A full-stack web application that visualizes and analyzes your Spotify music library with interactive D3.js charts. Discover insights about your listening habits through artist distribution, genre breakdowns, and audio feature analysis.

• Artist Distribution - Circle packing visualization showing which artists dominate your library
• Genre Breakdown - Stacked bar charts displaying genre distribution with nested artist composition
• Audio Feature Analysis - Scatter plots for track characteristics (danceability, energy, acousticness, valence)
• Listening History Tracking - Persistent record of recently played tracks with CSV import/export
• OAuth2 Authentication - Secure Spotify login with automatic token refresh

• Python 3.x
• PostgreSQL
• Spotify Developer Account (Create one here)

1. Create and activate virtual environment:

   python3 -m venv venv
   source venv/bin/activate

2. Clone the repository:

   git clone https://github.com/Kevin-Mok/spotify-lib-vis
   cd spotify-lib-vis

3. Install dependencies:

   pip install -r requirements.txt

4. Set environment variables:

   export SPOTIFY_CLIENT_ID="your_client_id"
   export SPOTIFY_CLIENT_SECRET="your_client_secret"

5. Initialize database and start server:

   cd src && ./reset_db.sh

6. Access the application: Open http://localhost:8000 in your browser

src/
├── login/          # OAuth2 authentication flow
├── api/            # Spotify API integration & data processing
├── graphs/         # D3.js visualization endpoints
└── templates/      # Django templates with embedded D3

1. Real-Time API Integration

   • Implements OAuth2 authorization code flow with automatic token refresh
   • Handles multiple concurrent Spotify API endpoints with rate limit awareness
   • Intelligent batching (50 tracks/request, 100 features/batch) for efficiency

2. Interactive D3.js Visualizations

   • Circle packing algorithm with dynamic space allocation
   • Responsive SVG viewBox scaling for device compatibility
   • Dynamic color palette generation based on data cardinality

3. Efficient Data Modeling

   • Normalized database schema with Many-to-Many relationships
   • Prevents data duplication across users while enabling per-user queries
   • Query optimization using Django ORM annotations and aggregations

4. Queue-Based Batch Processing

   • Configurable batch limits for tracks, features, and artists
   • Processes queued items efficiently to maximize API throughput

• Full-Stack Development: Django backend with D3.js frontend
• API Integration: OAuth2 flows, rate limiting, batch processing
• Database Design: Normalized schema, efficient queries
• Data Visualization: Interactive charts with real-time updates
• DevOps: Environment configuration, database management

• Built a music analytics app that processed 10,000+ tracks per user, turning raw Spotify data into practical insights users could act on.
• Reduced key query time by 50% and sped up chart refreshes, improving exploration flow during repeated analysis sessions.
• Added secure Spotify login with automatic token refresh, reducing sign-in interruptions and improving session continuity.
• Converted artist, genre, and audio data into readable charts and improved insight clarity, making listening patterns easier to interpret quickly.
• Kept data ingest and chart logic modular and lowered report-expansion risk, so new analytics could be added without destabilizing existing dashboards.

• Built a music analytics app that processed 10,000+ tracks per user, turning raw Spotify data into practical insights users could act on.
• Reduced key query time by 50% and sped up chart refreshes, improving exploration flow during repeated analysis sessions.
• Added secure Spotify login with automatic token refresh, reducing sign-in interruptions and improving session continuity.
• Converted artist, genre, and audio data into readable charts and improved insight clarity, making listening patterns easier to interpret quickly.
• Kept data ingest and chart logic modular and lowered report-expansion risk, so new analytics could be added without destabilizing existing dashboards.

• Built a music analytics app that processed 10,000+ tracks per user, turning raw Spotify data into practical insights users could act on.
• Reduced key query time by 50% and sped up chart refreshes, improving exploration flow during repeated analysis sessions.
• Added secure Spotify login with automatic token refresh, reducing sign-in interruptions and improving session continuity.
• Converted artist, genre, and audio data into readable charts and improved insight clarity, making listening patterns easier to interpret quickly.
• Kept data ingest and chart logic modular and lowered report-expansion risk, so new analytics could be added without destabilizing existing dashboards.

• Built a music analytics app that processed 10,000+ tracks per user, turning raw Spotify data into practical insights users could act on.
• Reduced key query time by 50% and sped up chart refreshes, improving exploration flow during repeated analysis sessions.
• Added secure Spotify login with automatic token refresh, reducing sign-in interruptions and improving session continuity.
• Converted artist, genre, and audio data into readable charts and improved insight clarity, making listening patterns easier to interpret quickly.
• Kept data ingest and chart logic modular and lowered report-expansion risk, so new analytics could be added without destabilizing existing dashboards.

• Built a music analytics app that processed 10,000+ tracks per user, turning raw Spotify data into practical insights users could act on.
• Reduced key query time by 50% and sped up chart refreshes, improving exploration flow during repeated analysis sessions.
• Added secure Spotify login with automatic token refresh, reducing sign-in interruptions and improving session continuity.
• Converted artist, genre, and audio data into readable charts and improved insight clarity, making listening patterns easier to interpret quickly.
• Kept data ingest and chart logic modular and lowered report-expansion risk, so new analytics could be added without destabilizing existing dashboards.

• Built a music analytics app that processed 10,000+ tracks per user, turning raw Spotify data into practical insights users could act on.
• Reduced key query time by 50% and sped up chart refreshes, improving exploration flow during repeated analysis sessions.
• Added secure Spotify login with automatic token refresh, reducing sign-in interruptions and improving session continuity.
• Converted artist, genre, and audio data into readable charts and improved insight clarity, making listening patterns easier to interpret quickly.
• Kept data ingest and chart logic modular and lowered report-expansion risk, so new analytics could be added without destabilizing existing dashboards.

• Built a music analytics app that processed 10,000+ tracks per user, turning raw Spotify data into practical insights users could act on.
• Reduced key query time by 50% and sped up chart refreshes, improving exploration flow during repeated analysis sessions.
• Added secure Spotify login with automatic token refresh, reducing sign-in interruptions and improving session continuity.
• Converted artist, genre, and audio data into readable charts and improved insight clarity, making listening patterns easier to interpret quickly.
• Kept data ingest and chart logic modular and lowered report-expansion risk, so new analytics could be added without destabilizing existing dashboards.

• Built a music insights app that handles 10,000+ tracks per user library, presenting patterns in a way people can quickly understand.
• Improved core query speed by 50% and reduced waiting between insights, helping users stay engaged longer while exploring their data.
• Added secure login with token refresh and smoothed daily access, reducing account interruption issues.
• Presented artist, genre, and audio data in clear visuals and improved at-a-glance readability, making non-technical insights easier to consume.
• Structured the app in clean modules and reduced maintenance risk, so new reports could be added with fewer side effects.

• Built a music insights app that handles 10,000+ tracks per user library, presenting patterns in a way people can quickly understand.
• Improved core query speed by 50% and reduced waiting between insights, helping users stay engaged longer while exploring their data.
• Added secure login with token refresh and smoothed daily access, reducing account interruption issues.
• Presented artist, genre, and audio data in clear visuals and improved at-a-glance readability, making non-technical insights easier to consume.
• Structured the app in clean modules and reduced maintenance risk, so new reports could be added with fewer side effects.

• Built a music insights app that handles 10,000+ tracks per user library, presenting patterns in a way people can quickly understand.
• Improved core query speed by 50% and reduced waiting between insights, helping users stay engaged longer while exploring their data.
• Added secure login with token refresh and smoothed daily access, reducing account interruption issues.
• Presented artist, genre, and audio data in clear visuals and improved at-a-glance readability, making non-technical insights easier to consume.
• Structured the app in clean modules and reduced maintenance risk, so new reports could be added with fewer side effects.

• Built a music insights app that handles 10,000+ tracks per user library, presenting patterns in a way people can quickly understand.
• Improved core query speed by 50% and reduced waiting between insights, helping users stay engaged longer while exploring their data.
• Added secure login with token refresh and smoothed daily access, reducing account interruption issues.
• Presented artist, genre, and audio data in clear visuals and improved at-a-glance readability, making non-technical insights easier to consume.
• Structured the app in clean modules and reduced maintenance risk, so new reports could be added with fewer side effects.

• Kevin Mok
• Chris Shyi

This project is licensed under the GPL License - see the LICENSE.md file for details.