38 KiB
38 KiB
Spotify Matched Download System - Technical Specification
📋 Document Purpose
This document provides comprehensive technical specifications for implementing the Spotify Matched Download System. It addresses the complexity of music metadata matching, file organization, and user interface design based on real-world challenges and requirements. I have attempted this feature previously with no success. so please give it your best shot to produce your best work.
Expected Use Case for a single(mostly the same for album?)
User clicks 'matched download' button on a 'single' and an elegant modal expands into view that offers two options: the top half (spotify auto matching with a list or slideshow or top 5 likely artists), the bottom half(manual use search on spotify to match the track to an artist). the app will use spotify metadata to update the track name and create the folder structure I detailed. so lets talk about the top half of the modal first. It will automatically populate the top 5 most likely artists to match the track with. each likely artist will display, if possible, the artist image, artist name, and percentage likelihood of match. clicking the artist will select that artist as the matched artist and the download will begin. now the bottom half: it will be a simple but elegant search bar for the user to search for an artist and it will display a list of 5 results similar to the top half but these results are user searched. it will display the same content, artist picture, artist name, percentage liklihood of match. clicking the artist will select that artist as the matched artist and the download will begin. So now that the user has decided which artist the track belongs to the track has begun downloading as normal to the download folder. the track and its parent folder will then appear in the downloads folder once complete. but while the track is downloading the app should attempt to gather additional information about the artist / album / track. specifically we will need to see if the track we downloaded was part of an album and if it is, make sure we create the correct folder structure. if a track is a single. it is layed out like this:
Transfer/
├── EXAMPLE ARTIST/
│ ├── EXAMPLE ARTIST - EXAMPLE SINGLE/
├── EXAMPLE SINGLE.flac
├── cover.png/jpg
if we determine a track we downloaded is part of an album by the matched artist it would be setup like this:
Transfer/
├── EXAMPLE ARTIST/
│ ├── EXAMPLE ARTIST - EXAMPLE ALBUM/
├── TRACK# EXAMPLE SINGLE.flac
├── cover.png/jpg
If we happen to download multiple tracks from the same album they should all end up with the same folder structure and in the same location.
Transfer/
├── EXAMPLE ARTIST/
│ ├── EXAMPLE ARTIST - EXAMPLE ALBUM/
├── TRACK# EXAMPLE SINGLE.flac
├── TRACK# EXAMPLE SINGLE.flac
├── TRACK# EXAMPLE SINGLE.flac
├── cover.png/jpg
├── ...
All accurate title information and cover art for albums, tracks, artists can be found with the matched artist via spotify api. this information is used to for renaming tracks and folders. That way we know tracks and albums will end up together with albums and artists having the exact same name. After we determine if the track is part of an album or not we can begin copying the download to the 'transfer' folder and creating the appropriate folder structure from above and rename the track as needed. After the folder structure is setup correctly we will begin updating the metadata within the actual track file based on the data pulled from spotify. Things like title, track number, genres, album, contributing artists and anything else spotify api provides. once folder structure is done and metadata data for all tracks is done, then delete the original download in the downloads folder and run 'clear completed' buttons function. now with everything cleaned up we can move on to the next matched download.
Now we need to incorporate this functionality into full album downloads by adding a 'matched album download' button beside the 'download album' button. this will essentially do the exact same process as singles but its a big batch added to the queue. we can't assume what we are downloading is an actual 'album' by an artist but could instead be a folder of a users favorite songs. but our app would download those songs and put them in the correct artist folder with correct metadata. if you think im missing intuitive or critical please add it in.
If we fail to match an artist in the modal, treat the download as a normal downoad without any matching and keep it in the downloads folder. Also any matched downloads need to update the 'download queue' the same way a normal download would. The cancel button should remain functional on a matched download in the queue and clicking it should behave exaclty the same. a finished matched download should transfer to finished downloads as expected.
VERY IMPORTANT! DO NOT BREAK ANYTHING
🏗️ System Architecture Overview
Core Components
┌─────────────────────────────────────────────────────────────────┐
│ Spotify Matched Download System │
├─────────────────────────────────────────────────────────────────┤
│ UI Layer │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
│ │ Matched Download│ │ Matching Modal │ │ Progress Tracking│ │
│ │ Buttons │ │ │ │ │ │
│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ Service Layer │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
│ │ Metadata │ │ Spotify Matching│ │ File Organization│ │
│ │ Extraction │ │ Service │ │ Service │ │
│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
├─────────────────────────────────────────────────────────────────┤
│ Integration Layer │
│ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │
│ │ Soulseek │ │ Spotify │ │ File System │ │
│ │ Client │ │ Client │ │ Manager │ │
│ └─────────────────┘ └─────────────────┘ └─────────────────┘ │
└─────────────────────────────────────────────────────────────────┘
🔍 Advanced Metadata Extraction System
Problem Statement
Soulseek metadata is notoriously inconsistent:
- Filenames:
"Track 01.mp3","asdjklh.flac","Artist - Song (Remix).mp3" - Directory paths:
"/User/Music/Random Folder/" - Missing or incorrect artist/title/album information
Solution: Multi-Tier Extraction Strategy
Tier 1: Leverage Existing TrackResult Fields (PRIMARY)
class EnhancedMetadataExtractor:
def extract_from_track_result(self, track: TrackResult) -> TrackMetadata:
"""
Primary extraction using existing TrackResult fields.
These are already parsed by soulseek_client.py
"""
return TrackMetadata(
artist=track.artist, # Already extracted!
title=track.title, # Already extracted!
album=track.album, # Already extracted!
track_number=track.track_number, # Already extracted!
filename=track.filename,
confidence=self.calculate_field_confidence(track)
)
Tier 2: Enhanced Filename Parsing (SECONDARY)
class AdvancedFilenameParser:
PATTERNS = [
# Pattern: "Artist - Title"
r'^(?P<artist>.+?)\s*[-–—]\s*(?P<title>.+?)(?:\s*\[(?P<extra>.*?)\])?(?:\s*\((?P<remix>.*?[Rr]emix.*?)\))?$',
# Pattern: "01 - Artist - Title"
r'^(?P<track>\d+)\s*[-\.]\s*(?P<artist>.+?)\s*[-–—]\s*(?P<title>.+?)(?:\s*\((?P<remix>.*?)\))?$',
# Pattern: "Artist - Album - Title"
r'^(?P<artist>.+?)\s*[-–—]\s*(?P<album>.+?)\s*[-–—]\s*(?P<title>.+?)$',
# Pattern: "Title (Artist Remix)"
r'^(?P<title>.+?)\s*\((?P<remix_artist>.+?)\s+[Rr]emix\)$',
# Pattern: "Album - Track - Title"
r'^(?P<album>.+?)\s*[-–—]\s*(?P<track>\d+)\s*[-–—]\s*(?P<title>.+?)$'
]
def parse_filename(self, filename: str) -> Optional[TrackMetadata]:
"""Enhanced filename parsing with remix detection"""
base_name = self.clean_filename(filename)
for pattern in self.PATTERNS:
match = re.match(pattern, base_name, re.IGNORECASE)
if match:
return self.create_metadata_from_match(match)
return None
Tier 3: Directory Context Analysis (TERTIARY)
class DirectoryContextAnalyzer:
def analyze_path_context(self, filepath: str) -> Optional[AlbumContext]:
"""
Extract album context from directory structure
Example: "/Music/Artist/Album (Year)/Track.flac"
"""
path_parts = Path(filepath).parts
# Common patterns for album directories
album_patterns = [
r'(?P<artist>.+?)\s*[-–—]\s*(?P<album>.+?)(?:\s*\((?P<year>\d{4})\))?',
r'(?P<album>.+?)(?:\s*\((?P<year>\d{4})\))?',
r'\[(?P<year>\d{4})\]\s*(?P<album>.+?)'
]
# Analyze parent directories for album info
for part in reversed(path_parts):
for pattern in album_patterns:
match = re.match(pattern, part, re.IGNORECASE)
if match:
return AlbumContext(**match.groupdict())
return None
🎵 Sophisticated Matching Algorithms
Multi-Stage Matching Pipeline
Stage 1: Exact Match Strategy
class ExactMatcher:
def find_exact_match(self, metadata: TrackMetadata) -> List[SpotifyMatch]:
"""
Highest confidence matching with exact metadata
"""
if not (metadata.artist and metadata.title):
return []
# Build exact search query
query_parts = []
if metadata.artist:
query_parts.append(f'artist:"{metadata.artist}"')
if metadata.title:
query_parts.append(f'track:"{metadata.title}"')
if metadata.album:
query_parts.append(f'album:"{metadata.album}"')
query = ' '.join(query_parts)
results = self.spotify_client.search_tracks(query, limit=5)
return [SpotifyMatch(track, confidence=0.95) for track in results[:3]]
Stage 2: Fuzzy Match Strategy
class FuzzyMatcher:
def find_fuzzy_matches(self, metadata: TrackMetadata) -> List[SpotifyMatch]:
"""
Similarity-based matching with confidence scoring
"""
# Normalize strings for comparison
normalized_artist = self.normalize_string(metadata.artist)
normalized_title = self.normalize_string(metadata.title)
# Generate search variations
search_queries = [
f"{normalized_artist} {normalized_title}",
f"{metadata.artist} {metadata.title}", # Original strings
f'"{normalized_artist}" "{normalized_title}"', # Quoted search
]
all_matches = []
for query in search_queries:
results = self.spotify_client.search_tracks(query, limit=10)
for track in results:
confidence = self.calculate_similarity_confidence(metadata, track)
if confidence >= 0.6: # Minimum threshold
all_matches.append(SpotifyMatch(track, confidence))
# Deduplicate and sort by confidence
return self.deduplicate_matches(all_matches)
def calculate_similarity_confidence(self, metadata: TrackMetadata, spotify_track: SpotifyTrack) -> float:
"""
Advanced confidence calculation with multiple factors
"""
# Artist similarity (weight: 40%)
artist_sim = self.string_similarity(
self.normalize_string(metadata.artist),
self.normalize_string(spotify_track.artists[0])
)
# Title similarity (weight: 50%)
title_sim = self.string_similarity(
self.normalize_string(metadata.title),
self.normalize_string(spotify_track.name)
)
# Album similarity (weight: 10%)
album_sim = 0.0
if metadata.album and spotify_track.album:
album_sim = self.string_similarity(
self.normalize_string(metadata.album),
self.normalize_string(spotify_track.album)
)
# Duration similarity bonus (weight: bonus +5%)
duration_bonus = 0.0
if metadata.duration and spotify_track.duration_ms:
duration_diff = abs(metadata.duration - (spotify_track.duration_ms / 1000))
if duration_diff <= 5: # Within 5 seconds
duration_bonus = 0.05
confidence = (artist_sim * 0.4) + (title_sim * 0.5) + (album_sim * 0.1) + duration_bonus
return min(confidence, 1.0)
Stage 3: Remix Detection & Handling
class RemixMatcher:
REMIX_PATTERNS = [
r'(?P<title>.+?)\s*\((?P<remix_artist>.+?)\s+[Rr]emix\)',
r'(?P<title>.+?)\s*\[(?P<remix_artist>.+?)\s+[Rr]emix\]',
r'(?P<title>.+?)\s*-\s*(?P<remix_artist>.+?)\s+[Rr]emix',
r'(?P<title>.+?)\s+\((?P<remix_artist>.+?)\s+[Vv]ersion\)',
]
def detect_remix(self, title: str) -> Optional[RemixInfo]:
"""
Extract remix information from track title
"""
for pattern in self.REMIX_PATTERNS:
match = re.search(pattern, title, re.IGNORECASE)
if match:
return RemixInfo(
original_title=match.group('title').strip(),
remix_artist=match.group('remix_artist').strip(),
is_remix=True
)
return None
def match_remix_track(self, metadata: TrackMetadata, remix_info: RemixInfo) -> List[SpotifyMatch]:
"""
Search for remix tracks with proper artist attribution
"""
search_queries = [
f'artist:"{remix_info.remix_artist}" track:"{remix_info.original_title}"',
f'"{remix_info.remix_artist}" "{remix_info.original_title}" remix',
f'"{remix_info.original_title}" "{remix_info.remix_artist}"'
]
matches = []
for query in search_queries:
results = self.spotify_client.search_tracks(query, limit=5)
for track in results:
# Prioritize tracks where remix artist is primary artist
if remix_info.remix_artist.lower() in [a.lower() for a in track.artists]:
confidence = 0.85 # High confidence for proper remix attribution
matches.append(SpotifyMatch(track, confidence, match_type="remix"))
return matches
🎨 Professional UI Architecture
Responsive Modal Design
Problem with Previous Implementation
- "Squished" content with poor spacing
- Inflexible layouts that didn't adapt to content
- Poor user experience with cramped interface
Solution: Professional Modal Architecture
class ResponsiveMatchingModal(QDialog):
"""
Professional modal with responsive design and proper spacing
"""
def __init__(self, parent=None):
super().__init__(parent)
self.setup_responsive_ui()
def setup_responsive_ui(self):
"""
Create responsive layout with proper spacing and sizing
"""
# Modal sizing - responsive to screen size
screen = QApplication.primaryScreen().geometry()
modal_width = min(900, int(screen.width() * 0.7)) # 70% of screen width, max 900px
modal_height = min(700, int(screen.height() * 0.8)) # 80% of screen height, max 700px
self.resize(modal_width, modal_height)
self.setMinimumSize(600, 500) # Minimum usable size
# Center on parent/screen
self.center_on_parent()
# Main layout with proper margins
main_layout = QVBoxLayout(self)
main_layout.setContentsMargins(24, 24, 24, 24) # Generous margins
main_layout.setSpacing(20) # Proper spacing between sections
# Create sections
self.create_header_section(main_layout)
self.create_progress_section(main_layout)
self.create_results_section(main_layout)
self.create_manual_search_section(main_layout)
self.create_action_buttons_section(main_layout)
def create_header_section(self, parent_layout):
"""
Track information header with proper typography
"""
header_frame = QFrame()
header_frame.setStyleSheet("""
QFrame {
background: rgba(30, 30, 30, 0.9);
border-radius: 12px;
padding: 20px;
}
""")
header_layout = QVBoxLayout(header_frame)
header_layout.setSpacing(12)
# Title with proper typography
title = QLabel("🎯 Spotify Track Matching")
title.setStyleSheet("""
QLabel {
font-size: 22px;
font-weight: bold;
color: #1db954;
margin-bottom: 8px;
}
""")
# Track info with readable formatting
track_info_layout = QGridLayout()
track_info_layout.setColumnStretch(1, 1) # Second column expands
# Add track details with proper alignment
self.add_info_row(track_info_layout, 0, "Track:", self.track_metadata.title)
self.add_info_row(track_info_layout, 1, "Artist:", self.track_metadata.artist)
self.add_info_row(track_info_layout, 2, "Album:", self.track_metadata.album or "Unknown")
header_layout.addWidget(title)
header_layout.addLayout(track_info_layout)
parent_layout.addWidget(header_frame)
def create_results_section(self, parent_layout):
"""
Results section with proper scrolling and spacing
"""
results_frame = QFrame()
results_frame.setStyleSheet("""
QFrame {
background: rgba(40, 40, 40, 0.9);
border-radius: 12px;
padding: 20px;
}
""")
results_layout = QVBoxLayout(results_frame)
results_layout.setSpacing(16)
# Section title
results_title = QLabel("🎵 Automatic Match Results")
results_title.setStyleSheet("""
QLabel {
font-size: 18px;
font-weight: bold;
color: white;
margin-bottom: 10px;
}
""")
# Scrollable results area
scroll_area = QScrollArea()
scroll_area.setWidgetResizable(True)
scroll_area.setHorizontalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarNever)
scroll_area.setVerticalScrollBarPolicy(Qt.ScrollBarPolicy.ScrollBarAsNeeded)
scroll_area.setMinimumHeight(200) # Ensure minimum visible area
scroll_area.setStyleSheet("""
QScrollArea {
border: none;
background: transparent;
}
QScrollBar:vertical {
background: rgba(60, 60, 60, 0.5);
width: 12px;
border-radius: 6px;
}
QScrollBar::handle:vertical {
background: rgba(29, 185, 84, 0.8);
border-radius: 6px;
min-height: 20px;
}
""")
# Results container
self.results_container = QWidget()
self.results_layout = QVBoxLayout(self.results_container)
self.results_layout.setSpacing(12) # Proper spacing between result items
self.results_layout.setContentsMargins(0, 0, 0, 0)
scroll_area.setWidget(self.results_container)
results_layout.addWidget(results_title)
results_layout.addWidget(scroll_area, 1) # Expand to fill space
parent_layout.addWidget(results_frame, 1) # Allow results section to expand
Individual Result Item Design
class SpotifyMatchResultItem(QFrame):
"""
Individual Spotify match result with professional styling
"""
def __init__(self, spotify_track: SpotifyTrack, confidence: float, parent=None):
super().__init__(parent)
self.spotify_track = spotify_track
self.confidence = confidence
self.setup_professional_ui()
def setup_professional_ui(self):
"""
Create professional result item with proper spacing
"""
self.setFixedHeight(100) # Consistent height for all items
self.setStyleSheet("""
QFrame {
background: rgba(50, 50, 50, 0.8);
border: 1px solid rgba(80, 80, 80, 0.6);
border-radius: 10px;
margin: 4px 0px;
}
QFrame:hover {
background: rgba(60, 60, 60, 0.9);
border-color: rgba(29, 185, 84, 0.8);
}
""")
layout = QHBoxLayout(self)
layout.setContentsMargins(16, 12, 16, 12) # Proper margins
layout.setSpacing(16) # Good spacing between elements
# Left section: Track info
info_layout = QVBoxLayout()
info_layout.setSpacing(4)
# Track title
title_label = QLabel(spotify_track.name)
title_label.setStyleSheet("""
QLabel {
font-size: 16px;
font-weight: bold;
color: white;
}
""")
title_label.setWordWrap(True)
# Artist and album
artist_text = ", ".join(spotify_track.artists)
details_label = QLabel(f"by {artist_text}")
details_label.setStyleSheet("""
QLabel {
font-size: 13px;
color: rgba(255, 255, 255, 0.8);
}
""")
album_label = QLabel(f"from {spotify_track.album}")
album_label.setStyleSheet("""
QLabel {
font-size: 12px;
color: rgba(255, 255, 255, 0.6);
}
""")
info_layout.addWidget(title_label)
info_layout.addWidget(details_label)
info_layout.addWidget(album_label)
# Right section: Confidence and select button
right_layout = QVBoxLayout()
right_layout.setAlignment(Qt.AlignmentFlag.AlignTop)
# Confidence indicator
confidence_widget = self.create_confidence_widget()
# Select button
select_button = QPushButton("Select This Track")
select_button.setStyleSheet("""
QPushButton {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 rgba(29, 185, 84, 0.9),
stop:1 rgba(25, 156, 71, 0.9));
color: white;
border: none;
border-radius: 6px;
padding: 8px 16px;
font-weight: bold;
font-size: 13px;
}
QPushButton:hover {
background: qlineargradient(x1:0, y1:0, x2:0, y2:1,
stop:0 rgba(32, 200, 90, 1.0),
stop:1 rgba(28, 170, 76, 1.0));
}
""")
select_button.clicked.connect(self.on_select_clicked)
right_layout.addWidget(confidence_widget)
right_layout.addStretch()
right_layout.addWidget(select_button)
# Assembly
layout.addLayout(info_layout, 1) # Expand info section
layout.addLayout(right_layout)
def create_confidence_widget(self) -> QWidget:
"""
Create professional confidence indicator
"""
confidence_widget = QFrame()
confidence_widget.setFixedSize(60, 60)
# Color based on confidence level
if self.confidence >= 0.9:
color = "#28a745" # Green
text_color = "white"
elif self.confidence >= 0.75:
color = "#ffc107" # Yellow
text_color = "black"
elif self.confidence >= 0.6:
color = "#fd7e14" # Orange
text_color = "white"
else:
color = "#dc3545" # Red
text_color = "white"
confidence_widget.setStyleSheet(f"""
QFrame {{
background: {color};
border-radius: 30px;
border: 2px solid rgba(255, 255, 255, 0.2);
}}
""")
layout = QVBoxLayout(confidence_widget)
layout.setContentsMargins(0, 0, 0, 0)
percentage_label = QLabel(f"{int(self.confidence * 100)}%")
percentage_label.setAlignment(Qt.AlignmentFlag.AlignCenter)
percentage_label.setStyleSheet(f"""
QLabel {{
color: {text_color};
font-size: 14px;
font-weight: bold;
}}
""")
layout.addWidget(percentage_label)
return confidence_widget
📁 Professional File Organization System
Atomic File Operations
class AtomicFileOrganizer:
"""
Professional file organization with rollback capability
"""
def __init__(self, transfer_base_path: str = "Transfer"):
self.transfer_base_path = Path(transfer_base_path)
self.operation_log = [] # Track operations for rollback
def organize_file(self, source_path: str, spotify_track: SpotifyTrack) -> FileOrganizationResult:
"""
Atomically organize file with full rollback capability
"""
try:
# Phase 1: Validation
source_file = Path(source_path)
if not source_file.exists():
return FileOrganizationResult(
success=False,
error="Source file does not exist",
source_path=source_path
)
# Phase 2: Destination planning
destination_path = self.calculate_destination_path(spotify_track, source_file.suffix)
# Phase 3: Conflict resolution
final_destination = self.resolve_conflicts(destination_path)
# Phase 4: Atomic operation
return self.perform_atomic_move(source_file, final_destination)
except Exception as e:
return FileOrganizationResult(
success=False,
error=f"Organization failed: {str(e)}",
source_path=source_path
)
def calculate_destination_path(self, spotify_track: SpotifyTrack, file_extension: str) -> Path:
"""
Calculate organized file path following professional naming conventions
"""
# Sanitize names for filesystem compatibility
artist_name = self.sanitize_filename(spotify_track.artists[0])
album_name = self.sanitize_filename(spotify_track.album)
track_name = self.sanitize_filename(spotify_track.name)
# Handle multi-artist tracks
if len(spotify_track.artists) > 1:
primary_artist = spotify_track.artists[0]
# Keep featured artists in track name
if "feat." in track_name.lower() or "featuring" in track_name.lower():
final_track_name = f"{primary_artist} - {track_name}"
else:
featured_artists = ", ".join(spotify_track.artists[1:])
final_track_name = f"{primary_artist} - {track_name} (feat. {featured_artists})"
else:
final_track_name = f"{artist_name} - {track_name}"
# Create path structure
artist_folder = artist_name
album_folder = f"{artist_name} - {album_name}"
filename = f"{final_track_name}{file_extension}"
return self.transfer_base_path / artist_folder / album_folder / filename
def resolve_conflicts(self, destination_path: Path) -> Path:
"""
Handle file naming conflicts professionally
"""
if not destination_path.exists():
return destination_path
# Generate unique filename
base_path = destination_path.parent / destination_path.stem
extension = destination_path.suffix
counter = 1
while True:
new_path = Path(f"{base_path} ({counter}){extension}")
if not new_path.exists():
return new_path
counter += 1
# Safety limit
if counter > 100:
raise Exception("Too many file conflicts")
def perform_atomic_move(self, source: Path, destination: Path) -> FileOrganizationResult:
"""
Perform atomic file move with backup and rollback
"""
operation_id = str(uuid.uuid4())
try:
# Ensure destination directory exists
destination.parent.mkdir(parents=True, exist_ok=True)
# Create backup if destination exists
backup_path = None
if destination.exists():
backup_path = destination.with_suffix(f".backup_{operation_id}")
shutil.copy2(destination, backup_path)
self.operation_log.append({
'operation_id': operation_id,
'type': 'backup',
'path': backup_path
})
# Perform the move
shutil.move(str(source), str(destination))
self.operation_log.append({
'operation_id': operation_id,
'type': 'move',
'source': str(source),
'destination': str(destination)
})
# Cleanup backup on success
if backup_path and backup_path.exists():
backup_path.unlink()
return FileOrganizationResult(
success=True,
source_path=str(source),
destination_path=str(destination),
operation_id=operation_id
)
except Exception as e:
# Rollback on failure
self.rollback_operation(operation_id)
return FileOrganizationResult(
success=False,
error=f"File move failed: {str(e)}",
source_path=str(source)
)
def sanitize_filename(self, name: str) -> str:
"""
Sanitize filename for cross-platform compatibility
"""
# Remove/replace invalid characters
invalid_chars = r'<>:"/\|?*'
for char in invalid_chars:
name = name.replace(char, '')
# Handle special cases
name = name.replace('..', '.') # Double dots
name = re.sub(r'\s+', ' ', name) # Multiple spaces
name = name.strip(' .') # Leading/trailing spaces and dots
# Length limit
if len(name) > 200:
name = name[:200].rsplit(' ', 1)[0] # Break at word boundary
return name or "Unknown" # Fallback for empty names
🔄 Integration with Existing Download System
Download Completion Detection
class DownloadCompletionMonitor:
"""
Monitor download completions and trigger matching process
"""
def __init__(self, download_manager, matching_service):
self.download_manager = download_manager
self.matching_service = matching_service
self.pending_matches = {} # Track matched downloads
def register_matched_download(self, search_result, track_metadata):
"""
Register a download for post-completion matching
"""
download_id = self.generate_download_id(search_result)
self.pending_matches[download_id] = {
'search_result': search_result,
'track_metadata': track_metadata,
'timestamp': time.time()
}
def on_download_completed(self, download_item):
"""
Handle download completion and trigger matching if needed
"""
download_id = self.generate_download_id(download_item.search_result)
if download_id in self.pending_matches:
# This was a matched download - trigger matching process
match_info = self.pending_matches[download_id]
self.trigger_post_download_matching(download_item, match_info)
del self.pending_matches[download_id]
def trigger_post_download_matching(self, download_item, match_info):
"""
Start matching process after download completion
"""
# Update track metadata with actual download path
track_metadata = match_info['track_metadata']
track_metadata.file_path = download_item.local_path
# Show matching modal
modal = MatchingModal(
matching_service=self.matching_service,
track_metadata=track_metadata,
download_path=download_item.local_path
)
modal.show()
🧪 Testing Strategy
Unit Tests
class TestMetadataExtraction:
"""Test metadata extraction with real-world examples"""
def test_common_filename_patterns(self):
test_cases = [
("Artist - Song.mp3", {"artist": "Artist", "title": "Song"}),
("01 - Artist - Song.flac", {"track": 1, "artist": "Artist", "title": "Song"}),
("Song (Artist Remix).mp3", {"title": "Song", "remix_artist": "Artist"}),
("Artist - Album - Song.mp3", {"artist": "Artist", "album": "Album", "title": "Song"}),
]
extractor = AdvancedFilenameParser()
for filename, expected in test_cases:
result = extractor.parse_filename(filename)
assert result.artist == expected.get("artist")
assert result.title == expected.get("title")
class TestMatchingAlgorithms:
"""Test matching accuracy with known examples"""
def test_exact_matches(self):
"""Test exact matching with perfect metadata"""
pass
def test_fuzzy_matches(self):
"""Test fuzzy matching with slight variations"""
pass
def test_remix_detection(self):
"""Test remix detection and proper artist attribution"""
pass
class TestFileOrganization:
"""Test file organization and conflict resolution"""
def test_atomic_operations(self):
"""Test atomic file moves with rollback"""
pass
def test_conflict_resolution(self):
"""Test handling of duplicate files"""
pass
def test_cross_platform_compatibility(self):
"""Test filename sanitization across platforms"""
pass
Integration Tests
class TestEndToEndWorkflow:
"""Test complete matched download workflow"""
def test_single_track_workflow(self):
"""Test complete single track matched download"""
pass
def test_error_handling_workflow(self):
"""Test error scenarios and fallbacks"""
pass
def test_ui_responsiveness(self):
"""Test UI behavior under various conditions"""
pass
📊 Performance Considerations
Optimization Strategies
- Caching: Cache Spotify search results to avoid duplicate API calls
- Batch Processing: Group multiple searches for efficiency
- Lazy Loading: Load UI elements as needed
- Background Processing: Perform heavy operations in separate threads
- Memory Management: Proper cleanup of modal dialogs and threads
Monitoring & Metrics
- Track matching success rates
- Monitor API response times
- Log file organization errors
- Measure user interaction patterns
🎯 Implementation Priorities
Phase 1: Core Foundation
- Enhanced metadata extraction system
- Basic matching algorithms
- File organization framework
- Professional UI architecture
Phase 2: Advanced Features
- Remix detection and handling
- Confidence scoring system
- Error handling and rollback
- Performance optimizations
Phase 3: Integration & Polish
- Download system integration
- Comprehensive testing
- User experience refinements
- Documentation and deployment
This specification provides a comprehensive foundation for implementing a professional-grade Spotify matching system that addresses real-world complexity and user experience requirements.