AI Music Recommender

An intelligent music recommendation system that uses Amazon Bedrock AI to analyze user listening patterns and generate personalized playlists through event-driven architecture.

📊 System Architecture

For a complete visual diagram and detailed explanation of how the system works, see:

• 📋 SYSTEM_OVERVIEW.md - Complete architecture diagram and component explanations
• 🏗️ ARCHITECTURE.md - Technical architecture details
• 🧪 TESTING_SUMMARY.md - Testing results and cost analysis

🎯 Quick Overview

🎧 User Listening → 🎵 Spotify API → ⚡ EventBridge → 🧠 AI Analysis → 🎵 Personalized Playlists

Components

Core Services

• EventBridge: Central event router for music events
• Lambda Functions: AI processing and playlist generation
• Amazon Bedrock: Claude/Titan models for music analysis
• SQS Queues: Async processing and retry handling
• DynamoDB: User profiles and music preferences storage
• SNS: User notifications and recommendations

External Integrations

• Spotify Web API
• Apple Music API
• Last.fm API (optional)

💰 Cost Analysis

Current Testing Status

• Spent so far: $0.00 (all within AWS free tier)
• Projected for 20 playlists: $10-15 (with Bedrock access)
• Cost optimization: Using Titan model (70% cheaper than Claude)

Cost Breakdown

Component	Free Tier	With Bedrock
Lambda	$0.00	$0.00
DynamoDB	$0.00	$0.00
EventBridge	$0.00	$0.00
SQS	$0.00	$0.00
Bedrock AI	$0.00	~$0.50/song

See TESTING_SUMMARY.md for detailed cost analysis.

🚀 Quick Start Testing

Option 1: Interactive Testing (Recommended)

cd /home/ubuntu/ai-music-recommender
python3 test-interface.py

Option 2: Connect Real Spotify Data

# Set up Spotify credentials (see SYSTEM_OVERVIEW.md for details)
export SPOTIFY_CLIENT_ID="your_client_id"
export SPOTIFY_CLIENT_SECRET="your_client_secret"

# Install required package
pip install spotipy

# Sync your real listening history
python3 spotify-connector.py

Option 3: Manual Testing

# Send custom test events
python3 manual-test.py

# Run cost analysis
python3 test-cost-optimized.py

🎯 Key Features

✅ Real-time Music Analysis - Process listening activity as it happens
✅ AI-Powered Insights - Bedrock AI analyzes mood, genre, and patterns
✅ Personalized Playlists - Generate custom playlists based on your taste
✅ Cost-Optimized - Smart model selection (Titan vs Claude)
✅ Event-Driven Architecture - Scalable and fault-tolerant
✅ Cross-Platform Support - Works with Spotify, Apple Music, and more
✅ Smart Notifications - Get notified when new playlists are ready

📁 Project Structure

ai-music-recommender/
├── 📋 SYSTEM_OVERVIEW.md          # Complete system diagram & explanation
├── 🏗️ ARCHITECTURE.md            # Technical architecture details  
├── 🧪 TESTING_SUMMARY.md          # Testing results & cost analysis
├── 🚀 deploy.sh                   # Automated deployment script
├── 🧪 test-interface.py           # Interactive testing interface
├── 🎵 spotify-connector.py        # Real Spotify data integration
├── infrastructure/
│   ├── cloudformation.yaml        # Full AWS infrastructure
│   └── minimal-testing.yaml       # Cost-optimized testing version
├── lambda-functions/
│   ├── listening-analyzer/        # AI-powered music analysis
│   ├── playlist-generator/        # Playlist creation logic
│   └── notification-sender/       # User notifications
└── tests/
    └── sample-events/             # Test data and examples

🔧 Monitoring & Management

Check System Status

# View stored data
aws dynamodb scan --table-name ai-music-recommender-user-profiles --limit 5

# Monitor Lambda logs  
aws logs tail /aws/lambda/ai-music-recommender-listening-analyzer --follow

# Check costs
aws ce get-cost-and-usage --time-period Start=2024-06-01,End=2024-06-30 --granularity DAILY --metrics BlendedCost

Clean Up Resources

# Delete all resources when done testing
aws cloudformation delete-stack --stack-name ai-music-recommender-testing-stack

📚 Documentation

• SYSTEM_OVERVIEW.md - Visual architecture diagram and complete system explanation
• ARCHITECTURE.md - Detailed technical architecture and design patterns
• TESTING_SUMMARY.md - Testing results, cost analysis, and deployment status
• spotify-setup.md - Step-by-step Spotify integration guide

🎉 Ready to Test!

The system is deployed and ready for cost-optimized testing. Start with the interactive interface:

python3 test-interface.py

Estimated testing cost: $10-15 for 20 AI-generated playlists (vs $180+ with non-optimized setup)