Music has always been a powerful connector of people. We realized that someone's music taste can tell you a lot about their personality, emotions, and potential compatibility. Flirtify was born from the idea that shared musical interests could be the foundation for meaningful connections.

Flirtify is a sophisticated music-based matching platform that:

• Analyzes users' Spotify listening habits
• Compares musical preferences using advanced algorithms
• Generates compatibility scores based on: -Shared favorite artists (30%) Common tracks (20%) Genre overlap (15%) Music characteristic similarity (35%) Provides detailed match insights with specific shared interests Stores match history for future reference

Tech Stack:

Frontend: (Planned)

• React
• TailwindCSS
• Spotify Web Playback SDK

Backend:

• FastAPI (Python)
• Firebase/Firestore
• Spotify API
• NumPy & Scikit-learn

Authentication System (auth.py): Spotify OAuth integration Secure token management User session handling Matching Algorithm (match.py): Advanced similarity calculations Audio feature analysis Weighted scoring system Database Architecture (database.py): Firebase integration Real-time data updates Secure credential management

Spotify API Integration: Complex OAuth flow Token refresh management Rate limiting considerations Firebase Setup: Credential management Environment variable configuration Real-time database synchronization Algorithm Development: Balancing different matching criteria Handling edge cases Optimizing performance

Sophisticated Matching System: Multi-factor analysis Detailed compatibility explanations Real-time processing Robust Backend Architecture: Clean code structure Efficient error handling Scalable design Security Implementation: Secure credential management Protected user data Safe API integration

Technical Skills: FastAPI development Firebase integration Spotify API usage Algorithm design Development Practices: Environment management API documentation Code organization Error handling Project Management: Feature prioritization System architecture Documentation importance

Feature Enhancements: Playlist generation for matches Real-time chat integration Music recommendation system Match history analytics Technical Improvements: Frontend development Mobile app version Performance optimization Enhanced matching algorithms User Experience: Interactive matching visualization Detailed music taste analysis Social features integration Premium features