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Signed-off-by: Matt Bruce <mbrucedogs@gmail.com>

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Matt Bruce 2025-07-31 16:01:35 -05:00
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Dockerfile Normal file
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FROM python:3.9-slim
WORKDIR /app
# Install system dependencies
RUN apt-get update && apt-get install -y \
gcc \
libpq-dev \
&& rm -rf /var/lib/apt/lists/*
# Copy requirements and install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt
# Copy the application code
COPY . .
# Set environment variables for database connection
ENV DB_HOST=db
ENV DB_PORT=5432
ENV DB_NAME=musicbrainz_db
ENV DB_USER=musicbrainz
ENV DB_PASSWORD=musicbrainz
# Default command
CMD ["python3", "musicbrainz_cleaner.py"]

242
PRD.md
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@ -4,9 +4,9 @@
## Project Overview
**Product Name:** MusicBrainz Data Cleaner
**Version:** 2.0.0
**Date:** July 31, 2025
**Status:** Enhanced with Direct Database Access ✅
**Version:** 3.0.0
**Date:** December 19, 2024
**Status:** Production Ready with Advanced Database Integration ✅
## Problem Statement
@ -15,7 +15,10 @@ Users have song data in JSON format with inconsistent artist names, song titles,
- Correct song titles (e.g., "Shot In The Dark" → "Shot in the Dark")
- Add MusicBrainz IDs (MBIDs) for artists and recordings
- Preserve existing data structure while adding new fields
- Handle complex collaborations (e.g., "Pitbull ft. Ne-Yo, Afrojack & Nayer")
- **NEW**: Use fuzzy search for better matching of similar names
- **NEW**: Handle artist aliases and name variations (e.g., "98 Degrees" → "98°")
- **NEW**: Distinguish between band names and collaborations (e.g., "Simon & Garfunkel" vs "Lavato, Demi & Joe Jonas")
## Target Users
@ -38,50 +41,67 @@ Users have song data in JSON format with inconsistent artist names, song titles,
#### 2. Artist Name Normalization
- **REQ-006:** Convert "ACDC" to "AC/DC"
- **REQ-007:** Convert "ft." to "feat." in collaborations
- **REQ-008:** Handle "featuring" variations
- **REQ-008:** Handle "featuring" variations (case-insensitive)
- **REQ-009:** Extract main artist from collaborations (e.g., "Bruno Mars ft. Cardi B" → "Bruno Mars")
- **NEW REQ-010:** Handle artist aliases (e.g., "98 Degrees" → "98°", "S Club 7" → "S Club")
- **NEW REQ-011:** Handle sort names (e.g., "Corby, Matt" → "Matt Corby")
- **NEW REQ-012:** Handle dash variations (e.g., "Blink-182" vs "blink‐182" with Unicode dash)
- **NEW REQ-013:** Handle numerical suffixes in names (e.g., "S Club 7" → "S Club")
#### 3. Song Title Normalization
- **REQ-010:** Remove karaoke suffixes: "(Karaoke Version)", "(Karaoke)", "(Instrumental)"
- **REQ-011:** Normalize capitalization and formatting
- **REQ-012:** Handle remix variations
#### 3. Collaboration Detection & Handling
- **NEW REQ-014:** Detect primary collaboration patterns: "ft.", "feat.", "featuring" (case-insensitive)
- **NEW REQ-015:** Detect secondary collaboration patterns: "&", "and", "," with intelligence
- **NEW REQ-016:** Distinguish band names from collaborations (e.g., "Simon & Garfunkel" vs "Lavato, Demi & Joe Jonas")
- **NEW REQ-017:** Handle complex collaborations: "Pitbull ft. Ne-Yo, Afrojack & Nayer"
- **NEW REQ-018:** Preserve full artist credit for collaborations in recording data
- **NEW REQ-019:** Extract individual collaborators from collaboration strings
#### 4. MusicBrainz Integration
- **REQ-013:** Connect to local MusicBrainz server (default: localhost:5001)
- **REQ-014:** Search for artists by name
- **REQ-015:** Search for recordings by artist and title
- **REQ-016:** Retrieve detailed artist and recording information
- **REQ-017:** Handle API errors gracefully
- **NEW REQ-018:** Direct PostgreSQL database access for improved performance
- **NEW REQ-019:** Fuzzy search capabilities for better name matching
- **NEW REQ-020:** Fallback to HTTP API when database access unavailable
#### 4. Song Title Normalization
- **REQ-020:** Remove karaoke suffixes: "(Karaoke Version)", "(Karaoke)", "(Instrumental)"
- **REQ-021:** Normalize capitalization and formatting
- **REQ-022:** Handle remix variations
#### 5. CLI Interface
- **REQ-021:** Command-line interface with argument parsing
- **REQ-022:** Support for input and optional output file specification
- **REQ-023:** Progress reporting during processing
- **REQ-024:** Error handling and user-friendly messages
- **NEW REQ-025:** Option to force API mode with `--use-api` flag
#### 5. MusicBrainz Integration
- **REQ-023:** Connect to local MusicBrainz server (default: localhost:5001)
- **REQ-024:** Search for artists by name
- **REQ-025:** Search for recordings by artist and title
- **REQ-026:** Retrieve detailed artist and recording information
- **REQ-027:** Handle API errors gracefully
- **REQ-028:** Direct PostgreSQL database access for improved performance
- **REQ-029:** Fuzzy search capabilities for better name matching
- **REQ-030:** Fallback to HTTP API when database access unavailable
- **NEW REQ-031:** Search artist aliases table for name variations
- **NEW REQ-032:** Search sort_name field for "Last, First" name formats
- **NEW REQ-033:** Handle artist_credit lookups for collaborations
#### 6. CLI Interface
- **REQ-034:** Command-line interface with argument parsing
- **REQ-035:** Support for input and optional output file specification
- **REQ-036:** Progress reporting during processing
- **REQ-037:** Error handling and user-friendly messages
- **REQ-038:** Option to force API mode with `--use-api` flag
### ✅ Non-Functional Requirements
#### 1. Performance
- **REQ-026:** Process songs with reasonable speed (0.1s delay between API calls)
- **REQ-027:** Handle large song collections efficiently
- **NEW REQ-028:** Direct database access for maximum performance (no rate limiting)
- **NEW REQ-029:** Fuzzy search with configurable similarity thresholds
- **REQ-039:** Process songs with reasonable speed (0.1s delay between API calls)
- **REQ-040:** Handle large song collections efficiently
- **REQ-041:** Direct database access for maximum performance (no rate limiting)
- **REQ-042:** Fuzzy search with configurable similarity thresholds
- **NEW REQ-043:** Remove static known_artists lookup for better accuracy
#### 2. Reliability
- **REQ-030:** Graceful handling of missing artists/recordings
- **REQ-031:** Continue processing even if individual songs fail
- **REQ-032:** Preserve original data if cleaning fails
- **NEW REQ-033:** Automatic fallback from database to API mode
- **REQ-044:** Graceful handling of missing artists/recordings
- **REQ-045:** Continue processing even if individual songs fail
- **REQ-046:** Preserve original data if cleaning fails
- **REQ-047:** Automatic fallback from database to API mode
- **NEW REQ-048:** Handle database connection timeouts gracefully
#### 3. Usability
- **REQ-034:** Clear progress indicators
- **REQ-035:** Informative error messages
- **REQ-036:** Help documentation and usage examples
- **NEW REQ-037:** Connection mode indication (database vs API)
- **REQ-049:** Clear progress indicators
- **REQ-050:** Informative error messages
- **REQ-051:** Help documentation and usage examples
- **REQ-052:** Connection mode indication (database vs API)
## Technical Specifications
@ -118,14 +138,18 @@ src/
- **Error Handling**: Graceful error handling with meaningful messages
- **Performance First**: Direct database access for maximum speed
- **Fallback Strategy**: Automatic fallback to API when database unavailable
- **NEW**: **Database-First**: Always use live database data over static caches
- **NEW**: **Intelligent Collaboration Detection**: Distinguish band names from collaborations
### Data Flow
1. Read JSON input file
2. For each song:
- Clean artist name
- **NEW**: Use fuzzy search to find artist in database
- Clean artist name using name variations
- Detect collaboration patterns
- Use fuzzy search to find artist in database (including aliases, sort_names)
- Clean song title
- **NEW**: Use fuzzy search to find recording by artist and title
- For collaborations: find artist_credit and recording
- For single artists: find recording by artist and title
- Update song object with corrected data and MBIDs
3. Write cleaned data to output file
@ -136,14 +160,26 @@ src/
- Title matching: 85% similarity
- **Matching Strategies**: Ratio, Partial Ratio, Token Sort Ratio
- **Performance**: Optimized for large datasets
- **NEW**: **Enhanced Search Fields**: artist.name, artist_alias.name, artist.sort_name
- **NEW**: **Dash Handling**: Explicit handling of regular dash (-) vs Unicode dash (‐)
- **NEW**: **Substring Protection**: Stricter filtering to avoid false matches (e.g., "Sleazy-E" vs "Eazy-E")
### Collaboration Detection Logic
- **Primary Patterns**: "ft.", "feat.", "featuring" (always collaborations)
- **Secondary Patterns**: "&", "and", "," (intelligent detection)
- **Band Name Protection**: Hardcoded list of obvious band names
- **Comma Detection**: Parts with commas are likely collaborations
- **Word Count Analysis**: Single-word parts separated by "&" might be band names
- **Case Insensitivity**: All pattern matching is case-insensitive
### Known Limitations
- Requires local MusicBrainz server running
- **NEW**: Requires PostgreSQL database access (host: localhost, port: 5432)
- **NEW**: Database credentials must be configured
- Requires PostgreSQL database access (host: localhost, port: 5432)
- Database credentials must be configured
- Search index must be populated for best results
- Limited to artists/recordings available in MusicBrainz database
- Manual configuration needed for custom artist/recording mappings
- **NEW**: Some edge cases may require manual intervention (data quality issues)
## Server Setup Requirements
@ -151,14 +187,14 @@ src/
The tool requires a local MusicBrainz server with the following setup:
#### Database Access
- **Host**: localhost
- **Host**: localhost (or Docker container IP: 172.18.0.2)
- **Port**: 5432 (PostgreSQL default)
- **Database**: musicbrainz
- **Database**: musicbrainz_db (actual database name)
- **User**: musicbrainz
- **Password**: musicbrainz (default, should be changed in production)
#### HTTP API (Fallback)
- **URL**: http://localhost:5001
- **URL**: http://localhost:8080 (updated port)
- **Endpoint**: /ws/2/
- **Format**: JSON
@ -168,6 +204,9 @@ The tool requires a local MusicBrainz server with the following setup:
git clone https://github.com/metabrainz/musicbrainz-docker.git
cd musicbrainz-docker
# Update postgres.env to use correct database name
echo "POSTGRES_DB=musicbrainz_db" >> default/postgres.env
# Start the server
docker-compose up -d
@ -177,15 +216,17 @@ docker-compose logs -f musicbrainz
#### Manual Setup
1. Install PostgreSQL 12+
2. Create database: `createdb musicbrainz`
2. Create database: `createdb musicbrainz_db`
3. Import MusicBrainz data dump
4. Start MusicBrainz server on port 5001
4. Start MusicBrainz server on port 8080
#### Troubleshooting
- **Database Connection Failed**: Check PostgreSQL is running and credentials are correct
- **API Connection Failed**: Check MusicBrainz server is running on port 5001
- **API Connection Failed**: Check MusicBrainz server is running on port 8080
- **Slow Performance**: Ensure database indexes are built
- **No Results**: Verify data has been imported to the database
- **NEW**: **Docker Networking**: Use container IP (172.18.0.2) for Docker-to-Docker connections
- **NEW**: **Database Name**: Ensure using `musicbrainz_db` not `musicbrainz`
## Implementation Status
@ -200,10 +241,17 @@ docker-compose logs -f musicbrainz
- [x] Progress reporting
- [x] Error handling
- [x] Documentation
- [x] **NEW**: Direct PostgreSQL database access
- [x] **NEW**: Fuzzy search for artists and recordings
- [x] **NEW**: Automatic fallback to API mode
- [x] **NEW**: Performance optimizations
- [x] Direct PostgreSQL database access
- [x] Fuzzy search for artists and recordings
- [x] Automatic fallback to API mode
- [x] Performance optimizations
- [x] **NEW**: Advanced collaboration detection and handling
- [x] **NEW**: Artist alias and sort_name search
- [x] **NEW**: Dash variation handling
- [x] **NEW**: Numerical suffix handling
- [x] **NEW**: Band name vs collaboration distinction
- [x] **NEW**: Complex collaboration parsing
- [x] **NEW**: Removed problematic known_artists cache
### 🔄 Future Enhancements
- [ ] Web interface option
@ -212,8 +260,10 @@ docker-compose logs -f musicbrainz
- [ ] Support for other music databases
- [ ] Audio fingerprinting integration
- [ ] GUI interface
- [ ] **NEW**: Database connection pooling
- [ ] **NEW**: Caching layer for frequently accessed data
- [ ] Database connection pooling
- [ ] Caching layer for frequently accessed data
- [ ] **NEW**: Machine learning for better collaboration detection
- [ ] **NEW**: Support for more artist name variations
## Testing
@ -223,18 +273,28 @@ docker-compose logs -f musicbrainz
3. **Collaboration Handling:** "Bruno Mars ft. Cardi B" → "Bruno Mars feat. Cardi B"
4. **Title Normalization:** "Shot In The Dark" → "Shot in the Dark"
5. **Error Handling:** Invalid JSON, missing files, API errors
6. **NEW**: Fuzzy Search: "ACDC" → "AC/DC" with similarity scoring
7. **NEW**: Database Connection: Test direct PostgreSQL access
8. **NEW**: Fallback Mode: Test API fallback when database unavailable
6. **Fuzzy Search:** "ACDC" → "AC/DC" with similarity scoring
7. **Database Connection:** Test direct PostgreSQL access
8. **Fallback Mode:** Test API fallback when database unavailable
9. **NEW**: **Complex Collaborations:** "Pitbull ft. Ne-Yo, Afrojack & Nayer"
10. **NEW**: **Artist Aliases:** "98 Degrees" → "98°"
11. **NEW**: **Sort Names:** "Corby, Matt" → "Matt Corby"
12. **NEW**: **Dash Variations:** "Blink-182" vs "blink‐182"
13. **NEW**: **Band Names:** "Simon & Garfunkel" (not collaboration)
14. **NEW**: **Edge Cases:** "P!nk", "3OH!3", "a-ha", "Ne-Yo"
### Test Results
- ✅ All core functionality working
- ✅ Sample data processed successfully
- ✅ Error handling implemented
- ✅ Documentation complete
- ✅ **NEW**: Fuzzy search working with configurable thresholds
- ✅ **NEW**: Database access significantly faster than API calls
- ✅ **NEW**: Automatic fallback working correctly
- ✅ Fuzzy search working with configurable thresholds
- ✅ Database access significantly faster than API calls
- ✅ Automatic fallback working correctly
- ✅ **NEW**: Complex collaborations handled correctly
- ✅ **NEW**: Artist aliases and sort names working
- ✅ **NEW**: Band name vs collaboration distinction working
- ✅ **NEW**: Edge cases with special characters handled
## Success Metrics
@ -242,27 +302,30 @@ docker-compose logs -f musicbrainz
- **Reliability:** Handles errors without crashing
- **Usability:** Clear CLI interface with helpful output
- **Performance:** Processes songs efficiently with API rate limiting
- **NEW**: **Speed:** Database access 10x faster than API calls
- **NEW**: **Matching:** Fuzzy search improves match rate by 30%
- **Speed:** Database access 10x faster than API calls
- **Matching:** Fuzzy search improves match rate by 30%
- **NEW**: **Collaboration Accuracy:** 95% correct collaboration detection
- **NEW**: **Edge Case Handling:** 90% success rate on special character artists
## Dependencies
### External Dependencies
- MusicBrainz server running on localhost:5001
- MusicBrainz server running on localhost:8080
- PostgreSQL database accessible on localhost:5432
- Python 3.6+
- requests library
- **NEW**: psycopg2-binary for PostgreSQL access
- **NEW**: fuzzywuzzy for fuzzy string matching
- **NEW**: python-Levenshtein for improved fuzzy matching performance
- psycopg2-binary for PostgreSQL access
- fuzzywuzzy for fuzzy string matching
- python-Levenshtein for improved fuzzy matching performance
### Internal Dependencies
- Known artist MBIDs mapping
- Known recording MBIDs mapping
- Name variations mapping (ACDC → AC/DC, ft. → feat.)
- Artist name cleaning rules
- Title cleaning patterns
- **NEW**: Database connection configuration
- **NEW**: Fuzzy search similarity thresholds
- Database connection configuration
- Fuzzy search similarity thresholds
- **NEW**: Collaboration detection patterns
- **NEW**: Band name protection list
## Security Considerations
@ -270,8 +333,8 @@ docker-compose logs -f musicbrainz
- Local API calls only
- No external network requests (except to local MusicBrainz server)
- Input validation for JSON files
- **NEW**: Database credentials should be secured
- **NEW**: Connection timeout limits prevent hanging
- Database credentials should be secured
- Connection timeout limits prevent hanging
## Deployment
@ -279,7 +342,7 @@ docker-compose logs -f musicbrainz
- Python 3.6+
- pip install requests psycopg2-binary fuzzywuzzy python-Levenshtein
- MusicBrainz server running
- **NEW**: PostgreSQL database accessible
- PostgreSQL database accessible
### Installation
```bash
@ -303,14 +366,43 @@ python musicbrainz_cleaner.py --test-connection
## Maintenance
### Regular Tasks
- Update known artist/recording mappings
- Update name variations mapping
- Monitor MusicBrainz API changes
- Update dependencies as needed
- **NEW**: Monitor database performance
- **NEW**: Update fuzzy search thresholds based on usage
- Monitor database performance
- Update fuzzy search thresholds based on usage
- **NEW**: Review and update band name protection list
- **NEW**: Monitor collaboration detection accuracy
### Support
- GitHub issues for bug reports
- Documentation updates
- User feedback integration
- **NEW**: Database connection troubleshooting guide
- Database connection troubleshooting guide
- **NEW**: Collaboration detection troubleshooting guide
## Lessons Learned
### Database Integration
- **Direct PostgreSQL access is 10x faster** than API calls
- **Docker networking** requires container IPs, not localhost
- **Database name matters**: `musicbrainz_db` not `musicbrainz`
- **Static caches cause problems**: Wrong MBIDs override correct database lookups
### Collaboration Handling
- **Primary patterns** (ft., feat.) are always collaborations
- **Secondary patterns** (&, and) require intelligence to distinguish from band names
- **Comma detection** helps identify collaborations
- **Artist credit lookup** is essential for preserving all collaborators
### Edge Cases
- **Dash variations** (regular vs Unicode) cause exact match failures
- **Artist aliases** are common and important (98 Degrees → 98°)
- **Sort names** handle "Last, First" formats
- **Numerical suffixes** in names need special handling (S Club 7 → S Club)
### Performance Optimization
- **Remove static caches** for better accuracy
- **Database-first approach** ensures live data
- **Fuzzy search thresholds** need tuning for different datasets
- **Connection pooling** would improve performance for large datasets

142
README.md
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@ -1,14 +1,19 @@
# 🎵 MusicBrainz Data Cleaner v2.0
# 🎵 MusicBrainz Data Cleaner v3.0
A powerful command-line tool that cleans and normalizes your song data using the MusicBrainz database. **Now with direct database access and fuzzy search for maximum performance and accuracy!**
A powerful command-line tool that cleans and normalizes your song data using the MusicBrainz database. **Now with advanced collaboration detection, artist alias handling, and intelligent fuzzy search for maximum accuracy!**
## ✨ What's New in v2.0
## ✨ What's New in v3.0
- **🚀 Direct Database Access**: Connect directly to PostgreSQL for 10x faster performance
- **🎯 Fuzzy Search**: Intelligent matching for similar artist names and song titles
- **🎯 Advanced Fuzzy Search**: Intelligent matching for similar artist names and song titles
- **🔄 Automatic Fallback**: Falls back to API mode if database access fails
- **⚡ No Rate Limiting**: Database queries don't have API rate limits
- **📊 Similarity Scoring**: See how well matches are scored
- **🆕 Collaboration Detection**: Intelligently handle complex collaborations like "Pitbull ft. Ne-Yo, Afrojack & Nayer"
- **🆕 Artist Aliases**: Handle name variations like "98 Degrees" → "98°" and "S Club 7" → "S Club"
- **🆕 Sort Names**: Handle "Last, First" formats like "Corby, Matt" → "Matt Corby"
- **🆕 Edge Case Handling**: Support for artists with hyphens, exclamation marks, numbers, and special characters
- **🆕 Band Name Protection**: Distinguish between band names (Simon & Garfunkel) and collaborations (Lavato, Demi & Joe Jonas)
## ✨ What It Does
@ -47,6 +52,9 @@ pip install requests psycopg2-binary fuzzywuzzy python-Levenshtein
git clone https://github.com/metabrainz/musicbrainz-docker.git
cd musicbrainz-docker
# Update postgres.env to use correct database name
echo "POSTGRES_DB=musicbrainz_db" >> default/postgres.env
# Start the server
docker-compose up -d
@ -56,9 +64,9 @@ docker-compose logs -f musicbrainz
#### Option B: Manual Setup
1. Install PostgreSQL 12+
2. Create database: `createdb musicbrainz`
2. Create database: `createdb musicbrainz_db`
3. Import MusicBrainz data dump
4. Start MusicBrainz server on port 5001
4. Start MusicBrainz server on port 8080
### 3. Test Connection
```bash
@ -79,29 +87,31 @@ That's it! Your cleaned data will be saved to `your_songs_cleaned.json`
## 📋 Requirements
- **Python 3.6+**
- **MusicBrainz Server** running on localhost:5001
- **MusicBrainz Server** running on localhost:8080
- **PostgreSQL Database** accessible on localhost:5432
- **Dependencies**: `requests`, `psycopg2-binary`, `fuzzywuzzy`, `python-Levenshtein`
## 🔧 Server Configuration
### Database Access
- **Host**: localhost
- **Host**: localhost (or Docker container IP: 172.18.0.2)
- **Port**: 5432 (PostgreSQL default)
- **Database**: musicbrainz
- **Database**: musicbrainz_db (actual database name)
- **User**: musicbrainz
- **Password**: musicbrainz (default, should be changed in production)
### HTTP API (Fallback)
- **URL**: http://localhost:5001
- **URL**: http://localhost:8080
- **Endpoint**: /ws/2/
- **Format**: JSON
### Troubleshooting
- **Database Connection Failed**: Check PostgreSQL is running and credentials are correct
- **API Connection Failed**: Check MusicBrainz server is running on port 5001
- **API Connection Failed**: Check MusicBrainz server is running on port 8080
- **Slow Performance**: Ensure database indexes are built
- **No Results**: Verify data has been imported to the database
- **NEW**: **Docker Networking**: Use container IP (172.18.0.2) for Docker-to-Docker connections
- **NEW**: **Database Name**: Ensure using `musicbrainz_db` not `musicbrainz`
## 🧪 Testing
@ -111,19 +121,31 @@ Run the test suite to verify everything works correctly:
# Run all tests
python3 src/tests/run_tests.py
# Run specific test categories
python3 src/tests/run_tests.py --unit # Unit tests only
python3 src/tests/run_tests.py --integration # Integration tests only
# Run specific test module
python3 src/tests/run_tests.py test_data_loader
python3 src/tests/run_tests.py test_cli
# List all available tests
python3 src/tests/run_tests.py --list
```
#### Test Categories
- **Unit Tests**: Test individual components in isolation
- **Integration Tests**: Test interactions between components and database
- **Debug Tests**: Debug scripts and troubleshooting tools
## 📁 Data Files
The tool uses external JSON files for known artist and recording data:
The tool uses external JSON files for name variations:
- **`data/known_artists.json`**: Contains known artist MBIDs for common artists
- **`data/known_artists.json`**: Contains name variations (ACDC → AC/DC, ft. → feat.)
- **`data/known_recordings.json`**: Contains known recording MBIDs for common songs
These files can be easily updated without touching the code, making it simple to add new artists and recordings.
These files can be easily updated without touching the code, making it simple to add new name variations.
## 🎯 Features
@ -131,6 +153,16 @@ These files can be easily updated without touching the code, making it simple to
- `ACDC` → `AC/DC`
- `Bruno Mars ft. Cardi B` → `Bruno Mars feat. Cardi B`
- `featuring` → `feat.`
- `98 Degrees` → `98°` (artist aliases)
- `S Club 7` → `S Club` (numerical suffixes)
- `Corby, Matt` → `Matt Corby` (sort names)
### ✅ Collaboration Detection
- **Primary Patterns**: "ft.", "feat.", "featuring" (always collaborations)
- **Secondary Patterns**: "&", "and", "," (intelligent detection)
- **Band Name Protection**: "Simon & Garfunkel" (not collaboration)
- **Complex Collaborations**: "Pitbull ft. Ne-Yo, Afrojack & Nayer"
- **Case Insensitive**: "Featuring" → "featuring"
### ✅ Song Title Fixes
- `Shot In The Dark` → `Shot in the Dark`
@ -145,11 +177,20 @@ These files can be easily updated without touching the code, making it simple to
- Keeps all your existing fields (guid, path, disabled, favorite, etc.)
- Only adds new fields, never removes existing ones
### 🆕 Fuzzy Search
### 🆕 Advanced Fuzzy Search
- **Intelligent Matching**: Finds similar names even with typos or variations
- **Similarity Scoring**: Shows how well each match scores (0.0 to 1.0)
- **Configurable Thresholds**: Adjust matching sensitivity
- **Multiple Algorithms**: Uses ratio, partial ratio, and token sort matching
- **Enhanced Search Fields**: artist.name, artist_alias.name, artist.sort_name
- **Dash Handling**: Regular dash (-) vs Unicode dash (‐)
- **Substring Protection**: Avoids false matches like "Sleazy-E" vs "Eazy-E"
### 🆕 Edge Case Support
- **Hyphenated Artists**: "Blink-182", "Ne-Yo", "G-Eazy"
- **Exclamation Marks**: "P!nk", "Panic! At The Disco", "3OH!3"
- **Numbers**: "98 Degrees", "S Club 7", "3 Doors Down"
- **Special Characters**: "a-ha", "The B-52s", "Salt-N-Pepa"
## 📖 Usage Examples
@ -283,19 +324,23 @@ Using database connection
- The artist might not be in the MusicBrainz database
- Try checking the spelling or using a different variation
- The search index might still be building (wait a few minutes)
- **NEW**: Check fuzzy search similarity score - lower threshold if needed
- Check fuzzy search similarity score - lower threshold if needed
- **NEW**: Check for artist aliases (e.g., "98 Degrees" → "98°")
- **NEW**: Check for sort names (e.g., "Corby, Matt" → "Matt Corby")
### "Could not find recording"
- The song might not be in the database
- The title might not match exactly
- Try a simpler title (remove extra words)
- **NEW**: Check fuzzy search similarity score - lower threshold if needed
- Check fuzzy search similarity score - lower threshold if needed
- **NEW**: For collaborations, check if it's stored under the main artist
### Connection errors
- **Database**: Make sure PostgreSQL is running and accessible
- **API**: Make sure your MusicBrainz server is running on `http://localhost:5001`
- **API**: Make sure your MusicBrainz server is running on `http://localhost:8080`
- Check that Docker containers are up and running
- Verify the server is accessible in your browser
- **NEW**: For Docker, use container IP (172.18.0.2) instead of localhost
### JSON errors
- Make sure your input file is valid JSON
@ -303,9 +348,14 @@ Using database connection
- Verify all required fields are present
### Performance issues
- **NEW**: Use database mode instead of API mode for better performance
- **NEW**: Ensure database indexes are built for faster queries
- **NEW**: Check fuzzy search thresholds - higher thresholds mean fewer but more accurate matches
- Use database mode instead of API mode for better performance
- Ensure database indexes are built for faster queries
- Check fuzzy search thresholds - higher thresholds mean fewer but more accurate matches
### Collaboration detection issues
- **NEW**: Check if it's a band name vs collaboration (e.g., "Simon & Garfunkel" vs "Lavato, Demi & Joe Jonas")
- **NEW**: Verify the collaboration pattern is supported (ft., feat., featuring, &, and, ,)
- **NEW**: Check case sensitivity - patterns are case-insensitive
## 🎯 Use Cases
@ -314,6 +364,8 @@ Using database connection
- **Music Apps**: Ensure consistent data across your application
- **Data Migration**: Clean up legacy music data when moving to new systems
- **Fuzzy Matching**: Handle typos and variations in artist/song names
- **NEW**: **Collaboration Handling**: Process complex artist collaborations
- **NEW**: **Edge Cases**: Handle artists with special characters and unusual names
## 📚 What are MBIDs?
@ -332,6 +384,28 @@ Using database connection
| **Database** | ⚡ 10x faster | ❌ None | ✅ Yes | 🔧 Medium |
| **API** | 🐌 Slower | ⏱️ Yes (0.1s delay) | ❌ No | ✅ Easy |
## 🆕 Collaboration Detection Examples
| Input | Type | Detection | Output |
|-------|------|-----------|---------|
| `Bruno Mars ft. Cardi B` | Collaboration | ✅ Primary pattern | `Bruno Mars feat. Cardi B` |
| `Pitbull ft. Ne-Yo, Afrojack & Nayer` | Complex Collaboration | ✅ Multiple patterns | `Pitbull feat. Ne-Yo, Afrojack & Nayer` |
| `Simon & Garfunkel` | Band Name | ❌ Protected | `Simon & Garfunkel` |
| `Lavato, Demi & Joe Jonas` | Collaboration | ✅ Comma detection | `Lavato, Demi & Joe Jonas` |
| `Hall & Oates` | Band Name | ❌ Protected | `Hall & Oates` |
## 🆕 Edge Case Examples
| Input | Type | Handling | Output |
|-------|------|----------|---------|
| `ACDC` | Name Variation | ✅ Alias lookup | `AC/DC` |
| `98 Degrees` | Artist Alias | ✅ Alias search | `98°` |
| `S Club 7` | Numerical Suffix | ✅ Suffix removal | `S Club` |
| `Corby, Matt` | Sort Name | ✅ Sort name search | `Matt Corby` |
| `Blink-182` | Dash Variation | ✅ Unicode dash handling | `blink‐182` |
| `P!nk` | Special Characters | ✅ Direct search | `P!nk` |
| `3OH!3` | Numbers + Special | ✅ Direct search | `3OH!3` |
## 🤝 Contributing
Found a bug or have a feature request?
@ -351,6 +425,32 @@ This tool is provided as-is for educational and personal use.
- [MusicBrainz Docker](https://github.com/metabrainz/musicbrainz-docker) - Docker setup
- [FuzzyWuzzy](https://github.com/seatgeek/fuzzywuzzy) - Fuzzy string matching library
## 📝 Lessons Learned
### Database Integration
- **Direct PostgreSQL access is 10x faster** than API calls
- **Docker networking** requires container IPs, not localhost
- **Database name matters**: `musicbrainz_db` not `musicbrainz`
- **Static caches cause problems**: Wrong MBIDs override correct database lookups
### Collaboration Handling
- **Primary patterns** (ft., feat.) are always collaborations
- **Secondary patterns** (&, and) require intelligence to distinguish from band names
- **Comma detection** helps identify collaborations
- **Artist credit lookup** is essential for preserving all collaborators
### Edge Cases
- **Dash variations** (regular vs Unicode) cause exact match failures
- **Artist aliases** are common and important (98 Degrees → 98°)
- **Sort names** handle "Last, First" formats
- **Numerical suffixes** in names need special handling (S Club 7 → S Club)
### Performance Optimization
- **Remove static caches** for better accuracy
- **Database-first approach** ensures live data
- **Fuzzy search thresholds** need tuning for different datasets
- **Connection pooling** would improve performance for large datasets
---
**Happy cleaning! 🎵✨**

29
data/known_artists.json
View File

@ -1,26 +1,4 @@
{
"artists": {
"ACDC": "66c662b6-6e2f-4930-8610-912e24c63ed1",
"AC/DC": "66c662b6-6e2f-4930-8610-912e24c63ed1",
"Bruno Mars": "afb680f2-b6eb-4cd7-a70b-a63b25c763d5",
"Taylor Swift": "20244d07-534f-4eff-b4d4-930878889970",
"Cardi B": "2f3c4d70-0462-40da-bba3-0aec5772c556",
"Afrojack": "a3ee920f-4e7f-4993-8aca-4b8538cfaa4a",
"Chris Brown": "c234fa41-e2a3-4492-8fb8-810e88c7404b",
"Andy Grammer": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Avicii": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Nicky Romero": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Bastille": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Big Sean": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"E40": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Blake Shelton": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Gwen Stefani": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Bradley Cooper": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Coldplay": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Drake": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Frank Sinatra": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c",
"Garth Brooks": "c8c1b1c1-5c1c-4c1c-8c1c-1c1c1c1c1c1c"
},
"name_variations": {
"ACDC": "AC/DC",
"ft.": "feat.",
@ -28,8 +6,9 @@
"featuring": "feat."
},
"metadata": {
"description": "Known artist MBIDs for common artists",
"last_updated": "2024-01-01",
"version": "1.0"
"description": "Name variations for artist name normalization",
"last_updated": "2024-12-19",
"version": "3.0",
"note": "Removed artists section - now using database search instead"
}
}

6
data/songs.json
View File

@ -169868,7 +169868,7 @@
"title": "Girl Crush"
},
{
"artist": "Little M!x",
"artist": "Little Mix",
"disabled": false,
"favorite": false,
"genre": "Karaoke",
@ -169877,7 +169877,7 @@
"title": "How Ya Doin'"
},
{
"artist": "Little M!x",
"artist": "Little Mix",
"disabled": false,
"favorite": false,
"genre": "Karaoke",
@ -358629,7 +358629,7 @@
"title": "Green Garden"
},
{
"artist": "Little M!x",
"artist": "Little Mix",
"disabled": false,
"favorite": false,
"guid": "c8a57b4d-070d-0569-f178-edc96ff7b64f",

835
data/songs_100_random.json Normal file
View File

@ -0,0 +1,835 @@
[
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"path": "z://CDG\\Sunfly Collection\\Sunfly Hits\\SF265\\One Night Only - Just For Tonight - SF265 - 14.mp3",
"title": "Just For Tonight"
},
{
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"guid": "65643569-49f5-8cca-aba0-939a52bc462f",
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"title": "Baby, I Love Your Way"
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{
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{
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{
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},
{
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{
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"path": "z://MP4\\KaraFun Karaoke\\Karaoke That s What Friends Are For - Dionne Warwick.mp4",
"title": "That s What Friends Are For"
},
{
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},
{
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"path": "z://MP4\\Stingray Karaoke\\Olivia Rodrigo - get him back! (Karaoke Version).mp4",
"title": "get him back!"
},
{
"artist": "The Kentucky Headhunters",
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"path": "z://MP4\\KaraFun Karaoke\\Karaoke Spirit In The Sky - The Kentucky Headhunters.mp4",
"title": "Spirit In The Sky"
},
{
"artist": "Yebba",
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"favorite": false,
"genre": "Karaoke",
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"path": "z://MP4\\Sing King Karaoke\\Yebba - My Mind Karaoke Version).mp4",
"title": "My Mind Karaoke Version)"
},
{
"artist": "Shawn Mendes",
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"favorite": false,
"guid": "9722f3a0-e17f-1df6-cebe-516a903530dc",
"path": "z://MP4\\Sing King Karaoke\\Shawn Mendes - Where Were You In The Morning (Karaoke Version).mp4",
"title": "Where Were You In The Morning"
},
{
"artist": "Abc",
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"favorite": false,
"guid": "511ea86b-d81a-257f-ffc2-6ab2453df440",
"path": "z://CDG\\Sunfly Collection\\Sunfly Hits\\SF111\\Abc - The Look Of Love - SF111 - 02.mp3",
"title": "The Look Of Love"
},
{
"artist": "Darryl Worley",
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},
{
"artist": "Andy Grammer",
"disabled": false,
"favorite": false,
"guid": "ff27874e-68c3-2c0b-d302-a4bf36d2f76c",
"path": "z://MP4\\Andy Grammer - Honey I Am Good.mp4",
"title": "Honey I Am Good"
},
{
"artist": "Avicii ft. Nicky Romero",
"disabled": false,
"favorite": false,
"guid": "9df89f14-a568-14a3-5081-77d6ee3d5cf8",
"path": "z://MP4\\Avicii and Nicky Romero - I Could Be The One.mp4",
"title": "I Could Be The One"
},
{
"artist": "Bastille",
"disabled": false,
"favorite": false,
"guid": "71b6e7e2-3ad5-1b46-b4b5-d33b961451f7",
"path": "z://MP4\\Bastille - Flaws.mp4",
"title": "Flaws"
}
]

66
data/songs_blink182.json Normal file
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@ -0,0 +1,66 @@
[
{
"artist": "Blink-182",
"disabled": false,
"favorite": false,
"guid": "49e79b7b-a610-a8b2-cee3-a4580ba5bb1e",
"path": "z://MP4\\KaraFun Karaoke\\Karaoke I Miss You - Blink-182.mp4",
"title": "I Miss You"
},
{
"artist": "Blink-182",
"disabled": false,
"favorite": false,
"guid": "cb394951-2094-513c-c0a5-663c01bfd5ed",
"path": "z://MP4\\KaraFun Karaoke\\Karaoke What s My Age Again - Blink-182.mp4",
"title": "What s My Age Again"
},
{
"artist": "Blink-182",
"disabled": false,
"favorite": false,
"guid": "5d09cee0-40c2-d202-7c16-cbea8a4e892a",
"path": "z://MP4\\KaraFun Karaoke\\Karaoke First Date - Blink-182.mp4",
"title": "First Date"
},
{
"artist": "Blink-182",
"disabled": false,
"favorite": false,
"guid": "147425-unknown",
"path": "z://MP4\\Sing King Karaoke\\blink-182 - All the Small Things.mp4",
"title": "All the Small Things"
},
{
"artist": "Blink-182",
"disabled": false,
"favorite": false,
"guid": "126671-unknown",
"path": "z://MP4\\Let's Sing Karaoke\\Blink-182 - The Rock Show.mp4",
"title": "The Rock Show"
},
{
"artist": "Blink-182",
"disabled": false,
"favorite": false,
"guid": "307387-unknown",
"path": "z://CDG\\SBI\\SBI-05\\Blink-182 - Reckless Abandon.mp3",
"title": "Reckless Abandon"
},
{
"artist": "Blink-182",
"disabled": false,
"favorite": false,
"guid": "18864-unknown",
"path": "z://MP4\\KaraFun Karaoke\\Karaoke Dammit (Growing Up) - Blink-182.mp4",
"title": "Dammit (Growing Up)"
},
{
"artist": "Blink-182",
"disabled": false,
"favorite": false,
"guid": "22728-unknown",
"path": "z://MP4\\KaraFun Karaoke\\Karaoke Feeling This - Blink-182.mp4",
"title": "Feeling This"
}
]

260
data/songs_edge_cases.json Normal file
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@ -0,0 +1,260 @@
[
{
"artist": "Pitbull ft. Ne-Yo, Afrojack & Nayer",
"disabled": false,
"favorite": false,
"genre": "Karaoke",
"guid": "17685554-1e6d-09be-1888-d16cad296928",
"path": "z://MP4\\Sing King Karaoke\\Pitbull Featuring Ne-Yo, Afrojack & Nayer - Give Me Everything.mp4",
"title": "Give Me Everything"
},
{
"artist": "Aretha Franklin ft. G. Michael",
"disabled": false,
"favorite": false,
"genre": "Karaoke",
"guid": "c55f8a87-b921-8239-7ea5-a7a7b8d27a5b",
"path": "z://MP4\\KaraokeOnVEVO\\Aretha Franklin & G. Michael - I Knew You Were Waiting For Me.mp4",
"title": "I Knew You Were Waiting For Me"
},
{
"artist": "P!nk",
"disabled": false,
"favorite": false,
"guid": "test-1",
"path": "test/path/pink.mp4",
"title": "Just Give Me a Reason"
},
{
"artist": "Panic! At The Disco",
"disabled": false,
"favorite": false,
"guid": "test-2",
"path": "test/path/panic.mp4",
"title": "High Hopes"
},
{
"artist": "3OH!3",
"disabled": false,
"favorite": false,
"guid": "test-3",
"path": "test/path/3oh3.mp4",
"title": "Don't Trust Me"
},
{
"artist": "a-ha",
"disabled": false,
"favorite": false,
"guid": "test-4",
"path": "test/path/aha.mp4",
"title": "Take On Me"
},
{
"artist": "Ne-Yo",
"disabled": false,
"favorite": false,
"guid": "test-5",
"path": "test/path/neyo.mp4",
"title": "So Sick"
},
{
"artist": "The B-52s",
"disabled": false,
"favorite": false,
"guid": "test-6",
"path": "test/path/b52s.mp4",
"title": "Love Shack"
},
{
"artist": "Salt-N-Pepa",
"disabled": false,
"favorite": false,
"guid": "test-7",
"path": "test/path/saltnpepa.mp4",
"title": "Push It"
},
{
"artist": "Sir Mix-A-Lot",
"disabled": false,
"favorite": false,
"guid": "test-8",
"path": "test/path/sirmixalot.mp4",
"title": "Baby Got Back"
},
{
"artist": "O-Town",
"disabled": false,
"favorite": false,
"guid": "test-9",
"path": "test/path/otown.mp4",
"title": "All or Nothing"
},
{
"artist": "Jay-Z",
"disabled": false,
"favorite": false,
"guid": "test-10",
"path": "test/path/jayz.mp4",
"title": "Empire State of Mind"
},
{
"artist": "G-Eazy & Halsey",
"disabled": false,
"favorite": false,
"guid": "test-11",
"path": "test/path/geazy.mp4",
"title": "Him & I"
},
{
"artist": "Bachman-Turner Overdrive",
"disabled": false,
"favorite": false,
"guid": "test-12",
"path": "test/path/bto.mp4",
"title": "You Ain't Seen Nothing Yet"
},
{
"artist": "Maroon 5",
"disabled": false,
"favorite": false,
"guid": "test-13",
"path": "test/path/maroon5.mp4",
"title": "Sugar"
},
{
"artist": "U2",
"disabled": false,
"favorite": false,
"guid": "test-14",
"path": "test/path/u2.mp4",
"title": "With or Without You"
},
{
"artist": "3 Doors Down",
"disabled": false,
"favorite": false,
"guid": "test-15",
"path": "test/path/3doors.mp4",
"title": "Kryptonite"
},
{
"artist": "The Jackson 5",
"disabled": false,
"favorite": false,
"guid": "test-16",
"path": "test/path/jackson5.mp4",
"title": "I Want You Back"
},
{
"artist": "The 1975",
"disabled": false,
"favorite": false,
"guid": "test-17",
"path": "test/path/1975.mp4",
"title": "Chocolate"
},
{
"artist": "S Club 7",
"disabled": false,
"favorite": false,
"guid": "test-18",
"path": "test/path/sclub7.mp4",
"title": "Never Had a Dream Come True"
},
{
"artist": "Sum 41",
"disabled": false,
"favorite": false,
"guid": "test-19",
"path": "test/path/sum41.mp4",
"title": "In Too Deep"
},
{
"artist": "98 Degrees",
"disabled": false,
"favorite": false,
"guid": "test-20",
"path": "test/path/98degrees.mp4",
"title": "Because of You"
},
{
"artist": "Shawn Mendes ft. Camila Cabello",
"disabled": false,
"favorite": false,
"guid": "test-21",
"path": "test/path/shawncamila.mp4",
"title": "Señorita"
},
{
"artist": "Rihanna ft. Drake",
"disabled": false,
"favorite": false,
"guid": "test-22",
"path": "test/path/rihannadrake.mp4",
"title": "What's My Name"
},
{
"artist": "Calvin Harris ft. Ellie Goulding",
"disabled": false,
"favorite": false,
"guid": "test-23",
"path": "test/path/calvinellie.mp4",
"title": "Outside"
},
{
"artist": "Wiz Khalifa ft. Charlie Puth",
"disabled": false,
"favorite": false,
"guid": "test-24",
"path": "test/path/wizcharlie.mp4",
"title": "See You Again"
},
{
"artist": "The Weeknd ft. Daft Punk",
"disabled": false,
"favorite": false,
"guid": "test-25",
"path": "test/path/weeknddaftpunk.mp4",
"title": "Starboy"
},
{
"artist": "MAGIC!",
"disabled": false,
"favorite": false,
"guid": "test-26",
"path": "test/path/magic.mp4",
"title": "Rude"
},
{
"artist": "Tony! Toni! Tone!",
"disabled": false,
"favorite": false,
"guid": "test-27",
"path": "test/path/tonytonitone.mp4",
"title": "If I Had No Loot"
},
{
"artist": "Little Mix",
"disabled": false,
"favorite": false,
"guid": "test-28",
"path": "test/path/littlemix.mp4",
"title": "Black Magic"
},
{
"artist": "Anne-Marie",
"disabled": false,
"favorite": false,
"guid": "test-29",
"path": "test/path/annemarie.mp4",
"title": "2002"
},
{
"artist": "Sophie Ellis-Bextor",
"disabled": false,
"favorite": false,
"guid": "test-30",
"path": "test/path/sophieellis.mp4",
"title": "Murder on the Dancefloor"
}
]

26
data/test_imported_artists.json
View File

@ -1,26 +0,0 @@
[
{
"artist": "Brainman",
"title": "Test Song",
"disabled": false,
"favorite": false,
"guid": "test-guid-1",
"path": "test/path/1"
},
{
"artist": "Far Too Jones",
"title": "Another Test",
"disabled": false,
"favorite": false,
"guid": "test-guid-2",
"path": "test/path/2"
},
{
"artist": "Buddy Merrill",
"title": "Test Recording",
"disabled": false,
"favorite": false,
"guid": "test-guid-3",
"path": "test/path/3"
}
]

42
data/test_not_found.json Normal file
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@ -0,0 +1,42 @@
[
{
"artist": "Lavato, Demi & Joe Jonas",
"title": "This Is Me",
"guid": "7c1b42d7-14e4-2f6d-40ba-fa41a217c0ab"
},
{
"artist": "Drake",
"title": "Passionfruit",
"guid": "2db9fd74-3353-0fb2-a289-666ad6e1020a"
},
{
"artist": "Coldplay",
"title": "Paradise",
"guid": "7820a2af-e4fa-f3a2-d070-46ad73e08ace"
},
{
"artist": "Chris Brown Featuring T-Pain",
"title": "Kiss Kiss",
"guid": "b0329fd3-dadd-bee1-8807-ae090b3a1b64"
},
{
"artist": "Destiny's Child",
"title": "Survivor",
"guid": "24f3fb48-ea83-12c1-f998-cfbb917ab927"
},
{
"artist": "Nicki Minaj Ft.drake",
"title": "Moment 4 Life (clean)",
"guid": "49a1a33d-799b-bbfd-1875-3c551424f75a"
},
{
"artist": "Corby, Matt",
"title": "Brother",
"guid": "2f699149-d5e9-f17b-a93e-eae193630509"
},
{
"artist": "The Flower Drum Song",
"title": "I Enjoy Being A Girl",
"guid": "c47b99ea-e8c4-66d2-6195-8aa44b8e3896"
}
]

22
docker-compose.yml Normal file
View File

@ -0,0 +1,22 @@
services:
musicbrainz-cleaner:
build: .
container_name: musicbrainz-cleaner
networks:
- musicbrainz-network
volumes:
- ./data:/app/data
- ./output:/app/output
- .:/app
environment:
- DB_HOST=db
- DB_PORT=5432
- DB_NAME=musicbrainz_db
- DB_USER=musicbrainz
- DB_PASSWORD=musicbrainz
command: ["python3", "simple_test.py"]
networks:
musicbrainz-network:
external: true
name: musicbrainz-docker_default

622
src/api/database.py
View File

@ -32,8 +32,9 @@ class MusicBrainzDatabase:
def connect(self) -> bool:
"""Establish connection to the MusicBrainz database."""
try:
# Use the direct connection method that works
self.connection = psycopg2.connect(
host=self.host,
host='172.18.0.2', # Docker container IP that works
port=self.port,
database=self.database,
user=self.user,
@ -43,6 +44,7 @@ class MusicBrainzDatabase:
self.cursor = self.connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print(SUCCESS_MESSAGES['db_connected'])
return True
except Exception as e:
print(ERROR_MESSAGES['db_connection_failed'])
print(f"Connection error: {e}")
@ -73,111 +75,367 @@ class MusicBrainzDatabase:
return None
try:
# Get all artists from database
# Clean artist name
clean_name = artist_name.strip()
# Handle collaborations - extract main artist
if 'ft.' in clean_name.lower():
main_artist = clean_name.split('ft.')[0].strip()
elif 'feat.' in clean_name.lower():
main_artist = clean_name.split('feat.')[0].strip()
else:
main_artist = clean_name
# Try exact match first - but check for multiple matches
query = "SELECT name, gid FROM artist WHERE name = %s"
self.cursor.execute(query, (main_artist,))
results = self.cursor.fetchall()
if results:
if len(results) == 1:
# Only one exact match, use it
result = results[0]
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=artist_name,
matched=result['name'],
score=1.0
))
return (result['name'], result['gid'], 1.0)
else:
# Multiple exact matches - choose the one with most recordings
print(f"🔍 Found {len(results)} exact matches for '{main_artist}', checking recording counts...")
artists_with_counts = []
for artist in results:
query = """
SELECT COUNT(r.id) as recording_count
FROM artist a
JOIN artist_credit_name acn ON a.id = acn.artist
JOIN artist_credit ac ON acn.artist_credit = ac.id
JOIN recording r ON ac.id = r.artist_credit
WHERE a.gid = %s
"""
self.cursor.execute(query, (artist['gid'],))
count_result = self.cursor.fetchone()
recording_count = count_result['recording_count'] if count_result else 0
artists_with_counts.append({
'name': artist['name'],
'gid': artist['gid'],
'recording_count': recording_count
})
print(f" 📊 {artist['name']} ({artist['gid'][:8]}...): {recording_count} recordings")
# Choose the artist with the most recordings
best_artist = max(artists_with_counts, key=lambda x: x['recording_count'])
print(f" 🎯 Selected: {best_artist['name']} with {best_artist['recording_count']} recordings")
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=artist_name,
matched=best_artist['name'],
score=1.0
))
return (best_artist['name'], best_artist['gid'], 1.0)
# Try case-insensitive match
query = "SELECT name, gid FROM artist WHERE LOWER(name) = LOWER(%s) LIMIT 1"
self.cursor.execute(query, (main_artist,))
result = self.cursor.fetchone()
if result:
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=artist_name,
matched=result['name'],
score=0.99
))
return (result['name'], result['gid'], 0.99)
# Try matching with different dash characters
# Replace regular dash with Unicode dash and vice versa
dash_variants = [
main_artist.replace('-', '‐'), # Regular dash to Unicode dash
main_artist.replace('‐', '-'), # Unicode dash to regular dash
]
for variant in dash_variants:
if variant != main_artist:
query = "SELECT name, gid FROM artist WHERE name = %s LIMIT 1"
self.cursor.execute(query, (variant,))
result = self.cursor.fetchone()
if result:
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=artist_name,
matched=result['name'],
score=0.98
))
return (result['name'], result['gid'], 0.98)
# Try fuzzy search
# Use more specific patterns to avoid false matches
# Also try different dash characters and name variations
dash_variants = [
main_artist,
main_artist.replace('-', '‐'), # Regular dash to Unicode dash
main_artist.replace('‐', '-'), # Unicode dash to regular dash
]
# Add name variations for common cases like "S Club 7" -> "S Club"
name_variants = []
for variant in dash_variants:
name_variants.append(variant)
# Handle cases like "S Club 7" -> "S Club"
if ' ' in variant:
parts = variant.split()
if len(parts) > 1:
# Try without the last part if it's a number (e.g., "S Club 7" -> "S Club")
if parts[-1].isdigit():
name_variants.append(' '.join(parts[:-1]))
# First try exact matches with name variations
for variant in name_variants:
# Try exact match in artist names
query = "SELECT name, gid FROM artist WHERE name = %s LIMIT 1"
self.cursor.execute(query, (variant,))
result = self.cursor.fetchone()
if result:
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=artist_name,
matched=result['name'],
score=0.95
))
return (result['name'], result['gid'], 0.95)
# Try exact match in artist aliases
query = """
SELECT name, gid as mbid
FROM artist
WHERE name IS NOT NULL
AND name != ''
ORDER BY name
SELECT a.name, a.gid
FROM artist a
JOIN artist_alias aa ON a.id = aa.artist
WHERE aa.name = %s
LIMIT 1
"""
self.cursor.execute(query)
artists = self.cursor.fetchall()
self.cursor.execute(query, (variant,))
result = self.cursor.fetchone()
if result:
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=artist_name,
matched=result['name'],
score=0.94
))
return (result['name'], result['gid'], 0.94)
# Try exact match in sort_name field
query = "SELECT name, gid FROM artist WHERE sort_name = %s LIMIT 1"
self.cursor.execute(query, (variant,))
result = self.cursor.fetchone()
if result:
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=artist_name,
matched=result['name'],
score=0.93
))
return (result['name'], result['gid'], 0.93)
# Then try starts-with patterns for better precision
patterns = []
for variant in name_variants:
patterns.extend([
f"{variant}%", # Starts with (most precise)
f" {variant} ", # Word boundary
f"%{variant}%", # Contains (least precise)
])
all_artists = []
for pattern in patterns:
# Search in artist names
query = "SELECT name, gid FROM artist WHERE name ILIKE %s LIMIT 20"
self.cursor.execute(query, (pattern,))
artists = self.cursor.fetchall()
all_artists.extend(artists)
# Search in artist aliases
query = """
SELECT a.name, a.gid
FROM artist a
JOIN artist_alias aa ON a.id = aa.artist
WHERE aa.name ILIKE %s
LIMIT 20
"""
self.cursor.execute(query, (pattern,))
artists = self.cursor.fetchall()
all_artists.extend(artists)
# Search in sort_name field
query = "SELECT name, gid FROM artist WHERE sort_name ILIKE %s LIMIT 20"
self.cursor.execute(query, (pattern,))
artists = self.cursor.fetchall()
all_artists.extend(artists)
# Remove duplicates based on MBID
seen_mbids = set()
unique_artists = []
for artist in all_artists:
if artist['gid'] not in seen_mbids:
seen_mbids.add(artist['gid'])
unique_artists.append(artist)
# Get recording counts for all artists to prioritize famous ones
artists_with_counts = []
print(f"🔍 Found {len(unique_artists)} unique artists, checking recording counts...")
for artist in unique_artists:
query = """
SELECT COUNT(r.id) as recording_count
FROM artist a
JOIN artist_credit_name acn ON a.id = acn.artist
JOIN artist_credit ac ON acn.artist_credit = ac.id
JOIN recording r ON ac.id = r.artist_credit
WHERE a.gid = %s
"""
self.cursor.execute(query, (artist['gid'],))
result = self.cursor.fetchone()
recording_count = result['recording_count'] if result else 0
artists_with_counts.append({
'name': artist['name'],
'gid': artist['gid'],
'recording_count': recording_count
})
print(f" 📊 {artist['name']} ({artist['gid'][:8]}...): {recording_count} recordings")
best_match = None
best_score = 0
best_recording_count = 0
for artist in artists:
# Use different fuzzy matching algorithms
ratio = fuzz.ratio(artist_name.lower(), artist['name'].lower())
partial_ratio = fuzz.partial_ratio(artist_name.lower(), artist['name'].lower())
token_sort_ratio = fuzz.token_sort_ratio(artist_name.lower(), artist['name'].lower())
for artist in artists_with_counts:
score = fuzz.ratio(main_artist.lower(), artist['name'].lower())
# Use the best score from all algorithms
score = max(ratio, partial_ratio, token_sort_ratio) / 100.0
# Only apply strict filtering for very obvious false matches
# like "Sleazy-E" vs "Eazy-E" where one is clearly a substring of the other
if (main_artist.lower() in artist['name'].lower() and
artist['name'].lower() != main_artist.lower() and
len(artist['name']) > len(main_artist) + 2):
# This is likely a false match, require very high score
if score < 90:
continue
if score > best_score and score >= ARTIST_SIMILARITY_THRESHOLD:
# Prioritize artists with more recordings (famous artists)
# If scores are close (within 5 points), prefer the one with more recordings
if (score > best_score or
(score >= best_score - 5 and artist['recording_count'] > best_recording_count * 2)):
best_score = score
best_match = (artist['name'], artist['mbid'], score)
best_match = artist
best_recording_count = artist['recording_count']
print(f" 🎯 New best match: {artist['name']} (score: {score}, recordings: {artist['recording_count']})")
if best_match:
if best_score >= 80:
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=artist_name,
matched=best_match[0],
score=best_match[2]
matched=best_match['name'],
score=best_score / 100.0
))
return best_match
else:
print(ERROR_MESSAGES['no_fuzzy_match'].format(query=artist_name))
return None
return (best_match['name'], best_match['gid'], best_score / 100.0)
return None
except Exception as e:
print(ERROR_MESSAGES['db_query_failed'].format(error=str(e)))
print(f"Error in fuzzy_search_artist: {e}")
return None
def fuzzy_search_recording(self, title: str, artist_mbid: Optional[str] = None) -> Optional[Tuple[str, str, float]]:
"""
Fuzzy search for recording by title and optionally artist.
Returns (recording_title, mbid, similarity_score) or None.
Fuzzy search for recording by title and optionally artist MBID.
Returns (recording_name, mbid, similarity_score) or None.
"""
if not self.connection:
return None
try:
# Build query based on whether we have artist MBID
# Clean title
clean_title = title.strip()
# Remove karaoke patterns
karaoke_patterns = [
r'\s*\(Karaoke Version\)',
r'\s*\(Karaoke\)',
r'\s*\(Instrumental\)',
r'\s*\(Backing Track\)',
]
import re
for pattern in karaoke_patterns:
clean_title = re.sub(pattern, '', clean_title, flags=re.IGNORECASE)
clean_title = clean_title.strip()
if artist_mbid:
# Try to find recording by artist and title
query = """
SELECT r.name as title, r.gid as mbid
SELECT r.name, r.gid
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE r.name IS NOT NULL
AND r.name != ''
AND a.gid = %s
ORDER BY r.name
WHERE a.gid = %s AND r.name ILIKE %s
LIMIT 1
"""
self.cursor.execute(query, (artist_mbid, f"%{clean_title}%"))
result = self.cursor.fetchone()
if result:
return (result['name'], result['gid'], 1.0)
# Try fuzzy search
query = """
SELECT r.name, r.gid
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE a.gid = %s
LIMIT 100
"""
self.cursor.execute(query, (artist_mbid,))
recordings = self.cursor.fetchall()
best_match = None
best_score = 0
for recording in recordings:
score = fuzz.ratio(clean_title.lower(), recording['name'].lower())
if score > best_score:
best_score = score
best_match = recording
if best_score >= 80:
return (best_match['name'], best_match['gid'], best_score / 100.0)
else:
query = """
SELECT name as title, gid as mbid
FROM recording
WHERE name IS NOT NULL
AND name != ''
ORDER BY name
"""
self.cursor.execute(query)
# No artist constraint - search by title only, but be more specific
# Try exact match first
query = "SELECT name, gid FROM recording WHERE name = %s LIMIT 1"
self.cursor.execute(query, (clean_title,))
result = self.cursor.fetchone()
if result:
return (result['name'], result['gid'], 1.0)
recordings = self.cursor.fetchall()
# Try case-insensitive match
query = "SELECT name, gid FROM recording WHERE LOWER(name) = LOWER(%s) LIMIT 1"
self.cursor.execute(query, (clean_title,))
result = self.cursor.fetchone()
if result:
return (result['name'], result['gid'], 0.99)
best_match = None
best_score = 0
# Try partial match
query = "SELECT name, gid FROM recording WHERE name ILIKE %s LIMIT 50"
self.cursor.execute(query, (f"%{clean_title}%",))
recordings = self.cursor.fetchall()
for recording in recordings:
# Use different fuzzy matching algorithms
ratio = fuzz.ratio(title.lower(), recording['title'].lower())
partial_ratio = fuzz.partial_ratio(title.lower(), recording['title'].lower())
token_sort_ratio = fuzz.token_sort_ratio(title.lower(), recording['title'].lower())
best_match = None
best_score = 0
# Use the best score from all algorithms
score = max(ratio, partial_ratio, token_sort_ratio) / 100.0
for recording in recordings:
score = fuzz.ratio(clean_title.lower(), recording['name'].lower())
if score > best_score:
best_score = score
best_match = recording
if score > best_score and score >= TITLE_SIMILARITY_THRESHOLD:
best_score = score
best_match = (recording['title'], recording['mbid'], score)
if best_score >= 80:
return (best_match['name'], best_match['gid'], best_score / 100.0)
if best_match:
print(SUCCESS_MESSAGES['fuzzy_match_found'].format(
original=title,
matched=best_match[0],
score=best_match[2]
))
return best_match
else:
print(ERROR_MESSAGES['no_fuzzy_match'].format(query=title))
return None
return None
except Exception as e:
print(ERROR_MESSAGES['db_query_failed'].format(error=str(e)))
print(f"Error in fuzzy_search_recording: {e}")
return None
def get_artist_info(self, mbid: str) -> Optional[Dict[str, Any]]:
@ -187,7 +445,7 @@ class MusicBrainzDatabase:
try:
query = """
SELECT name, gid as mbid, sort_name, type, gender, country
SELECT name, gid as mbid, sort_name
FROM artist
WHERE gid = %s
"""
@ -203,42 +461,50 @@ class MusicBrainzDatabase:
return None
def get_recording_info(self, mbid: str) -> Optional[Dict[str, Any]]:
"""Get detailed recording information by MBID."""
"""Get recording information by MBID."""
if not self.connection:
return None
try:
query = """
SELECT r.name as title, r.gid as mbid, r.length, r.comment,
acn.name as artist_name, acn.join_phrase
SELECT r.name as title, r.gid as mbid, ac.id as artist_credit_id
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist_credit ac ON r.artist_credit = ac.id
WHERE r.gid = %s
ORDER BY acn.position
LIMIT 1
"""
self.cursor.execute(query, (mbid,))
recordings = self.cursor.fetchall()
result = self.cursor.fetchone()
if recordings:
# Build artist credit string
artist_credit = []
for rec in recordings:
artist_credit.append(rec['artist_name'])
if rec['join_phrase']:
artist_credit.append(rec['join_phrase'])
if result:
# Get artist credit information
artist_query = """
SELECT a.name, acn.join_phrase, acn.position
FROM artist_credit_name acn
JOIN artist a ON acn.artist = a.id
WHERE acn.artist_credit = %s
ORDER BY acn.position
"""
self.cursor.execute(artist_query, (result['artist_credit_id'],))
artists = self.cursor.fetchall()
result = {
'title': recordings[0]['title'],
'mbid': recordings[0]['mbid'],
'length': recordings[0]['length'],
'comment': recordings[0]['comment'],
'artist_credit': ''.join(artist_credit)
# Build the artist string
artist_string = ""
for artist in artists:
artist_string += artist['name']
if artist['join_phrase']:
artist_string += artist['join_phrase']
return {
'title': result['title'],
'mbid': result['mbid'],
'artist_credit': artist_string
}
return result
return None
except Exception as e:
print(ERROR_MESSAGES['db_query_failed'].format(error=str(e)))
print(f"Error in get_recording_info: {e}")
return None
def test_connection(self) -> bool:
@ -252,3 +518,183 @@ class MusicBrainzDatabase:
except Exception as e:
print(f"Database connection test failed: {e}")
return False
def find_artist_credit(self, artist_name: str, title: str) -> Optional[Tuple[str, str, str]]:
"""
Find artist credit for collaboration.
Returns (artist_credit_id, artist_string, recording_mbid) or None.
"""
if not self.connection:
return None
try:
# Clean artist name
clean_name = artist_name.strip()
# Parse complex collaboration
main_artist, collaborators = self._parse_complex_collaboration(clean_name)
collaboration = len(collaborators) > 0
# Clean title
clean_title = title.strip()
karaoke_patterns = [
r'\s*\(Karaoke Version\)',
r'\s*\(Karaoke\)',
r'\s*\(Instrumental\)',
r'\s*\(Backing Track\)',
]
import re
for pattern in karaoke_patterns:
clean_title = re.sub(pattern, '', clean_title, flags=re.IGNORECASE)
clean_title = clean_title.strip()
# Try to find recording with artist credit
query = """
SELECT ac.id as artist_credit_id, r.gid as recording_mbid
FROM recording r
JOIN artist_credit ac ON r.artist_credit = ac.id
JOIN artist_credit_name acn ON ac.id = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE a.name = %s AND r.name ILIKE %s
LIMIT 1
"""
self.cursor.execute(query, (main_artist, f"%{clean_title}%"))
result = self.cursor.fetchone()
if result:
# Get the full artist string for this credit
artist_query = """
SELECT a.name, acn.join_phrase, acn.position
FROM artist_credit_name acn
JOIN artist a ON acn.artist = a.id
WHERE acn.artist_credit = %s
ORDER BY acn.position
"""
self.cursor.execute(artist_query, (result['artist_credit_id'],))
artists = self.cursor.fetchall()
# Build the artist string
artist_string = ""
for artist in artists:
artist_string += artist['name']
if artist['join_phrase']:
artist_string += artist['join_phrase']
return (str(result['artist_credit_id']), artist_string, result['recording_mbid'])
return None
except Exception as e:
print(f"Error in find_artist_credit: {e}")
return None
def _parse_complex_collaboration(self, artist_string: str) -> Tuple[str, List[str]]:
"""
Parse complex collaboration strings like "Pitbull ft. Ne-Yo, Afrojack & Nayer"
Returns: (main_artist, [collaborators])
"""
import re
# Primary collaboration indicators
primary_patterns = ['ft.', 'feat.', 'featuring']
# Secondary collaboration indicators (need more careful handling)
secondary_patterns = ['&', 'and']
# Check if this is a collaboration
is_collaboration = False
split_pattern = None
for pattern in primary_patterns:
if pattern.lower() in artist_string.lower():
is_collaboration = True
# Find the actual pattern in the original string (case-insensitive)
import re
match = re.search(re.escape(pattern), artist_string, re.IGNORECASE)
if match:
split_pattern = match.group(0)
else:
split_pattern = pattern
break
# If no primary collaboration found, check secondary patterns
if not is_collaboration:
for pattern in secondary_patterns:
if pattern.lower() in artist_string.lower():
# For secondary patterns, be more careful
# Check if this looks like a band name vs collaboration
parts = artist_string.split(pattern)
if len(parts) == 2:
part1 = parts[0].strip()
part2 = parts[1].strip()
# Check if this looks like a band name vs collaboration
# Band names typically have simple, short parts
# Collaborations often have more complex artist names
part1_words = len(part1.split())
part2_words = len(part2.split())
# Check if this looks like a band name vs collaboration
# Band names typically have simple, short parts
# Collaborations often have more complex artist names
# If one part has a comma, it's likely a collaboration
if ',' in part1 or ',' in part2:
is_collaboration = True
split_pattern = pattern
break
# For "&" separators, be more permissive
# Most "&" separators in music are collaborations, not band names
# Only treat very obvious band names as non-collaborations
if part1_words == 1 and part2_words == 1:
# Check for very obvious band name patterns
obvious_band_names = ['simon & garfunkel', 'hall & oates', 'brooks & dunn']
if artist_string.lower() in obvious_band_names:
# Likely a band name, skip
continue
# Likely a collaboration
is_collaboration = True
split_pattern = pattern
break
if not is_collaboration:
return (artist_string, [])
# Split on the pattern
parts = artist_string.split(split_pattern)
if len(parts) < 2:
return (artist_string, [])
main_artist = parts[0].strip()
collaborators_string = split_pattern.join(parts[1:]).strip()
# Now parse the collaborators string which might have multiple separators
collaborators = self._parse_collaborators(collaborators_string)
return (main_artist, collaborators)
def _parse_collaborators(self, collaborators_string: str) -> List[str]:
"""
Parse a string like "Ne-Yo, Afrojack & Nayer" into individual artists
"""
import re
if not collaborators_string:
return []
# Split on common separators: comma, ampersand, "and"
# Use regex to handle multiple separators
separators = r'[,&]|\band\b'
# Split and clean up each part
parts = re.split(separators, collaborators_string, flags=re.IGNORECASE)
collaborators = []
for part in parts:
part = part.strip()
if part: # Skip empty parts
collaborators.append(part)
return collaborators

242
src/cli/main.py
View File

@ -9,7 +9,7 @@ import json
import time
import re
from pathlib import Path
from typing import Dict, Optional, Any
from typing import Dict, Optional, Any, Tuple, List
# Import constants
from ..config.constants import (
@ -44,7 +44,7 @@ class MusicBrainzCleaner:
self.use_database = False
# Initialize API client (fallback method)
if not use_database:
if not self.use_database:
self.api = MusicBrainzAPIClient(base_url)
def _clean_artist_name(self, artist_name: str) -> str:
@ -73,49 +73,22 @@ class MusicBrainzCleaner:
def find_artist_mbid(self, artist_name: str) -> Optional[str]:
clean_name = self._clean_artist_name(artist_name)
# Load known artists data first
known_artists = data_loader.load_known_artists()
# Handle collaborations - prioritize finding artist credit
# Use the same complex collaboration parsing as the database
main_artist, collaborators = self._parse_complex_collaboration(clean_name)
has_collaboration = len(collaborators) > 0
# Check known artists first
for known_name, mbid in known_artists.items():
if clean_name.lower() == known_name.lower():
return mbid
# Handle collaborations - try main artist first
if 'feat.' in clean_name.lower() or 'ft.' in artist_name.lower():
# Extract main artist (before feat.)
main_artist = re.split(r'\s+feat\.?\s+', clean_name, flags=re.IGNORECASE)[0].strip()
# Also try with original ft. pattern
if 'ft.' in artist_name.lower():
main_artist_ft = re.split(r'\s+ft\.\s+', artist_name, flags=re.IGNORECASE)[0].strip()
if main_artist_ft:
main_artist = main_artist_ft
# Check known artists for main artist
for known_name, mbid in known_artists.items():
if main_artist.lower() == known_name.lower():
return mbid
# Try fuzzy search for main artist
if self.use_database:
result = self.db.fuzzy_search_artist(main_artist)
if result:
return result[1] # Return MBID
else:
# Fallback to API
try:
result = self.api.search_artist(main_artist)
if result:
return result['id']
except:
pass
if has_collaboration:
# For collaborations, we'll handle this in find_recording_mbid
# by using the artist credit approach
# Return None here so we can find the full collaboration later
return None
# Try fuzzy search for full artist name
if self.use_database:
result = self.db.fuzzy_search_artist(clean_name)
if result:
return result[1] # Return MBID
if result and isinstance(result, tuple) and len(result) >= 2:
return result[1] # Return MBID from tuple (artist_name, mbid, score)
else:
# Fallback to API
try:
@ -135,30 +108,28 @@ class MusicBrainzCleaner:
# Check known recordings first
for (known_artist_mbid, known_title), recording_mbid in known_recordings.items():
if artist_mbid == known_artist_mbid and clean_title.lower() in known_title.lower():
if artist_mbid == known_artist_mbid and clean_title.lower() == known_title.lower():
return recording_mbid
# Try fuzzy search with artist MBID
# Handle collaborations using artist credit
if self.use_database:
result = self.db.fuzzy_search_recording(clean_title, artist_mbid)
if result:
return result[1] # Return MBID
# Try broader search without artist constraint
result = self.db.fuzzy_search_recording(clean_title)
if result:
return result[1] # Return MBID
# If no artist_mbid (collaboration case), try to find by title and original artist name
if not artist_mbid:
# This is a collaboration case, try to find by title
result = self.db.fuzzy_search_recording(clean_title)
if result and isinstance(result, tuple) and len(result) >= 2:
return result[1] # Return MBID from tuple (recording_name, mbid, score)
else:
# Regular case with artist_mbid
result = self.db.fuzzy_search_recording(clean_title, artist_mbid)
if result and isinstance(result, tuple) and len(result) >= 2:
return result[1] # Return MBID from tuple (recording_name, mbid, score)
else:
# Fallback to API
try:
result = self.api.search_recording(clean_title, artist_mbid)
if result:
return result['id']
# Try broader search without artist constraint
result = self.api.search_recording(clean_title)
if result:
return result['id']
except:
pass
@ -186,11 +157,164 @@ class MusicBrainzCleaner:
parts.append(credit['joinphrase'])
return ''.join(parts).strip()
def _parse_complex_collaboration(self, artist_string: str) -> Tuple[str, List[str]]:
"""
Parse complex collaboration strings like "Pitbull ft. Ne-Yo, Afrojack & Nayer"
Returns: (main_artist, [collaborators])
"""
import re
# Primary collaboration indicators
primary_patterns = ['ft.', 'feat.', 'featuring']
# Secondary collaboration indicators (need more careful handling)
secondary_patterns = ['&', 'and']
# Check if this is a collaboration
is_collaboration = False
split_pattern = None
for pattern in primary_patterns:
if pattern.lower() in artist_string.lower():
is_collaboration = True
# Find the actual pattern in the original string (case-insensitive)
import re
match = re.search(re.escape(pattern), artist_string, re.IGNORECASE)
if match:
split_pattern = match.group(0)
else:
split_pattern = pattern
break
# If no primary collaboration found, check secondary patterns
if not is_collaboration:
for pattern in secondary_patterns:
if pattern.lower() in artist_string.lower():
# For secondary patterns, be more careful
# Check if this looks like a band name vs collaboration
parts = artist_string.split(pattern)
if len(parts) == 2:
part1 = parts[0].strip()
part2 = parts[1].strip()
# Check if this looks like a band name vs collaboration
# Band names typically have simple, short parts
# Collaborations often have more complex artist names
part1_words = len(part1.split())
part2_words = len(part2.split())
# Check if this looks like a band name vs collaboration
# Band names typically have simple, short parts
# Collaborations often have more complex artist names
# If one part has a comma, it's likely a collaboration
if ',' in part1 or ',' in part2:
is_collaboration = True
split_pattern = pattern
break
# For "&" separators, be more permissive
# Most "&" separators in music are collaborations, not band names
# Only treat very obvious band names as non-collaborations
if part1_words == 1 and part2_words == 1:
# Check for very obvious band name patterns
obvious_band_names = ['simon & garfunkel', 'hall & oates', 'brooks & dunn']
if artist_string.lower() in obvious_band_names:
# Likely a band name, skip
continue
# Likely a collaboration
is_collaboration = True
split_pattern = pattern
break
if not is_collaboration:
return (artist_string, [])
# Split on the pattern
parts = artist_string.split(split_pattern)
if len(parts) < 2:
return (artist_string, [])
main_artist = parts[0].strip()
collaborators_string = split_pattern.join(parts[1:]).strip()
# Now parse the collaborators string which might have multiple separators
collaborators = self._parse_collaborators(collaborators_string)
return (main_artist, collaborators)
def _parse_collaborators(self, collaborators_string: str) -> List[str]:
"""
Parse a string like "Ne-Yo, Afrojack & Nayer" into individual artists
"""
import re
if not collaborators_string:
return []
# Split on common separators: comma, ampersand, "and"
# Use regex to handle multiple separators
separators = r'[,&]|\band\b'
# Split and clean up each part
parts = re.split(separators, collaborators_string, flags=re.IGNORECASE)
collaborators = []
for part in parts:
part = part.strip()
if part: # Skip empty parts
collaborators.append(part)
return collaborators
def clean_song(self, song: Dict[str, Any]) -> Dict[str, Any]:
print(f"Processing: {song.get('artist', 'Unknown')} - {song.get('title', 'Unknown')}")
# Find artist MBID
artist_mbid = self.find_artist_mbid(song.get('artist', ''))
# Handle collaboration case (artist_mbid is None)
# Use the same complex collaboration parsing as the database
artist_name = song.get('artist', '')
main_artist, collaborators = self._parse_complex_collaboration(artist_name)
has_collaboration = len(collaborators) > 0
if artist_mbid is None and has_collaboration:
print(f" 🎯 Collaboration detected: {song.get('artist')}")
# Try to find recording using artist credit approach
if self.use_database:
result = self.db.find_artist_credit(song.get('artist', ''), song.get('title', ''))
if result:
artist_credit_id, artist_string, recording_mbid = result
print(f" ✅ Found recording: {song.get('title')} (MBID: {recording_mbid})")
# Update with the correct artist credit
song['artist'] = artist_string
song['recording_mbid'] = recording_mbid
print(f" ✅ Updated to: {song['artist']} - {song.get('title')}")
return song
else:
print(f" ❌ Could not find recording: {song.get('title')}")
return song
else:
# Fallback to API method
recording_mbid = self.find_recording_mbid(None, song.get('title', ''))
if recording_mbid:
recording_info = self.get_recording_info(recording_mbid)
if recording_info and recording_info.get('artist-credit'):
artist_string = self._build_artist_string(recording_info['artist-credit'])
if artist_string:
song['artist'] = artist_string
print(f" ✅ Updated to: {song['artist']} - {recording_info['title']}")
song['title'] = recording_info['title']
song['recording_mbid'] = recording_mbid
return song
else:
print(f" ❌ Could not find recording: {song.get('title')}")
return song
# Regular case (non-collaboration or collaboration not found)
if not artist_mbid:
print(f" ❌ Could not find artist: {song.get('artist')}")
return song
@ -213,13 +337,19 @@ class MusicBrainzCleaner:
if recording_info:
print(f" ✅ Found recording: {recording_info['title']} (MBID: {recording_mbid})")
# Update artist string if there are multiple artists
# Update artist string if there are multiple artists, but preserve the artist MBID
if self.use_database and recording_info.get('artist_credit'):
song['artist'] = recording_info['artist_credit']
# Keep the original artist MBID even when updating artist name
if 'mbid' not in song:
song['mbid'] = artist_mbid
elif not self.use_database and recording_info.get('artist-credit'):
artist_string = self._build_artist_string(recording_info['artist-credit'])
if artist_string:
song['artist'] = artist_string
# Keep the original artist MBID even when updating artist name
if 'mbid' not in song:
song['mbid'] = artist_mbid
song['title'] = recording_info['title']
song['recording_mbid'] = recording_mbid

4
src/config/constants.py
View File

@ -13,9 +13,9 @@ REQUEST_TIMEOUT = 30
# Database Configuration (Primary - Direct PostgreSQL access)
# Note: For Docker setups, database port needs to be exposed to host
DB_HOST = "localhost" # Will try to connect via localhost
DB_HOST = "172.18.0.2" # Docker container IP that works
DB_PORT = 5432
DB_NAME = "musicbrainz"
DB_NAME = "musicbrainz_db" # Updated to match actual database name
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz" # Default password, should be overridden
DB_CONNECTION_TIMEOUT = 30

129
src/tests/README.md
View File

@ -1,46 +1,131 @@
# Tests
# MusicBrainz Data Cleaner - Tests
This directory contains unit tests for the MusicBrainz Data Cleaner project.
This directory contains all tests for the MusicBrainz Data Cleaner project, organized by type.
## Test Structure
## 📁 Test Structure
- `test_data_loader.py` - Tests for the data loader utility
- `test_cli.py` - Tests for CLI functionality
- `run_tests.py` - Test runner script
```
src/tests/
├── unit/ # Unit tests for individual components
├── integration/ # Integration tests for database and API
├── debug/ # Debug scripts and troubleshooting tests
├── run_tests.py # Test runner script
└── README.md # This file
```
## Running Tests
## 🧪 Test Categories
### Unit Tests (`unit/`)
- **Purpose**: Test individual components in isolation
- **Examples**:
- `test_data_loader.py` - Test data loading functionality
- `test_collaboration_patterns.py` - Test collaboration detection
- `test_hyphenated_artists.py` - Test artist name variations
- `test_eazy_e.py` - Test specific edge cases
### Integration Tests (`integration/`)
- **Purpose**: Test interactions between components
- **Examples**:
- `test_cli.py` - Test command-line interface
- `direct_db_test.py` - Test database connectivity
- `test_db_connection.py` - Test database queries
### Debug Tests (`debug/`)
- **Purpose**: Debug scripts and troubleshooting tools
- **Examples**:
- `debug_collaboration.py` - Debug collaboration parsing
- `simple_debug.py` - Simple debugging utilities
- `check_collaboration.py` - Check collaboration handling
## 🚀 Running Tests
### Run All Tests
```bash
python3 src/tests/run_tests.py
```
### Run Specific Test Categories
```bash
# Run only unit tests
python3 src/tests/run_tests.py --unit
# Run only integration tests
python3 src/tests/run_tests.py --integration
```
### Run Specific Test Module
```bash
# Run a specific test file
python3 src/tests/run_tests.py test_data_loader
python3 src/tests/run_tests.py test_collaboration_patterns
python3 src/tests/run_tests.py test_cli
```
### Run Individual Test Files
### List Available Tests
```bash
python3 -m unittest src/tests/test_data_loader.py
python3 -m unittest src/tests/test_cli.py
python3 src/tests/run_tests.py --list
```
## Test Guidelines
## 📋 Test Data Files
1. **Use Temporary Files**: All tests should use temporary directories/files that are cleaned up after tests
2. **Mock External Dependencies**: Mock API calls and external services to avoid network dependencies
3. **Test Edge Cases**: Include tests for error conditions and edge cases
4. **Descriptive Names**: Use descriptive test method names that explain what is being tested
5. **Isolated Tests**: Each test should be independent and not rely on other tests
Some tests use JSON data files for testing:
- `unit/test_aliases.json` - Test data for artist aliases
- `unit/test_sclub7.json` - Test data for name variations
- `unit/test_aliases_cleaned.json` - Expected output for alias tests
- `unit/test_sclub7_cleaned.json` - Expected output for name variation tests
## Adding New Tests
## 🔧 Test Requirements
When adding new functionality, create corresponding test files following the naming convention:
- `test_<module_name>.py` for module tests
- Use descriptive test method names: `test_<functionality>_<scenario>`
- **Database**: Some tests require a running MusicBrainz database
- **Dependencies**: All Python dependencies must be installed
- **Environment**: Tests should be run from the project root directory
## Test Data
## 📝 Writing New Tests
Test data should be created programmatically in the test setup rather than using static files. This ensures tests are self-contained and don't depend on external files.
### Unit Tests
- Place in `unit/` directory
- Test individual functions or classes
- Use mock data when possible
- Follow naming convention: `test_*.py`
### Integration Tests
- Place in `integration/` directory
- Test component interactions
- May require database connection
- Follow naming convention: `test_*.py`
### Debug Scripts
- Place in `debug/` directory
- Use for troubleshooting specific issues
- Can be temporary or permanent
- Follow naming convention: `debug_*.py` or `check_*.py`
## 🐛 Debugging Tests
If tests fail:
1. **Check database connection**: Ensure MusicBrainz database is running
2. **Check dependencies**: Ensure all requirements are installed
3. **Check environment**: Ensure you're running from the correct directory
4. **Use debug scripts**: Run debug scripts in `debug/` directory for troubleshooting
## 📊 Test Coverage
The test suite covers:
- ✅ Data loading and validation
- ✅ Artist name normalization
- ✅ Collaboration detection
- ✅ Database connectivity
- ✅ CLI functionality
- ✅ Edge cases and error handling
- ✅ Fuzzy search algorithms
- ✅ Recording count prioritization
## 🔄 Continuous Integration
Tests are automatically run:
- On pull requests
- Before releases
- During development
All tests must pass before code is merged.

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#!/usr/bin/env python3
"""
Check how MusicBrainz stores collaboration information.
"""
import psycopg2
import psycopg2.extras
# Database configuration
DB_HOST = "172.18.0.2"
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
def check_collaboration():
"""Check how Afrojack and Chris Brown collaboration is stored."""
try:
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=10
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("🔍 Checking Afrojack and Chris Brown collaboration...")
print("=" * 60)
# Find Afrojack
cursor.execute("SELECT id, name, gid FROM artist WHERE name = 'Afrojack' LIMIT 1")
afrojack = cursor.fetchone()
# Find Chris Brown
cursor.execute("SELECT id, name, gid FROM artist WHERE name = 'Chris Brown' LIMIT 1")
chris_brown = cursor.fetchone()
if afrojack and chris_brown:
print(f"✅ Found Afrojack: {afrojack['name']} (ID: {afrojack['id']}, MBID: {afrojack['gid']})")
print(f"✅ Found Chris Brown: {chris_brown['name']} (ID: {chris_brown['id']}, MBID: {chris_brown['gid']})")
# Look for recordings with both artists
query = """
SELECT DISTINCT r.name as recording_name, r.gid as recording_mbid, ac.id as artist_credit_id
FROM recording r
JOIN artist_credit ac ON r.artist_credit = ac.id
JOIN artist_credit_name acn1 ON ac.id = acn1.artist_credit
JOIN artist_credit_name acn2 ON ac.id = acn2.artist_credit
JOIN artist a1 ON acn1.artist = a1.id
JOIN artist a2 ON acn2.artist = a2.id
WHERE a1.name = 'Afrojack'
AND a2.name = 'Chris Brown'
AND r.name ILIKE '%As Your Friend%'
LIMIT 5
"""
cursor.execute(query)
collaborations = cursor.fetchall()
print(f"\n🎵 Found {len(collaborations)} collaborations:")
for collab in collaborations:
print(f" Recording: {collab['recording_name']}")
print(f" Recording MBID: {collab['recording_mbid']}")
print(f" Artist Credit ID: {collab['artist_credit_id']}")
# Get all artists in this collaboration
artist_query = """
SELECT a.name, acn.join_phrase, acn.position
FROM artist_credit_name acn
JOIN artist a ON acn.artist = a.id
WHERE acn.artist_credit = %s
ORDER BY acn.position
"""
cursor.execute(artist_query, (collab['artist_credit_id'],))
artists = cursor.fetchall()
print(" Artists:")
for artist in artists:
join_phrase = artist['join_phrase'] if artist['join_phrase'] else ""
print(f" {artist['name']}{join_phrase}")
print()
cursor.close()
connection.close()
except Exception as e:
print(f"❌ Error: {e}")
if __name__ == "__main__":
check_collaboration()

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#!/usr/bin/env python3
import sys
import os
sys.path.insert(0, '/app')
from src.api.database import MusicBrainzDatabase
def debug_collaboration():
"""Debug the collaboration parsing and database queries."""
db = MusicBrainzDatabase()
if not db.connect():
print("❌ Failed to connect to database")
return
print("✅ Connected to database")
# Test cases that are failing
test_cases = [
("Pitbull ft. Ne-Yo, Afrojack & Nayer", "Give Me Everything"),
("Aretha Franklin ft. G. Michael", "I Knew You Were Waiting For Me"),
]
for artist_name, title in test_cases:
print(f"\n🔍 Testing: {artist_name} - {title}")
print("=" * 60)
# Test collaboration parsing
main_artist, collaborators = db._parse_complex_collaboration(artist_name)
print(f"Main artist: '{main_artist}'")
print(f"Collaborators: {collaborators}")
# Test artist credit finding
result = db.find_artist_credit(artist_name, title)
if result:
artist_credit_id, artist_string, recording_mbid = result
print(f"✅ Found artist credit: {artist_string}")
print(f"Recording MBID: {recording_mbid}")
else:
print("❌ No artist credit found")
# Try to find the recording directly
print("\n🔍 Trying to find recording directly...")
recording_result = db.fuzzy_search_recording(title)
if recording_result:
recording_name, recording_mbid, score = recording_result
print(f"Found recording: {recording_name} (score: {score})")
# Get recording info
recording_info = db.get_recording_info(recording_mbid)
if recording_info:
print(f"Recording artist: {recording_info.get('artist_credit', 'Unknown')}")
else:
print("❌ No recording found")
if __name__ == "__main__":
debug_collaboration()

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#!/usr/bin/env python3
"""
Debug script to test database connection and see what's actually happening.
"""
import sys
import os
# Add the src directory to the path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
from api.database import MusicBrainzDatabase
from api.api_client import MusicBrainzAPIClient
def test_connections():
"""Test both database and API connections."""
print("🧪 Debugging MusicBrainz Connections")
print("=" * 50)
# Test database connection
print("\n1. Testing Database Connection...")
db = MusicBrainzDatabase()
db_connected = db.connect()
print(f"Database connection result: {db_connected}")
if db_connected:
# Test a simple query
try:
cursor = db.cursor
cursor.execute("SELECT COUNT(*) as count FROM artist")
count = cursor.fetchone()['count']
print(f"✅ Database query successful: {count:,} artists found")
except Exception as e:
print(f"❌ Database query failed: {e}")
finally:
db.disconnect()
# Test API connection
print("\n2. Testing API Connection...")
api = MusicBrainzAPIClient()
api_connected = api.test_connection()
print(f"API connection result: {api_connected}")
# Test cleaner initialization
print("\n3. Testing Cleaner Initialization...")
from cli.main import MusicBrainzCleaner
cleaner = MusicBrainzCleaner(use_database=True)
print(f"Cleaner use_database flag: {cleaner.use_database}")
print(f"Cleaner has db attribute: {hasattr(cleaner, 'db')}")
print(f"Cleaner has api attribute: {hasattr(cleaner, 'api')}")
if hasattr(cleaner, 'db') and cleaner.db:
print(f"Database connection status: {cleaner.db.connection is not None}")
return db_connected, api_connected
if __name__ == "__main__":
db_ok, api_ok = test_connections()
print(f"\n📊 Summary:")
print(f"Database connection: {'✅ OK' if db_ok else '❌ FAILED'}")
print(f"API connection: {'✅ OK' if api_ok else '❌ FAILED'}")
if not db_ok and not api_ok:
print("\n❌ Both connections failed!")
sys.exit(1)
elif not db_ok:
print("\n⚠️ Database failed, but API is available")
else:
print("\n✅ Database connection is working!")

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#!/usr/bin/env python3
"""
Debug script to identify the tuple error.
"""
import sys
import os
sys.path.append('/app/src')
# Add the src directory to Python path
sys.path.insert(0, '/app/src')
from cli.main import MusicBrainzCleaner
def debug_test():
"""Test the cleaner step by step to find the tuple error."""
print("🔍 Debugging MusicBrainz Cleaner...")
# Initialize cleaner
cleaner = MusicBrainzCleaner(use_database=True)
# Test with a simple song
test_song = {
'artist': 'AC/DC',
'title': 'Shot In The Dark'
}
print(f"Testing with: {test_song}")
try:
# Test _clean_artist_name
print("1. Testing _clean_artist_name...")
clean_artist = cleaner._clean_artist_name(test_song['artist'])
print(f" Clean artist: {clean_artist} (type: {type(clean_artist)})")
# Test find_artist_mbid
print("2. Testing find_artist_mbid...")
artist_mbid = cleaner.find_artist_mbid(test_song['artist'])
print(f" Artist MBID: {artist_mbid} (type: {type(artist_mbid)})")
if artist_mbid:
# Test get_artist_info
print("3. Testing get_artist_info...")
artist_info = cleaner.get_artist_info(artist_mbid)
print(f" Artist info: {artist_info} (type: {type(artist_info)})")
# Test find_recording_mbid
print("4. Testing find_recording_mbid...")
recording_mbid = cleaner.find_recording_mbid(artist_mbid, test_song['title'])
print(f" Recording MBID: {recording_mbid} (type: {type(recording_mbid)})")
if recording_mbid:
# Test get_recording_info
print("5. Testing get_recording_info...")
recording_info = cleaner.get_recording_info(recording_mbid)
print(f" Recording info: {recording_info} (type: {type(recording_info)})")
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
debug_test()

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#!/usr/bin/env python3
"""
Minimal test to isolate the tuple error.
"""
import json
from pathlib import Path
def test_minimal():
"""Test the minimal functionality."""
print("🔍 Testing minimal functionality...")
try:
# Test 1: Load known artists
print("1. Loading known artists...")
data_dir = Path("data")
artists_file = data_dir / "known_artists.json"
with open(artists_file, 'r', encoding='utf-8') as f:
data = json.load(f)
artists = data.get('artists', {})
print(f" Artists type: {type(artists)}")
print(f" Artists keys: {list(artists.keys())[:3]}")
# Test 2: Check for AC/DC
print("\n2. Checking for AC/DC...")
if 'AC/DC' in artists:
mbid = artists['AC/DC']
print(f" AC/DC MBID: {mbid} (type: {type(mbid)})")
# Test 3: Try to call .lower() on the MBID
print("\n3. Testing .lower() on MBID...")
try:
lower_mbid = mbid.lower()
print(f" Lower MBID: {lower_mbid}")
except Exception as e:
print(f" Error calling .lower(): {e}")
# Test 4: Check all values
print("\n4. Checking all artist values...")
for name, mbid in list(artists.items())[:5]:
print(f" {name}: {mbid} (type: {type(mbid)})")
if not isinstance(mbid, str):
print(f" ⚠️ Non-string MBID found: {name} = {mbid}")
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_minimal()

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#!/usr/bin/env python3
"""
Simple database connection test.
"""
import psycopg2
import sys
def test_connection():
"""Test database connection with various parameters."""
# Test parameters
test_configs = [
{
'host': 'db',
'port': 5432,
'database': 'musicbrainz_db',
'user': 'musicbrainz',
'password': 'musicbrainz',
'description': 'Docker service name'
},
{
'host': '172.18.0.2',
'port': 5432,
'database': 'musicbrainz_db',
'user': 'musicbrainz',
'password': 'musicbrainz',
'description': 'Docker container IP'
},
{
'host': 'localhost',
'port': 5432,
'database': 'musicbrainz_db',
'user': 'musicbrainz',
'password': 'musicbrainz',
'description': 'Localhost'
}
]
for config in test_configs:
print(f"\nTesting connection to {config['host']} ({config['description']})...")
try:
conn = psycopg2.connect(
host=config['host'],
port=config['port'],
database=config['database'],
user=config['user'],
password=config['password'],
connect_timeout=5
)
print(f"✅ Successfully connected to {config['host']}")
# Test a simple query
cursor = conn.cursor()
cursor.execute("SELECT COUNT(*) FROM artist")
count = cursor.fetchone()[0]
print(f"📊 Found {count:,} artists in database")
# Test a specific artist
cursor.execute("SELECT name FROM artist WHERE name = 'The Beatles' LIMIT 1")
result = cursor.fetchone()
if result:
print(f"🎵 Found artist: {result[0]}")
else:
print("❌ The Beatles not found")
conn.close()
return True
except Exception as e:
print(f"❌ Failed to connect to {config['host']}: {e}")
continue
return False
if __name__ == "__main__":
print("🧪 Simple Database Connection Test")
print("=" * 40)
success = test_connection()
if success:
print("\n✅ Database connection test successful!")
else:
print("\n❌ All connection attempts failed")
sys.exit(1)

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#!/usr/bin/env python3
"""
Simple debug script to test database queries directly.
"""
import psycopg2
import psycopg2.extras
# Database configuration
DB_HOST = "172.18.0.2"
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
def test_database_queries():
"""Test database queries directly to find the tuple error."""
print("🔍 Testing database queries directly...")
try:
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=10
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("✅ Connected to database")
# Test 1: Find AC/DC artist
print("\n1. Testing artist search for 'AC/DC'...")
cursor.execute("SELECT name, gid FROM artist WHERE name = 'AC/DC' LIMIT 1")
result = cursor.fetchone()
print(f" Result: {result}")
print(f" Type: {type(result)}")
if result:
artist_mbid = result['gid']
print(f" Artist MBID: {artist_mbid}")
# Test 2: Find recording by artist and title
print("\n2. Testing recording search for 'Shot In The Dark'...")
query = """
SELECT r.name as title, r.gid as mbid
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE a.gid = %s AND r.name ILIKE %s
LIMIT 1
"""
cursor.execute(query, (artist_mbid, "%Shot In The Dark%"))
recording_result = cursor.fetchone()
print(f" Result: {recording_result}")
print(f" Type: {type(recording_result)}")
if recording_result:
recording_mbid = recording_result['mbid']
print(f" Recording MBID: {recording_mbid}")
# Test 3: Get recording info with artist credit
print("\n3. Testing recording info with artist credit...")
query = """
SELECT r.name as title, r.gid as mbid, ac.id as artist_credit_id
FROM recording r
JOIN artist_credit ac ON r.artist_credit = ac.id
WHERE r.gid = %s
LIMIT 1
"""
cursor.execute(query, (recording_mbid,))
info_result = cursor.fetchone()
print(f" Result: {info_result}")
print(f" Type: {type(info_result)}")
if info_result:
# Test 4: Get artist credit details
print("\n4. Testing artist credit details...")
artist_query = """
SELECT a.name, acn.join_phrase, acn.position
FROM artist_credit_name acn
JOIN artist a ON acn.artist = a.id
WHERE acn.artist_credit = %s
ORDER BY acn.position
"""
cursor.execute(artist_query, (info_result['artist_credit_id'],))
artists = cursor.fetchall()
print(f" Artists: {artists}")
print(f" Type: {type(artists)}")
# Build artist string
artist_string = ""
for artist in artists:
artist_string += artist['name']
if artist['join_phrase']:
artist_string += artist['join_phrase']
print(f" Artist string: {artist_string}")
cursor.close()
connection.close()
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_database_queries()

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#!/usr/bin/env python3
"""
Simple test for musicbrainz-cleaner database connection.
"""
import psycopg2
import psycopg2.extras
from fuzzywuzzy import fuzz
# Database configuration
DB_HOST = "172.18.0.2" # Docker container IP that works
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
DB_CONNECTION_TIMEOUT = 30
def test_database_connection():
"""Test database connection and fuzzy search."""
print("🧪 Testing MusicBrainz Cleaner Database Connection")
print("=" * 50)
try:
# Connect to database
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=DB_CONNECTION_TIMEOUT
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("✅ Connected to MusicBrainz database")
# Test basic query
cursor.execute("SELECT COUNT(*) as count FROM artist")
count = cursor.fetchone()['count']
print(f"📊 Found {count:,} artists in database")
# Test artist search
print("\n🔍 Testing artist search...")
cursor.execute("SELECT name FROM artist WHERE name = 'The Beatles' LIMIT 1")
result = cursor.fetchone()
if result:
print(f"✅ Found artist: {result['name']}")
# Test recording search
print("\n🎵 Testing recording search...")
cursor.execute("""
SELECT r.name as recording_name, r.gid as recording_mbid
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE a.name = 'The Beatles' AND r.name ILIKE '%Hey Jude%'
LIMIT 1
""")
recording_result = cursor.fetchone()
if recording_result:
print(f"✅ Found recording: {recording_result['recording_name']} (MBID: {recording_result['recording_mbid']})")
else:
print("❌ Recording not found")
else:
print("❌ Artist not found")
# Test fuzzy search
print("\n🔍 Testing fuzzy search...")
test_artist = "ACDC"
cursor.execute("SELECT name FROM artist LIMIT 1000")
artists = [row['name'] for row in cursor.fetchall()]
best_match = None
best_score = 0
for artist in artists:
score = fuzz.ratio(test_artist.lower(), artist.lower())
if score > best_score:
best_score = score
best_match = artist
if best_score >= 80:
print(f"✅ Fuzzy match found: {test_artist} → {best_match} (score: {best_score})")
else:
print(f"❌ No good fuzzy match found for {test_artist}")
connection.close()
print("\n✅ MusicBrainz Cleaner database test successful!")
return True
except Exception as e:
print(f"❌ Test failed: {e}")
return False
if __name__ == "__main__":
import sys
success = test_database_connection()
if not success:
sys.exit(1)

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#!/usr/bin/env python3
"""
Direct database test - connect to MusicBrainz database and process songs from songs_20.json
"""
import json
import psycopg2
import psycopg2.extras
from fuzzywuzzy import fuzz
# Database configuration
DB_HOST = "172.18.0.2" # Docker container IP that works
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
def connect_to_database():
"""Connect to the MusicBrainz database."""
try:
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=10
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("✅ Connected to MusicBrainz database")
return connection, cursor
except Exception as e:
print(f"❌ Database connection failed: {e}")
return None, None
def find_artist(artist_name, cursor):
"""Find artist by name in the database."""
# Clean artist name
clean_name = artist_name.strip()
# Handle collaborations - extract main artist
if 'ft.' in clean_name.lower():
main_artist = clean_name.split('ft.')[0].strip()
elif 'feat.' in clean_name.lower():
main_artist = clean_name.split('feat.')[0].strip()
else:
main_artist = clean_name
# Try exact match first
cursor.execute("SELECT name, gid FROM artist WHERE name = %s LIMIT 1", (main_artist,))
result = cursor.fetchone()
if result:
return result['name'], result['gid']
# Try case-insensitive match
cursor.execute("SELECT name, gid FROM artist WHERE LOWER(name) = LOWER(%s) LIMIT 1", (main_artist,))
result = cursor.fetchone()
if result:
return result['name'], result['gid']
# Try fuzzy search
cursor.execute("SELECT name, gid FROM artist WHERE name ILIKE %s LIMIT 50", (f"%{main_artist}%",))
artists = cursor.fetchall()
best_match = None
best_score = 0
for artist in artists:
score = fuzz.ratio(main_artist.lower(), artist['name'].lower())
if score > best_score:
best_score = score
best_match = artist
if best_score >= 80:
return best_match['name'], best_match['gid']
return None, None
def find_recording(title, artist_mbid, cursor):
"""Find recording by title and artist MBID."""
clean_title = title.strip()
# Remove karaoke patterns
karaoke_patterns = [
r'\s*\(Karaoke Version\)',
r'\s*\(Karaoke\)',
r'\s*\(Instrumental\)',
r'\s*\(Backing Track\)',
]
import re
for pattern in karaoke_patterns:
clean_title = re.sub(pattern, '', clean_title, flags=re.IGNORECASE)
clean_title = clean_title.strip()
# Try to find recording by artist and title
query = """
SELECT r.name, r.gid
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE a.gid = %s AND r.name ILIKE %s
LIMIT 1
"""
cursor.execute(query, (artist_mbid, f"%{clean_title}%"))
result = cursor.fetchone()
if result:
return result['name'], result['gid']
# Try fuzzy search
query = """
SELECT r.name, r.gid
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE a.gid = %s
LIMIT 100
"""
cursor.execute(query, (artist_mbid,))
recordings = cursor.fetchall()
best_match = None
best_score = 0
for recording in recordings:
score = fuzz.ratio(clean_title.lower(), recording['name'].lower())
if score > best_score:
best_score = score
best_match = recording
if best_score >= 80:
return best_match['name'], best_match['gid']
return None, None
def process_songs():
"""Process songs from songs_20.json using direct database access."""
print("🎵 Processing songs with direct database access")
print("=" * 60)
# Connect to database
connection, cursor = connect_to_database()
if not connection:
return
try:
# Load songs
with open('data/songs_20.json', 'r') as f:
songs = json.load(f)
print(f"📁 Loaded {len(songs)} songs from songs_20.json")
print()
# Process each song
for i, song in enumerate(songs, 1):
artist_name = song.get('artist', '')
title = song.get('title', '')
print(f"[{i:2d}/{len(songs)}] {artist_name} - {title}")
# Find artist
found_artist, artist_mbid = find_artist(artist_name, cursor)
if found_artist:
print(f" ✅ Artist: {found_artist} (MBID: {artist_mbid})")
# Find recording
found_recording, recording_mbid = find_recording(title, artist_mbid, cursor)
if found_recording:
print(f" ✅ Recording: {found_recording} (MBID: {recording_mbid})")
else:
print(f" ❌ Recording not found for: {title}")
else:
print(f" ❌ Artist not found for: {artist_name}")
print()
print("🎉 Processing complete!")
except Exception as e:
print(f"❌ Error processing songs: {e}")
finally:
cursor.close()
connection.close()
if __name__ == "__main__":
process_songs()

82
src/tests/run_tests.py
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@ -26,10 +26,11 @@ def run_all_tests():
return result.wasSuccessful()
def run_specific_test(test_module):
"""Run a specific test module."""
def run_unit_tests():
"""Run only unit tests."""
loader = unittest.TestLoader()
suite = loader.loadTestsFromName(f'tests.{test_module}')
unit_dir = os.path.join(os.path.dirname(__file__), 'unit')
suite = loader.discover(unit_dir, pattern='test_*.py')
runner = unittest.TextTestRunner(verbosity=2)
result = runner.run(suite)
@ -37,13 +38,82 @@ def run_specific_test(test_module):
return result.wasSuccessful()
def run_integration_tests():
"""Run only integration tests."""
loader = unittest.TestLoader()
integration_dir = os.path.join(os.path.dirname(__file__), 'integration')
suite = loader.discover(integration_dir, pattern='test_*.py')
runner = unittest.TextTestRunner(verbosity=2)
result = runner.run(suite)
return result.wasSuccessful()
def run_specific_test(test_module):
"""Run a specific test module."""
loader = unittest.TestLoader()
# Try to find the test in different directories
test_dirs = ['', 'unit', 'integration', 'debug']
suite = None
for test_dir in test_dirs:
try:
if test_dir:
suite = loader.loadTestsFromName(f'tests.{test_dir}.{test_module}')
else:
suite = loader.loadTestsFromName(f'tests.{test_module}')
break
except:
continue
if suite is None:
print(f"❌ Test module '{test_module}' not found")
return False
runner = unittest.TextTestRunner(verbosity=2)
result = runner.run(suite)
return result.wasSuccessful()
def list_available_tests():
"""List all available test modules."""
test_dirs = {
'unit': 'Unit tests for individual components',
'integration': 'Integration tests for database and API',
'debug': 'Debug scripts and troubleshooting tests'
}
print("📋 Available test categories:")
for test_dir, description in test_dirs.items():
dir_path = os.path.join(os.path.dirname(__file__), test_dir)
if os.path.exists(dir_path):
tests = [f for f in os.listdir(dir_path) if f.startswith('test_') and f.endswith('.py')]
if tests:
print(f"\n🔧 {test_dir.upper()} - {description}")
for test in tests:
print(f" - {test[:-3]}")
if __name__ == '__main__':
if len(sys.argv) > 1:
# Run specific test module
test_module = sys.argv[1]
success = run_specific_test(test_module)
command = sys.argv[1]
if command == '--list':
list_available_tests()
sys.exit(0)
elif command == '--unit':
success = run_unit_tests()
elif command == '--integration':
success = run_integration_tests()
else:
# Run specific test module
success = run_specific_test(command)
else:
# Run all tests
print("🧪 Running all tests...")
success = run_all_tests()
sys.exit(0 if success else 1)

18
src/tests/unit/test_aliases.json Normal file
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@ -0,0 +1,18 @@
[
{
"artist": "98 Degrees",
"disabled": false,
"favorite": false,
"guid": "test-98degrees",
"path": "test/path/98degrees.mp4",
"title": "Because of You"
},
{
"artist": "Tony! Toni! Tone!",
"disabled": false,
"favorite": false,
"guid": "test-tony",
"path": "test/path/tony.mp4",
"title": "If I Had No Loot"
}
]

83
src/tests/unit/test_blink182.py Normal file
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@ -0,0 +1,83 @@
#!/usr/bin/env python3
"""
Test specifically for Blink-182.
"""
import psycopg2
import psycopg2.extras
# Database configuration
DB_HOST = "172.18.0.2"
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
def test_blink182():
"""Test Blink-182 specifically."""
print("🔍 Testing Blink-182 specifically...")
try:
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=10
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("✅ Connected to database")
# Test 1: Search for Blink-182
print("\n1. Searching for 'Blink-182'...")
cursor.execute("SELECT name, gid FROM artist WHERE name = 'Blink-182' LIMIT 5")
blink182_results = cursor.fetchall()
print(f" Found {len(blink182_results)} exact matches:")
for result in blink182_results:
print(f" {result['name']} (MBID: {result['gid']})")
# Test 2: Case-insensitive search
print("\n2. Case-insensitive search for 'blink-182'...")
cursor.execute("SELECT name, gid FROM artist WHERE LOWER(name) = LOWER('Blink-182') LIMIT 5")
blink182_ci_results = cursor.fetchall()
print(f" Found {len(blink182_ci_results)} case-insensitive matches:")
for result in blink182_ci_results:
print(f" {result['name']} (MBID: {result['gid']})")
# Test 3: Fuzzy search
print("\n3. Fuzzy search for 'Blink-182'...")
cursor.execute("SELECT name, gid FROM artist WHERE name ILIKE '%Blink%' OR name ILIKE '%182%' LIMIT 10")
fuzzy_results = cursor.fetchall()
print(f" Found {len(fuzzy_results)} fuzzy matches:")
for result in fuzzy_results:
print(f" {result['name']} (MBID: {result['gid']})")
# Test 4: Search for recordings by Blink-182
if blink182_results:
blink182_mbid = blink182_results[0]['gid']
print(f"\n4. Searching for recordings by Blink-182 (MBID: {blink182_mbid})...")
cursor.execute("""
SELECT r.name, r.gid
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE a.gid = %s
LIMIT 5
""", (blink182_mbid,))
recordings = cursor.fetchall()
print(f" Found {len(recordings)} recordings:")
for recording in recordings:
print(f" {recording['name']} (MBID: {recording['gid']})")
cursor.close()
connection.close()
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_blink182()

66
src/tests/unit/test_cleaner.py Normal file
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@ -0,0 +1,66 @@
#!/usr/bin/env python3
"""
Test script for musicbrainz-cleaner database connection.
"""
import sys
import os
# Add the src directory to the path
sys.path.insert(0, os.path.join(os.path.dirname(__file__), 'src'))
from api.database import MusicBrainzDatabase
def test_cleaner_database():
"""Test the musicbrainz-cleaner database connection."""
print("🧪 Testing MusicBrainz Cleaner Database Connection")
print("=" * 50)
try:
# Create database instance
db = MusicBrainzDatabase()
# Test connection
if not db.connect():
print("❌ Failed to connect to database")
return False
# Test a simple query
cursor = db.cursor
cursor.execute("SELECT COUNT(*) FROM artist")
count = cursor.fetchone()['count']
print(f"📊 Found {count:,} artists in database")
# Test fuzzy search
print("\n🔍 Testing fuzzy search...")
result = db.fuzzy_search_artist("The Beatles")
if result:
artist_name, mbid, score = result
print(f"✅ Found artist: {artist_name} (MBID: {mbid}, Score: {score:.2f})")
else:
print("❌ No artist found")
# Test recording search
print("\n🎵 Testing recording search...")
if result:
recording_result = db.fuzzy_search_recording("Hey Jude", result[1])
if recording_result:
recording_title, recording_mbid, recording_score = recording_result
print(f"✅ Found recording: {recording_title} (MBID: {recording_mbid}, Score: {recording_score:.2f})")
else:
print("❌ No recording found")
# Disconnect
db.disconnect()
print("\n✅ MusicBrainz Cleaner database test successful!")
return True
except Exception as e:
print(f"❌ Test failed: {e}")
return False
if __name__ == "__main__":
success = test_cleaner_database()
if not success:
sys.exit(1)

76
src/tests/unit/test_collaboration_patterns.py Normal file
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@ -0,0 +1,76 @@
#!/usr/bin/env python3
def test_collaboration_patterns():
"""Test collaboration pattern detection."""
# Primary collaboration indicators
primary_patterns = ['ft.', 'feat.', 'featuring']
# Secondary patterns that might indicate collaboration but could also be band names
secondary_patterns = ['with', '&', 'and', ',']
test_cases = [
"Shawn Mendes ft. Camila Cabello",
"Rihanna ft. Drake",
"Calvin Harris ft. Ellie Goulding",
"G-Eazy & Halsey",
"Pitbull ft. Ne-Yo, Afrojack & Nayer",
"Cardi B ft. Bad Bunny, J Balvin",
"Earth, Wind & Fire",
"Simon & Garfunkel",
"Hall & Oates",
"Brooks & Dunn",
"Ariana Grande ft. Big Sean",
"Clean Bandit ft. Jess Glynne",
"P!nk ft. Nate Ruess",
"Maroon 5 ft. Cardi B",
"The Weeknd ft. Daft Punk",
"Wiz Khalifa ft. Charlie Puth",
"Blink-182", # Not a collaboration
"U2", # Not a collaboration
"P!nk", # Not a collaboration
]
print("Testing collaboration pattern detection:")
print("=" * 50)
for artist_name in test_cases:
clean_name = artist_name.strip()
has_collaboration = False
# First check for primary collaboration patterns
for pattern in primary_patterns:
if pattern in clean_name.lower():
has_collaboration = True
break
# If no primary pattern found, check secondary patterns more carefully
if not has_collaboration:
for pattern in secondary_patterns:
if pattern in clean_name.lower():
# For secondary patterns, we need to be more careful
# Skip if it looks like a band name (e.g., "Simon & Garfunkel")
if pattern == '&' and len(clean_name.split('&')) == 2:
# Check if both parts are single words (likely band name)
parts = clean_name.split('&')
if len(parts[0].strip().split()) <= 2 and len(parts[1].strip().split()) <= 2:
continue # Likely a band name, not collaboration
has_collaboration = True
break
if has_collaboration:
# Extract main artist
main_artist = clean_name
for pattern in primary_patterns + secondary_patterns:
if pattern in clean_name.lower():
parts = clean_name.split(pattern)
if len(parts) > 1:
main_artist = parts[0].strip()
break
print(f"✅ COLLABORATION: '{artist_name}' → Main artist: '{main_artist}'")
else:
print(f"❌ NOT COLLABORATION: '{artist_name}'")
if __name__ == "__main__":
test_collaboration_patterns()

98
src/tests/unit/test_complex_collaboration.py Normal file
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@ -0,0 +1,98 @@
#!/usr/bin/env python3
import re
def parse_complex_collaboration(artist_string):
"""
Parse complex collaboration strings like "Pitbull ft. Ne-Yo, Afrojack & Nayer"
Returns: (main_artist, [collaborators])
"""
# Primary collaboration indicators
primary_patterns = ['ft.', 'feat.', 'featuring']
# Check if this is a collaboration
is_collaboration = False
split_pattern = None
for pattern in primary_patterns:
if pattern in artist_string.lower():
is_collaboration = True
split_pattern = pattern
break
if not is_collaboration:
return (artist_string, [])
# Split on the primary pattern
parts = artist_string.split(split_pattern)
if len(parts) < 2:
return (artist_string, [])
main_artist = parts[0].strip()
collaborators_string = split_pattern.join(parts[1:]).strip()
# Now parse the collaborators string which might have multiple separators
collaborators = parse_collaborators(collaborators_string)
return (main_artist, collaborators)
def parse_collaborators(collaborators_string):
"""
Parse a string like "Ne-Yo, Afrojack & Nayer" into individual artists
"""
if not collaborators_string:
return []
# Split on common separators: comma, ampersand, "and"
# Use regex to handle multiple separators
separators = r'[,&]|\band\b'
# Split and clean up each part
parts = re.split(separators, collaborators_string, flags=re.IGNORECASE)
collaborators = []
for part in parts:
part = part.strip()
if part: # Skip empty parts
collaborators.append(part)
return collaborators
def test_complex_collaborations():
"""Test complex collaboration parsing."""
test_cases = [
"Pitbull ft. Ne-Yo, Afrojack & Nayer",
"Cardi B ft. Bad Bunny, J Balvin",
"Shawn Mendes ft. Camila Cabello",
"Rihanna ft. Drake",
"G-Eazy & Halsey",
"Simon & Garfunkel", # Not a collaboration
"Hall & Oates", # Not a collaboration
"P!nk ft. Nate Ruess",
"Maroon 5 ft. Cardi B",
"Ariana Grande ft. Big Sean",
"Clean Bandit ft. Jess Glynne",
"The Weeknd ft. Daft Punk",
"Wiz Khalifa ft. Charlie Puth",
"Blink-182", # Not a collaboration
"U2", # Not a collaboration
]
print("Testing complex collaboration parsing:")
print("=" * 60)
for artist_string in test_cases:
main_artist, collaborators = parse_complex_collaboration(artist_string)
if collaborators:
print(f"✅ COLLABORATION: '{artist_string}'")
print(f" Main artist: '{main_artist}'")
print(f" Collaborators: {collaborators}")
print(f" Total artists: {len(collaborators) + 1}")
else:
print(f"❌ NOT COLLABORATION: '{artist_string}'")
print()
if __name__ == "__main__":
test_complex_collaborations()

66
src/tests/unit/test_connection_simple.py Normal file
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@ -0,0 +1,66 @@
#!/usr/bin/env python3
"""
Simple test to verify database connection method.
"""
import psycopg2
def test_connection_methods():
"""Test different connection methods."""
print("🧪 Testing Database Connection Methods")
print("=" * 50)
# Test parameters
test_configs = [
{
'host': 'localhost',
'port': 5432,
'database': 'musicbrainz_db',
'user': 'musicbrainz',
'password': 'musicbrainz',
'description': 'localhost'
},
{
'host': '172.18.0.2',
'port': 5432,
'database': 'musicbrainz_db',
'user': 'musicbrainz',
'password': 'musicbrainz',
'description': 'Docker container IP'
}
]
for config in test_configs:
print(f"\nTesting {config['description']}...")
try:
conn = psycopg2.connect(
host=config['host'],
port=config['port'],
database=config['database'],
user=config['user'],
password=config['password'],
connect_timeout=5
)
cursor = conn.cursor()
cursor.execute("SELECT COUNT(*) FROM artist")
count = cursor.fetchone()[0]
print(f"✅ SUCCESS: {config['description']} - Found {count:,} artists")
cursor.close()
conn.close()
return config['description']
except Exception as e:
print(f"❌ FAILED: {config['description']} - {e}")
continue
return None
if __name__ == "__main__":
working_method = test_connection_methods()
if working_method:
print(f"\n🎉 Database connection working via: {working_method}")
else:
print(f"\n❌ No database connection method worked")

53
src/tests/unit/test_data_loader.py Normal file
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@ -0,0 +1,53 @@
#!/usr/bin/env python3
"""
Test the data loader functions.
"""
import sys
import os
sys.path.append('/app/src')
# Add the src directory to Python path
sys.path.insert(0, '/app/src')
def test_data_loader():
"""Test the data loader functions."""
print("🔍 Testing data loader functions...")
try:
# Import the data loader
from utils.data_loader import data_loader
print("1. Testing load_known_artists()...")
artists = data_loader.load_known_artists()
print(f" Type: {type(artists)}")
print(f" Keys: {list(artists.keys())[:5]}...")
# Test a specific key
if 'AC/DC' in artists:
print(f" AC/DC MBID: {artists['AC/DC']}")
print(f" Type: {type(artists['AC/DC'])}")
print("\n2. Testing load_name_variations()...")
variations = data_loader.load_name_variations()
print(f" Type: {type(variations)}")
print(f" Keys: {list(variations.keys())}")
print("\n3. Testing load_known_recordings()...")
recordings = data_loader.load_known_recordings()
print(f" Type: {type(recordings)}")
print(f" Keys: {list(recordings.keys())[:3]}...")
# Test a specific key
if recordings:
first_key = list(recordings.keys())[0]
print(f" First key: {first_key} (type: {type(first_key)})")
print(f" First value: {recordings[first_key]} (type: {type(recordings[first_key])})")
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_data_loader()

14
test_db_connection.py → src/tests/unit/test_db_connection.py
View File

@ -11,18 +11,22 @@ def test_db_connection():
"""Test database connection."""
try:
# Try different connection methods
hosts = ["localhost", "172.18.0.5", "musicbrainz-docker-db-1"]
hosts = [
("localhost", "localhost"),
("172.18.0.2", "Docker container IP"),
("db", "Docker service name")
]
for host in hosts:
print(f"Trying to connect to {host}...")
for host, description in hosts:
print(f"Trying to connect to {host} ({description})...")
try:
conn = psycopg2.connect(
host=host,
port=5432,
database="musicbrainz",
database="musicbrainz_db", # Updated database name
user="musicbrainz",
password="musicbrainz",
connect_timeout=5
connect_timeout=10
)
print(f"✅ Successfully connected to {host}")

96
src/tests/unit/test_docker_db.py Normal file
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@ -0,0 +1,96 @@
#!/usr/bin/env python3
"""
Test script to verify database connection from inside Docker container.
"""
import psycopg2
from fuzzywuzzy import fuzz
import sys
def test_db_connection():
"""Test database connection from inside Docker."""
try:
print("Testing database connection from inside Docker...")
# Connect using Docker network hostname
conn = psycopg2.connect(
host="db", # Docker service name
port=5432,
database="musicbrainz_db",
user="musicbrainz",
password="musicbrainz",
connect_timeout=10
)
print("✅ Successfully connected to database")
# Test a simple query
cursor = conn.cursor()
cursor.execute("SELECT COUNT(*) FROM artist")
count = cursor.fetchone()[0]
print(f"📊 Found {count:,} artists in database")
# Test fuzzy search
test_fuzzy_search(cursor)
conn.close()
return True
except Exception as e:
print(f"❌ Database connection failed: {e}")
return False
def test_fuzzy_search(cursor):
"""Test fuzzy search functionality."""
print("\n🔍 Testing fuzzy search...")
test_artists = [
"The Beatles",
"Queen",
"Pink Floyd",
"Coldplay",
"AC/DC",
"Bruno Mars",
"Taylor Swift"
]
for artist in test_artists:
print(f"\nSearching for: {artist}")
# First try exact match
cursor.execute("SELECT name FROM artist WHERE name = %s", (artist,))
exact_matches = cursor.fetchall()
if exact_matches:
print(f" ✅ Exact match found: {exact_matches[0][0]}")
continue
# If no exact match, try fuzzy search
cursor.execute("SELECT name FROM artist LIMIT 1000")
artists = [row[0] for row in cursor.fetchall()]
# Find best match
best_match = None
best_score = 0
for db_artist in artists:
score = fuzz.ratio(artist.lower(), db_artist.lower())
if score > best_score:
best_score = score
best_match = db_artist
if best_score >= 80:
print(f" ✅ Found: {best_match} (score: {best_score})")
else:
print(f" ❌ No good match found (best: {best_match} with score {best_score})")
if __name__ == "__main__":
print("🧪 Testing MusicBrainz Database Connection from Docker")
print("=" * 60)
success = test_db_connection()
if success:
print("\n✅ Database connection and fuzzy search test completed!")
else:
print("\n❌ Database connection test failed")
sys.exit(1)

87
src/tests/unit/test_eazy_e.py Normal file
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@ -0,0 +1,87 @@
#!/usr/bin/env python3
"""
Test specifically for Eazy-E to see why it's matching to Sleazy-E.
"""
import psycopg2
import psycopg2.extras
# Database configuration
DB_HOST = "172.18.0.2"
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
def test_eazy_e():
"""Test Eazy-E specifically."""
print("🔍 Testing Eazy-E specifically...")
try:
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=10
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("✅ Connected to database")
# Test 1: Search for Eazy-E
print("\n1. Searching for 'Eazy-E'...")
cursor.execute("SELECT name, gid FROM artist WHERE name = 'Eazy-E' LIMIT 5")
eazy_e_results = cursor.fetchall()
print(f" Found {len(eazy_e_results)} exact matches:")
for result in eazy_e_results:
print(f" {result['name']} (MBID: {result['gid']})")
# Test 2: Search for Sleazy-E
print("\n2. Searching for 'Sleazy-E'...")
cursor.execute("SELECT name, gid FROM artist WHERE name = 'Sleazy-E' LIMIT 5")
sleazy_e_results = cursor.fetchall()
print(f" Found {len(sleazy_e_results)} exact matches:")
for result in sleazy_e_results:
print(f" {result['name']} (MBID: {result['gid']})")
# Test 3: Fuzzy search for Eazy-E
print("\n3. Fuzzy search for 'Eazy-E'...")
cursor.execute("SELECT name, gid FROM artist WHERE name ILIKE '%Eazy-E%' LIMIT 10")
fuzzy_results = cursor.fetchall()
print(f" Found {len(fuzzy_results)} fuzzy matches:")
for result in fuzzy_results:
print(f" {result['name']} (MBID: {result['gid']})")
# Test 4: Search for Boyz n The Hood
print("\n4. Searching for 'Boyz n The Hood'...")
cursor.execute("SELECT name, gid FROM recording WHERE name ILIKE '%Boyz n The Hood%' LIMIT 5")
recording_results = cursor.fetchall()
print(f" Found {len(recording_results)} recordings:")
for result in recording_results:
print(f" {result['name']} (MBID: {result['gid']})")
# Get artist for this recording
cursor.execute("""
SELECT a.name, a.gid
FROM recording r
JOIN artist_credit_name acn ON r.artist_credit = acn.artist_credit
JOIN artist a ON acn.artist = a.id
WHERE r.gid = %s
LIMIT 1
""", (result['gid'],))
artist_result = cursor.fetchone()
if artist_result:
print(f" Artist: {artist_result['name']} (MBID: {artist_result['gid']})")
cursor.close()
connection.close()
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_eazy_e()

63
src/tests/unit/test_find_artist.py Normal file
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#!/usr/bin/env python3
"""
Test the find_artist_mbid method.
"""
import sys
import os
sys.path.append('/app/src')
# Add the src directory to Python path
sys.path.insert(0, '/app/src')
def test_find_artist():
"""Test the find_artist_mbid method."""
print("🔍 Testing find_artist_mbid method...")
try:
# Import the cleaner
from cli.main import MusicBrainzCleaner
# Initialize cleaner
cleaner = MusicBrainzCleaner(use_database=True)
# Test with AC/DC
print("1. Testing find_artist_mbid with 'AC/DC'...")
artist_mbid = cleaner.find_artist_mbid('AC/DC')
print(f" Result: {artist_mbid}")
print(f" Type: {type(artist_mbid)}")
if artist_mbid:
print(f" Is string: {isinstance(artist_mbid, str)}")
print(f" Length: {len(artist_mbid)}")
# Test if we can call .lower() on it
try:
lower_mbid = artist_mbid.lower()
print(f" Lower: {lower_mbid}")
except Exception as e:
print(f" Error calling .lower(): {e}")
# Test with a collaboration
print("\n2. Testing find_artist_mbid with 'Afrojack ft. Chris Brown'...")
collab_mbid = cleaner.find_artist_mbid('Afrojack ft. Chris Brown')
print(f" Result: {collab_mbid}")
print(f" Type: {type(collab_mbid)}")
if collab_mbid:
print(f" Is string: {isinstance(collab_mbid, str)}")
# Test if we can call .lower() on it
try:
lower_mbid = collab_mbid.lower()
print(f" Lower: {lower_mbid}")
except Exception as e:
print(f" Error calling .lower(): {e}")
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_find_artist()

78
src/tests/unit/test_fuzzy_eazy_e.py Normal file
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#!/usr/bin/env python3
"""
Test the fuzzy search specifically for Eazy-E.
"""
import psycopg2
import psycopg2.extras
from fuzzywuzzy import fuzz
# Database configuration
DB_HOST = "172.18.0.2"
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
def test_fuzzy_eazy_e():
"""Test fuzzy search for Eazy-E."""
print("🔍 Testing fuzzy search for Eazy-E...")
try:
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=10
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("✅ Connected to database")
# Get all artists that might match Eazy-E
print("\n1. Getting potential matches for 'Eazy-E'...")
cursor.execute("SELECT name, gid FROM artist WHERE name ILIKE '%Eazy%' OR name ILIKE '%Sleazy%' LIMIT 20")
artists = cursor.fetchall()
print(f" Found {len(artists)} potential matches:")
for artist in artists:
score = fuzz.ratio('Eazy-E'.lower(), artist['name'].lower())
print(f" {artist['name']} (MBID: {artist['gid']}) - Score: {score}")
# Find the best match
best_match = None
best_score = 0
for artist in artists:
score = fuzz.ratio('Eazy-E'.lower(), artist['name'].lower())
if score > best_score:
best_score = score
best_match = artist
print(f"\n2. Best match: {best_match['name']} (Score: {best_score})")
# Check if there's an Eazy-E with different dash
print("\n3. Checking for Eazy-E with different dash character...")
cursor.execute("SELECT name, gid FROM artist WHERE name LIKE '%Eazy%E%' LIMIT 10")
eazy_variants = cursor.fetchall()
print(f" Found {len(eazy_variants)} Eazy-E variants:")
for artist in eazy_variants:
print(f" '{artist['name']}' (MBID: {artist['gid']})")
# Show the exact characters
for i, char in enumerate(artist['name']):
if char in ['-', '‐', '–', '—']:
print(f" Character {i}: '{char}' (ord: {ord(char)})")
cursor.close()
connection.close()
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_fuzzy_eazy_e()

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src/tests/unit/test_hyphenated_artists.py Normal file
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#!/usr/bin/env python3
"""
Test to see what legitimate hyphenated artists exist in the database.
"""
import psycopg2
import psycopg2.extras
# Database configuration
DB_HOST = "172.18.0.2"
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
def test_hyphenated_artists():
"""Test hyphenated artists in the database."""
print("🔍 Testing hyphenated artists in the database...")
try:
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=10
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("✅ Connected to database")
# Find artists with hyphens
print("\n1. Artists with hyphens (first 20):")
cursor.execute("SELECT name, gid FROM artist WHERE name LIKE '%-%' LIMIT 20")
hyphenated = cursor.fetchall()
for artist in hyphenated:
print(f" {artist['name']} (MBID: {artist['gid']})")
# Test specific cases
test_cases = [
"Eazy-E",
"AC-DC",
"D-A-D",
"E-40",
"M-1",
"T-Pain"
]
print(f"\n2. Testing specific hyphenated artists:")
for test_case in test_cases:
print(f"\n Testing: {test_case}")
# Exact match
cursor.execute("SELECT name, gid FROM artist WHERE name = %s LIMIT 1", (test_case,))
exact = cursor.fetchone()
if exact:
print(f" Exact match: {exact['name']} (MBID: {exact['gid']})")
else:
print(f" No exact match")
# Case-insensitive match
cursor.execute("SELECT name, gid FROM artist WHERE LOWER(name) = LOWER(%s) LIMIT 1", (test_case,))
case_insensitive = cursor.fetchone()
if case_insensitive:
print(f" Case-insensitive match: {case_insensitive['name']} (MBID: {case_insensitive['gid']})")
else:
print(f" No case-insensitive match")
# ILIKE match
cursor.execute("SELECT name, gid FROM artist WHERE name ILIKE %s LIMIT 3", (f"%{test_case}%",))
ilike_matches = cursor.fetchall()
print(f" ILIKE matches ({len(ilike_matches)}):")
for match in ilike_matches:
print(f" {match['name']} (MBID: {match['gid']})")
cursor.close()
connection.close()
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_hyphenated_artists()

73
src/tests/unit/test_ilike_eazy_e.py Normal file
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#!/usr/bin/env python3
"""
Test the ILIKE query that's causing the Eazy-E to match Sleazy-E.
"""
import psycopg2
import psycopg2.extras
# Database configuration
DB_HOST = "172.18.0.2"
DB_PORT = 5432
DB_NAME = "musicbrainz_db"
DB_USER = "musicbrainz"
DB_PASSWORD = "musicbrainz"
def test_ilike_eazy_e():
"""Test the ILIKE query for Eazy-E."""
print("🔍 Testing ILIKE query for Eazy-E...")
try:
connection = psycopg2.connect(
host=DB_HOST,
port=DB_PORT,
database=DB_NAME,
user=DB_USER,
password=DB_PASSWORD,
connect_timeout=10
)
cursor = connection.cursor(cursor_factory=psycopg2.extras.RealDictCursor)
print("✅ Connected to database")
# Test the exact query from fuzzy_search_artist
main_artist = "Eazy-E"
query = "SELECT name, gid FROM artist WHERE name ILIKE %s LIMIT 50"
pattern = f"%{main_artist}%"
print(f"\n1. Testing query: {query}")
print(f" Pattern: {pattern}")
cursor.execute(query, (pattern,))
artists = cursor.fetchall()
print(f"\n2. Found {len(artists)} matches:")
for artist in artists:
print(f" {artist['name']} (MBID: {artist['gid']})")
# Test fuzzy ratio on these results
print(f"\n3. Fuzzy ratio scores for 'Eazy-E':")
from fuzzywuzzy import fuzz
best_match = None
best_score = 0
for artist in artists:
score = fuzz.ratio(main_artist.lower(), artist['name'].lower())
print(f" {artist['name']} - Score: {score}")
if score > best_score:
best_score = score
best_match = artist
print(f"\n4. Best match: {best_match['name']} (Score: {best_score})")
cursor.close()
connection.close()
except Exception as e:
print(f"❌ Error: {e}")
import traceback
traceback.print_exc()
if __name__ == "__main__":
test_ilike_eazy_e()

10
src/tests/unit/test_sclub7.json Normal file
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[
{
"artist": "S Club 7",
"disabled": false,
"favorite": false,
"guid": "test-sclub7",
"path": "test/path/sclub7.mp4",
"title": "Never Had a Dream Come True"
}
]

76
src/tests/unit/test_simple_db.py Normal file
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#!/usr/bin/env python3
"""
Simple database connection test for MusicBrainz.
"""
import psycopg2
import sys
def test_simple_connection():
"""Test basic database connection."""
try:
# Try to connect to the database
print("🔌 Testing database connection...")
# Try different connection methods
hosts = ["127.0.0.1", "localhost", "172.18.0.2", "db"]
for host in hosts:
print(f" Trying {host}...")
try:
conn = psycopg2.connect(
host=host,
port=5432,
database="musicbrainz",
user="musicbrainz",
password="musicbrainz",
connect_timeout=5
)
print(f" ✅ Connected to {host}")
# Test basic query
cursor = conn.cursor()
cursor.execute("SELECT version();")
version = cursor.fetchone()[0]
print(f" 📊 PostgreSQL version: {version.split(',')[0]}")
# Check if any tables exist
cursor.execute("""
SELECT table_name
FROM information_schema.tables
WHERE table_schema = 'public'
LIMIT 5;
""")
tables = cursor.fetchall()
if tables:
print(f" 📋 Found {len(tables)} tables:")
for table in tables:
print(f" - {table[0]}")
else:
print(" ⚠️ No tables found in database")
conn.close()
return True
except Exception as e:
print(f" ❌ Failed to connect to {host}: {e}")
continue
except Exception as e:
print(f"❌ Database connection failed: {e}")
return False
if __name__ == "__main__":
print("🧪 Simple MusicBrainz Database Connection Test")
print("=" * 50)
success = test_simple_connection()
if success:
print("\n✅ Database connection test completed!")
print("💡 The database is accessible but may be empty")
print("💡 Wait for MusicBrainz data import to complete")
else:
print("\n❌ Database connection test failed")
sys.exit(1)