""" try: from core.metadata_service import get_primary_source, get_client_for_source source = get_primary_source() client = get_client_for_source(source) if not client or not hasattr(client, 'search_tracks'): return None query = f"{artist} {title}" if artist else title results = client.search_tracks(query, limit=5) if not results: return None # Score results best_result = None best_score = 0 for r in results: r_title = getattr(r, 'name', '') or getattr(r, 'title', '') or '' r_artists = getattr(r, 'artists', []) r_artist = '' if r_artists: a = r_artists[0] r_artist = a.get('name', str(a)) if isinstance(a, dict) else str(a) score = _similarity(title, r_title) * 0.6 if artist: score += _similarity(artist, r_artist) * 0.4 if score > best_score: best_score = score best_result = r if not best_result or best_score < 0.5: return None r_artist = '' r_album = '' r_album_id = '' r_image = '' if hasattr(best_result, 'artists') and best_result.artists: a = best_result.artists[0] r_artist = a.get('name', str(a)) if isinstance(a, dict) else str(a) # Extract image — try direct image_url first (Deezer), then album.images (Spotify) r_image = getattr(best_result, 'image_url', '') or '' if hasattr(best_result, 'album'): alb = best_result.album if isinstance(alb, dict): r_album = alb.get('name', '') r_album_id = alb.get('id', '') if not r_image: images = alb.get('images', []) if images: r_image = images[0].get('url', '') if isinstance(images[0], dict) else str(images[0]) elif isinstance(alb, str): r_album = alb # Extract track number and release date from the matched result r_track_number = getattr(best_result, 'track_number', None) or 1 r_release_date = getattr(best_result, 'release_date', '') or '' return { 'album_id': r_album_id or None, 'album_name': r_album or title, 'artist_name': r_artist or artist or '', 'track_name': getattr(best_result, 'name', '') or title, 'track_id': getattr(best_result, 'id', ''), 'image_url': r_image, 'release_date': r_release_date, 'track_number': r_track_number, 'total_tracks': getattr(best_result, 'total_tracks', 1) or 1, 'source': source, 'method': 'tags', 'identification_confidence': best_score, 'is_single': True, } except Exception as e: logger.debug(f"Single track search failed for '{artist} - {title}': {e}") return None def _identify_from_tags(self, candidate: FolderCandidate) -> Optional[Dict]: """Try to identify album from embedded file tags.""" tags_list = [] for f in candidate.audio_files[:20]: # Cap at 20 files tags = _read_file_tags(f) if tags['album'] and tags['artist']: tags_list.append(tags) if len(tags_list) < max(1, len(candidate.audio_files) * 0.5): return None # Less than 50% of files have usable tags # Check consistency — most common album+artist album_artist_counts = {} for t in tags_list: key = (t['album'].lower().strip(), t['artist'].lower().strip()) album_artist_counts[key] = album_artist_counts.get(key, 0) + 1 if not album_artist_counts: return None best_key, best_count = max(album_artist_counts.items(), key=lambda x: x[1]) if best_count < len(tags_list) * 0.6: return None # Tags too inconsistent album_name, artist_name = best_key return self._search_metadata_source(artist_name, album_name, 'tags', candidate) def _identify_from_folder_name(self, candidate: FolderCandidate) -> Optional[Dict]: """Try to identify album from folder name.""" artist, album = _parse_folder_name(candidate.name) query = f"{artist} {album}" if artist else album return self._search_metadata_source(artist, album, 'folder_name', candidate, query=query) def _identify_from_acoustid(self, candidate: FolderCandidate) -> Optional[Dict]: """Try to identify album by fingerprinting a few files.""" try: from core.acoustid_client import AcoustIDClient client = AcoustIDClient() except Exception: return None # Fingerprint first 3 files identified_artists = [] identified_albums = [] for f in candidate.audio_files[:3]: try: result = client.fingerprint_and_lookup(f) if result and result.get('recordings'): best = result['recordings'][0] if best.get('artist'): identified_artists.append(best['artist']) # Try to get album from recording # AcoustID doesn't directly give album — use artist+title to search time.sleep(1) # Rate limit except Exception: continue if not identified_artists: return None # Most common artist from collections import Counter artist = Counter(identified_artists).most_common(1)[0][0] return self._search_metadata_source(artist, candidate.name, 'acoustid', candidate) def _search_metadata_source(self, artist: Optional[str], album: str, method: str, candidate: FolderCandidate, query: str = None) -> Optional[Dict]: """Search the active metadata source for an album match.""" try: from core.metadata_service import get_primary_source, get_client_for_source source = get_primary_source() client = get_client_for_source(source) if not client or not hasattr(client, 'search_albums'): return None search_query = query or (f"{artist} {album}" if artist else album) results = client.search_albums(search_query, limit=5) if not results: return None # Score each result best_result = None best_score = 0 for r in results: score = 0 # Album name similarity (50%) score += _similarity(album, r.name) * 0.5 # Artist similarity (20%) if artist: r_artist = r.artists[0] if hasattr(r, 'artists') and r.artists else '' if isinstance(r_artist, dict): r_artist = r_artist.get('name', '') score += _similarity(artist, str(r_artist)) * 0.2 # Track count match (30%) r_tracks = getattr(r, 'total_tracks', 0) or 0 file_count = len(candidate.audio_files) if r_tracks > 0 and file_count > 0: count_ratio = 1.0 - abs(r_tracks - file_count) / max(r_tracks, file_count) score += max(0, count_ratio) * 0.3 if score > best_score: best_score = score best_result = r if not best_result or best_score < 0.4: return None # Get image image_url = '' if hasattr(best_result, 'image_url'): image_url = best_result.image_url or '' elif hasattr(best_result, 'images') and best_result.images: img = best_result.images[0] image_url = img.get('url', '') if isinstance(img, dict) else str(img) r_artist = '' if hasattr(best_result, 'artists') and best_result.artists: a = best_result.artists[0] r_artist = a.get('name', str(a)) if isinstance(a, dict) else str(a) # Get release date release_date = getattr(best_result, 'release_date', '') or '' return { 'album_id': best_result.id, 'album_name': best_result.name, 'artist_name': r_artist or artist or '', 'image_url': image_url, 'release_date': release_date, 'total_tracks': getattr(best_result, 'total_tracks', 0), 'source': source, 'method': method, 'identification_confidence': best_score, } except Exception as e: logger.debug(f"Metadata search failed for '{album}': {e}") return None # ── Track Matching ── def _match_tracks(self, candidate: FolderCandidate, identification: Dict) -> Optional[Dict]: """Match staging files to the identified album's tracklist.""" # Singles: no album tracklist to match against — the file IS the match if candidate.is_single or identification.get('is_single'): conf = identification.get('identification_confidence', 0.7) track_data = { 'name': identification.get('track_name', identification.get('album_name', '')), 'artists': [{'name': identification.get('artist_name', '')}], 'id': identification.get('track_id', ''), 'track_number': identification.get('track_number', 1), 'disc_number': 1, } return { 'matches': [{'track': track_data, 'file': candidate.audio_files[0], 'confidence': conf}], 'unmatched_files': [], 'total_tracks': 1, 'matched_count': 1, 'coverage': 1.0, 'confidence': conf, 'album_data': {'id': identification.get('album_id') or '', 'name': identification.get('album_name', ''), 'tracks': {'items': [track_data]}}, } try: from core.metadata_service import get_client_for_source, get_album_tracks_for_source source = identification['source'] album_id = identification['album_id'] # Fetch album with tracks client = get_client_for_source(source) if not client: return None album_data = None if hasattr(client, 'get_album'): album_data = client.get_album(album_id) # Fallback: try get_album_metadata (Deezer) or get_album_tracks if not album_data and hasattr(client, 'get_album_metadata'): album_data = client.get_album_metadata(str(album_id), include_tracks=True) if not album_data and hasattr(client, 'get_album_tracks'): tracks_data = client.get_album_tracks(str(album_id)) if tracks_data: album_data = {'id': album_id, 'name': identification.get('album_name', ''), 'tracks': tracks_data} if not album_data: return None # Extract tracks — handle various response formats tracks = [] if isinstance(album_data, dict): if 'tracks' in album_data: raw = album_data['tracks'] if isinstance(raw, dict) and 'items' in raw: tracks = raw['items'] elif isinstance(raw, dict) and 'data' in raw: tracks = raw['data'] # Deezer format elif isinstance(raw, list): tracks = raw elif 'items' in album_data: tracks = album_data['items'] if not tracks: return None # Read tags for all files file_tags = {} for f in candidate.audio_files: file_tags[f] = _read_file_tags(f) # Resolve quality duplicates — if multiple files match same track, keep best # Group by probable track (using track number from tags) seen_track_nums = {} deduped_files = [] for f in candidate.audio_files: tn = file_tags[f]['track_number'] ext = os.path.splitext(f)[1].lower() if tn > 0 and tn in seen_track_nums: prev_f = seen_track_nums[tn] prev_ext = os.path.splitext(prev_f)[1].lower() if _quality_rank(ext) > _quality_rank(prev_ext): deduped_files.remove(prev_f) deduped_files.append(f) seen_track_nums[tn] = f else: deduped_files.append(f) if tn > 0: seen_track_nums[tn] = f # Match files to tracks using weighted scoring matches = [] used_files = set() target_album = identification.get('album_name', '') for track in tracks: track_name = track.get('name', '') track_num = track.get('track_number', 0) track_artists = track.get('artists', []) track_artist = '' if track_artists: a = track_artists[0] track_artist = a.get('name', str(a)) if isinstance(a, dict) else str(a) best_file = None best_score = 0 for f in deduped_files: if f in used_files: continue ft = file_tags[f] score = 0 # Title similarity (45%) title = ft['title'] or os.path.splitext(os.path.basename(f))[0] score += _similarity(title, track_name) * 0.45 # Artist similarity (15%) if ft['artist'] and track_artist: score += _similarity(ft['artist'], track_artist) * 0.15 # Track number (30%) if ft['track_number'] > 0 and track_num > 0: if ft['track_number'] == track_num: score += 0.30 elif abs(ft['track_number'] - track_num) <= 1: score += 0.12 # Album tag bonus (10%) if ft['album']: score += _similarity(ft['album'], target_album) * 0.10 if score > best_score and score >= 0.4: best_score = score best_file = f if best_file: used_files.add(best_file) matches.append({ 'track': track, 'file': best_file, 'confidence': round(best_score, 3), }) if not matches: return None # Compute overall confidence album_conf = identification.get('identification_confidence', 0.5) avg_track_conf = sum(m['confidence'] for m in matches) / len(matches) if matches else 0 coverage = len(matches) / len(tracks) if tracks else 0 overall = album_conf * avg_track_conf * coverage return { 'matches': matches, 'unmatched_files': [f for f in deduped_files if f not in used_files], 'total_tracks': len(tracks), 'matched_count': len(matches), 'coverage': round(coverage, 3), 'confidence': round(overall, 3), 'album_data': album_data, } except Exception as e: logger.error(f"Track matching error: {e}") return None # ── Processing ── def _process_matches(self, candidate: FolderCandidate, identification: Dict, match_result: Dict) -> bool: """Process matched files through the post-processing pipeline.""" if not self._process_callback: logger.warning("No process callback configured — cannot auto-process") return False album_data = match_result.get('album_data', {}) if not isinstance(album_data, dict): album_data = {} source = identification.get('source', 'deezer') artist_name = identification.get('artist_name', 'Unknown') album_name = identification.get('album_name', 'Unknown') image_url = identification.get('image_url', '') # Parent folder artist override: if the staging folder structure is # Artist/Albums/AlbumName or Artist/AlbumName, use the parent folder # as the artist name when the tag-extracted artist looks wrong. # This handles mixtapes/compilations where embedded tags have DJ names. try: staging_root = self._resolve_staging_path() or self.staging_path rel_path = os.path.relpath(candidate.path, staging_root) parts = [p for p in rel_path.replace('\\', '/').split('/') if p] # parts[0] = artist folder, parts[1] = album or category subfolder, etc. # Only attempt override if there's at least 2 levels (artist/album) folder_artist = None if len(parts) >= 2: _category_names = {'albums', 'singles', 'eps', 'compilations', 'mixtapes', 'discography', 'music', 'downloads'} if len(parts) >= 3 and parts[1].lower() in _category_names: # Artist/Albums/AlbumFolder → parts[0] is artist folder_artist = parts[0] elif parts[0].lower() not in _category_names: # Artist/AlbumFolder → parts[0] is artist folder_artist = parts[0] if folder_artist and folder_artist.lower() != artist_name.lower(): logger.info(f"[Auto-Import] Parent folder artist '{folder_artist}' differs from tag artist '{artist_name}' — using folder artist") artist_name = folder_artist except Exception: pass release_date = identification.get('release_date', '') or album_data.get('release_date', '') # Compute total discs total_discs = 1 if candidate.disc_structure and len(candidate.disc_structure) > 1: total_discs = max(candidate.disc_structure.keys()) processed = 0 errors = [] for match in match_result.get('matches', []): track = match['track'] file_path = match['file'] if not os.path.exists(file_path): errors.append(f"File not found: {os.path.basename(file_path)}") continue try: track_name = track.get('name', 'Unknown') track_number = track.get('track_number', 1) disc_number = track.get('disc_number', 1) track_id = track.get('id', '') # Build context matching the manual import format context_key = f"auto_import_{candidate.folder_hash}_{track_number}" context = { 'spotify_artist': { 'id': identification.get('album_id') or 'auto_import', 'name': artist_name, 'genres': [], }, 'spotify_album': { 'id': album_data.get('id') or identification.get('album_id') or '', 'name': album_name, 'release_date': release_date, 'total_tracks': album_data.get('total_tracks', match_result.get('total_tracks', 0)), 'total_discs': total_discs, 'image_url': image_url, 'images': album_data.get('images', [{'url': image_url}] if image_url else []), 'artists': [{'name': artist_name}], 'album_type': album_data.get('album_type', 'album'), }, 'track_info': { 'name': track_name, 'id': track_id, 'track_number': track_number, 'disc_number': disc_number, 'duration_ms': track.get('duration_ms', 0), 'artists': track.get('artists', [{'name': artist_name}]), 'uri': track.get('uri', ''), }, 'original_search_result': { 'title': track_name, 'artist': artist_name, 'album': album_name, 'track_number': track_number, 'disc_number': disc_number, 'spotify_clean_title': track_name, 'spotify_clean_album': album_name, 'spotify_clean_artist': artist_name, 'artists': track.get('artists', [{'name': artist_name}]), }, 'is_album_download': True, 'has_clean_spotify_data': True, 'has_full_spotify_metadata': True, } self._process_callback(context_key, context, file_path) processed += 1 logger.info(f"[Auto-Import] Processed: {track_number}. {track_name}") except Exception as e: errors.append(f"{track.get('name', '?')}: {str(e)}") logger.warning(f"[Auto-Import] Error processing track: {e}") # Emit automation events if processed > 0 and self._automation_engine: try: self._automation_engine.emit('import_completed', { 'track_count': str(processed), 'album_name': album_name, 'artist': artist_name, }) self._automation_engine.emit('batch_complete', { 'playlist_name': f'Import: {album_name}', 'total_tracks': str(len(match_result.get('matches', []))), 'completed_tracks': str(processed), 'failed_tracks': str(len(errors)), }) except Exception: pass return processed > 0 # ── Database ── def _record_result(self, candidate: FolderCandidate, status: str, confidence: float, album_id: str = None, album_name: str = None, artist_name: str = None, image_url: str = None, identification_method: str = None, match_data: Dict = None, error_message: str = None): """Record auto-import result to database.""" try: # Serialize match data (strip non-serializable album_data) match_json = None if match_data: serializable = { 'matches': [{'track_name': m['track']['name'], 'track_number': m['track'].get('track_number', 0), 'file': os.path.basename(m['file']), 'confidence': m['confidence']} for m in match_data.get('matches', [])], 'unmatched_files': [os.path.basename(f) for f in match_data.get('unmatched_files', [])], 'total_tracks': match_data.get('total_tracks', 0), 'matched_count': match_data.get('matched_count', 0), 'coverage': match_data.get('coverage', 0), } match_json = json.dumps(serializable) conn = self.database._get_connection() cursor = conn.cursor() cursor.execute(""" INSERT INTO auto_import_history (folder_name, folder_path, folder_hash, status, confidence, album_id, album_name, artist_name, image_url, total_files, matched_files, match_data, identification_method, error_message, processed_at) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) """, ( candidate.name, candidate.path, candidate.folder_hash, status, confidence, album_id, album_name, artist_name, image_url, len(candidate.audio_files), match_data.get('matched_count', 0) if match_data else 0, match_json, identification_method, error_message, datetime.now().isoformat() if status == 'completed' else None, )) conn.commit() conn.close() except Exception as e: logger.error(f"Error recording auto-import result: {e}") def get_results(self, status_filter: str = None, limit: int = 50) -> List[Dict]: """Get auto-import results from database.""" try: conn = self.database._get_connection() cursor = conn.cursor() if status_filter: cursor.execute(""" SELECT * FROM auto_import_history WHERE status = ? ORDER BY created_at DESC LIMIT ? """, (status_filter, limit)) else: cursor.execute(""" SELECT * FROM auto_import_history ORDER BY created_at DESC LIMIT ? """, (limit,)) rows = cursor.fetchall() conn.close() return [dict(r) for r in rows] except Exception: return [] def approve_item(self, item_id: int) -> Dict: """Approve a pending_review item and process it.""" try: conn = self.database._get_connection() cursor = conn.cursor() cursor.execute("SELECT * FROM auto_import_history WHERE id = ? AND status = 'pending_review'", (item_id,)) row = cursor.fetchone() conn.close() if not row: return {'success': False, 'error': 'Item not found or not pending review'} # Rebuild candidate and match data match_data_raw = json.loads(row['match_data']) if row['match_data'] else None if not match_data_raw: return {'success': False, 'error': 'No match data available'} # We can't easily re-process from stored data alone because we don't store # the full album_data or file paths. Mark as approved and let next scan pick it up. # For now, update status to trigger re-processing. conn = self.database._get_connection() cursor = conn.cursor() cursor.execute("UPDATE auto_import_history SET status = 'approved' WHERE id = ?", (item_id,)) conn.commit() conn.close() return {'success': True, 'message': 'Item approved — will be processed on next scan'} except Exception as e: return {'success': False, 'error': str(e)} def reject_item(self, item_id: int) -> Dict: """Reject/dismiss an auto-import item.""" try: conn = self.database._get_connection() cursor = conn.cursor() cursor.execute("UPDATE auto_import_history SET status = 'rejected' WHERE id = ?", (item_id,)) conn.commit() conn.close() return {'success': True} except Exception as e: return {'success': False, 'error': str(e)}