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align=\"center\"\u003e\r\n  \u003cbr\u003e\r\n  \u003cem\u003e🎧 Your Intelligent Music Companion Powered by AI \u0026 Emotion Analysis\u003c/em\u003e\u003cbr\u003e\r\n  \u003cem\u003eAdvanced recommendation system using transformers, vector databases, and semantic search.\u003c/em\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp align=\"center\"\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/Python-3.9%2B-blue?logo=python\u0026logoColor=white\" /\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/Streamlit-1.30.0-ff4b4b?logo=streamlit\u0026logoColor=white\" /\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/Transformers-4.36-yellow?logo=huggingface\u0026logoColor=white\" /\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/ChromaDB-0.4.22-7b2cbf?logo=databricks\u0026logoColor=white\" /\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/FAISS-1.7.4-00ADD8?logo=meta\u0026logoColor=white\" /\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/Deezer%20API-Connected-00C7F2?logo=deezer\u0026logoColor=white\" /\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/Security-AES--256-green?logo=lock\u0026logoColor=white\" /\u003e\r\n  \u003cimg src=\"https://img.shields.io/badge/License-MIT-green\" /\u003e\r\n\u003c/p\u003e\r\n\r\n\u003cp align=\"center\"\u003e\r\n  \u003cstrong\u003eEnglish\u003c/strong\u003e | \u003ca href=\"README_ES.md\"\u003eEspañol\u003c/a\u003e\r\n\u003c/p\u003e\r\n\r\n---\r\n\r\n## 📋 Table of Contents\r\n\r\n- [Overview](#-overview)\r\n- [Features](#-features)\r\n- [Technical Architecture](#-technical-architecture)\r\n- [Vector Database Implementation](#-vector-database-implementation)\r\n- [Installation](#-installation)\r\n- [Configuration](#-configuration)\r\n- [Usage](#-usage)\r\n- [Testing](#-testing)\r\n- [Security](#-security)\r\n- [API Documentation](#-api-documentation)\r\n- [Project Structure](#-project-structure)\r\n- [Technologies](#-technologies)\r\n- [Authors](#-authors)\r\n- [License](#-license)\r\n\r\n---\r\n\r\n## 🌟 Overview\r\n\r\n**RhythmAI** is a state-of-the-art AI-powered music recommendation system that understands your emotions and recommends the perfect soundtrack for your mood. Built with cutting-edge AI technologies, it combines emotional intelligence, semantic search, and vector databases to deliver personalized music experiences.\r\n\r\n### How It Works\r\n\r\n```\r\nUser Input → Emotion Analysis → Vectorization → Semantic Search → Recommendations → Memory Learning\r\n```\r\n\r\n1. 🗣️ **User describes their emotional state** (natural language)\r\n2. 🧠 **AI analyzes sentiment \u0026 emotions** using multilingual XLM-RoBERTa + semantic similarity\r\n3. 🔢 **Text vectorization** with Sentence-BERT (384-dimensional embeddings)\r\n4. 🔍 **Semantic similarity search** in ChromaDB/FAISS vector database\r\n5. 🎵 **Music recommendations** from local vector database\r\n6. 💾 **Learning system** remembers preferences for future sessions\r\n\r\n---\r\n\r\n## ✨ Features\r\n\r\n### 🎭 Advanced Emotion Analysis\r\n- **Hybrid Detection**: Multilingual sentiment analysis + semantic similarity mapping\r\n- **Confidence Scoring**: Each emotion detection includes confidence percentage\r\n- **Multi-Emotion Detection**: Recognizes complex emotional states through embeddings\r\n- **Energy \u0026 Valence Dimensions**: Quantifies musical mood on two axes (0-1 scale)\r\n\r\n### 🔍 Vector Database \u0026 Semantic Search\r\n- **Dual Vector Store Support**: Choose between **ChromaDB** or **FAISS**\r\n- **High-Dimensional Storage**: 384-dimensional embedding vectors\r\n- **Cosine Similarity Search**: Find semantically similar songs\r\n- **Batch Vectorization**: Efficient processing of large playlists\r\n- **Metadata Filtering**: Search by genre, mood, or context\r\n- **HNSW Indexing**: Fast approximate nearest neighbor search\r\n- **Performance**: FAISS is 10-100x faster for large datasets\r\n\r\n### 🎨 Multimodal Vectorization\r\n- **Image Analysis**: CLIP-based album cover vectorization\r\n- **Visual Search**: Find songs by visual style similarity\r\n- **Lyrics Vectorization**: Semantic search through song lyrics (Genius API)\r\n- **Theme Detection**: Automatic extraction of lyrical themes\r\n- **Cross-Modal Search**: Text → Image, Image → Image, Lyrics → Songs\r\n\r\n### 🔐 Enterprise-Grade Security\r\n- **AES-256 Encryption**: Military-grade data encryption\r\n- **PBKDF2 Key Derivation**: Secure password-based key generation (100,000 iterations)\r\n- **Encrypted Storage**: Secure user profiles and conversation history\r\n- **Data Privacy**: No sensitive information stored in plaintext\r\n\r\n### 🎵 Intelligent Recommendation System\r\n- **Context-Aware**: Understands situations (workout, study, party, sleep)\r\n- **Preference Learning**: Improves recommendations over time\r\n- **Genre Mapping**: Suggests optimal genres based on emotions\r\n- **Deezer Integration**: Music source via Deezer API\r\n- **Fallback Mechanisms**: Robust error handling with alternative strategies\r\n\r\n### 💬 Professional User Interface\r\n- **Modern Design**: Gradient backgrounds, glassmorphism effects\r\n- **Responsive Layout**: Works on desktop and mobile\r\n- **Animated Elements**: Smooth transitions and hover effects\r\n- **Audio Previews**: Listen to 30-second track previews\r\n- **Visual Analytics**: Emotion breakdowns and statistics\r\n- **Real-time Feedback**: Progress indicators and loading states\r\n\r\n---\r\n\r\n## 🏗️ Technical Architecture\r\n\r\n### System Architecture Diagram\r\n\r\n```\r\n┌─────────────────────────────────────────────────────────────────┐\r\n│                      Frontend Layer (Streamlit)                 │\r\n│  ┌──────────────┬─────────────────┬──────────────────────────┐ │\r\n│  │  User Input  │  Visualizations │  Playback \u0026 Navigation   │ │\r\n│  │  Validation  │  \u0026 Analytics    │  Audio Preview Player    │ │\r\n│  └──────────────┴─────────────────┴──────────────────────────┘ │\r\n└───────────────────────────┬─────────────────────────────────────┘\r\n                            │\r\n┌───────────────────────────▼─────────────────────────────────────┐\r\n│              Application Layer (MusicRecommender)               │\r\n│  ┌──────────────────────────────────────────────────────────┐  │\r\n│  │  • Request Orchestration    • Context Management         │  │\r\n│  │  • Preference Adjustment    • Response Generation        │  │\r\n│  │  • Memory Integration       • Error Handling             │  │\r\n│  └──────────────────────────────────────────────────────────┘  │\r\n└─────┬──────────┬──────────────┬──────────────┬──────────┬──────┘\r\n      │          │              │              │          │\r\n┌─────▼───┐ ┌───▼───────┐ ┌────▼────────┐ ┌──▼──────┐ ┌▼──────────┐\r\n│Emotion  │ │ Embedding │ │  Vector DB  │ │ Deezer  │ │  Security │\r\n│Analyzer │ │   Model   │ │  (ChromaDB) │ │   API   │ │  Module   │\r\n│         │ │           │ │             │ │         │ │           │\r\n│XLM-     │ │Sentence-  │ │ HNSW Index  │ │ Web API │ │  AES-256  │\r\n│RoBERTa  │ │BERT       │ │ Cosine Sim  │ │ Client  │ │  PBKDF2   │\r\n└─────────┘ └───────────┘ └─────────────┘ └─────────┘ └───────────┘\r\n      │            │              │                │           │\r\n      └────────────┴──────────────┴────────────────┴───────────┘\r\n                            │\r\n┌───────────────────────────▼─────────────────────────────────────┐\r\n│              Memory \u0026 Context Layer                             │\r\n│  ┌──────────────────────────────────────────────────────────┐  │\r\n│  │  • Conversation History    • User Profiles               │  │\r\n│  │  • Preference Tracking     • Emotion Patterns            │  │\r\n│  │  • Context Enrichment      • Statistical Analysis        │  │\r\n│  └──────────────────────────────────────────────────────────┘  │\r\n└─────────────────────────────────────────────────────────────────┘\r\n```\r\n\r\n### Data Flow Architecture\r\n\r\n```mermaid\r\ngraph LR\r\n    A[User Input] --\u003e B[Emotion Analysis]\r\n    B --\u003e C[Embedding Generation]\r\n    C --\u003e D[Vector Search]\r\n    C --\u003e E[Deezer API]\r\n    D --\u003e F[Result Fusion]\r\n    E --\u003e F\r\n    F --\u003e G[Memory Update]\r\n    G --\u003e H[Response to User]\r\n```\r\n\r\n---\r\n\r\n## 💾 Vector Database Implementation\r\n\r\n### Overview\r\n\r\nRhythmAI supports **dual vector database** implementations: **ChromaDB** (default) and **FAISS**, providing flexibility between ease-of-use and performance.\r\n\r\n### Choosing a Vector Store\r\n\r\n| Feature | ChromaDB | FAISS |\r\n|---------|----------|-------|\r\n| **Speed** | Fast (\u003c50ms for 10K songs) | Ultra-fast (10-100x faster) |\r\n| **Metadata Filtering** | Native support | Manual implementation |\r\n| **Setup** | Zero configuration | Minimal configuration |\r\n| **Scalability** | Good (10K-100K songs) | Excellent (millions of vectors) |\r\n| **Memory Usage** | Moderate | Low |\r\n| **Best For** | General use, development | Production, large datasets |\r\n\r\nSwitch between stores by setting `VECTOR_STORE=chroma` or `VECTOR_STORE=faiss` in `.env`.\r\n\r\n### Key Components\r\n\r\n#### 1. **Embedding Generation**\r\n```python\r\nModel: sentence-transformers/all-MiniLM-L6-v2\r\nDimensions: 384\r\nTraining: Multi-lingual, optimized for semantic similarity\r\n```\r\n\r\n#### 2. **Vector Storage**\r\n- **Engine**: ChromaDB with HNSW indexing\r\n- **Distance Metric**: Cosine similarity\r\n- **Persistence**: Local filesystem storage\r\n- **Metadata**: Genre, artist, mood, URL, description\r\n\r\n#### 3. **Search Operations**\r\n\r\n**Semantic Search Example:**\r\n```python\r\nquery = \"I need energetic music for working out\"\r\nembedding = embedder.encode(query)  # 384D vector\r\nresults = vector_db.search(embedding, n_results=10)\r\n# Returns: Top 10 most similar songs by cosine distance\r\n```\r\n\r\n**Filtered Search:**\r\n```python\r\n# Search within specific genre\r\nresults = vector_db.search_by_genre(\r\n    query_embedding=embedding,\r\n    genre=\"electronic\",\r\n    n_results=5\r\n)\r\n```\r\n\r\n### Vectorization Process\r\n\r\n1. **Text Description** → Clean and normalize\r\n2. **Tokenization** → BERT tokenizer\r\n3. **Encoding** → 384D sentence embedding\r\n4. **Normalization** → L2 norm for cosine similarity\r\n5. **Storage** → ChromaDB with metadata\r\n\r\n### Performance Metrics\r\n\r\n- **Embedding Speed**: ~100 texts/second (CPU)\r\n- **Search Latency**: \u003c50ms for 10K songs\r\n- **Index Size**: ~1.5MB per 1000 songs\r\n- **Precision@10**: \u003e85% relevance\r\n\r\n---\r\n\r\n## 🚀 Installation\r\n\r\n### Prerequisites\r\n\r\n- Python 3.9 or higher\r\n- pip package manager\r\n- 4GB RAM minimum (8GB recommended)\r\n- 2GB free disk space\r\n\r\n### Step 1: Clone Repository\r\n\r\n```bash\r\ngit clone https://github.com/cesarsm24/rhythmai.git\r\ncd rhythmai\r\n```\r\n\r\n### Step 2: Create Virtual Environment\r\n\r\n```bash\r\n# Create virtual environment\r\npython -m venv venv\r\n\r\n# Activate (Windows)\r\nvenv\\Scripts\\activate\r\n\r\n# Activate (macOS/Linux)\r\nsource venv/bin/activate\r\n```\r\n\r\n### Step 3: Install Dependencies\r\n\r\n```bash\r\npip install -r requirements.txt\r\n```\r\n\r\n**First-time installation** will download AI models (~1GB):\r\n- `sentence-transformers/all-MiniLM-L6-v2` (80MB)\r\n- `cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual` (560MB)\r\n\r\n### Step 4: Configure Environment\r\n\r\nCreate `.env` file in project root:\r\n\r\n```env\r\n# Vector Store Selection\r\nVECTOR_STORE=chroma  # Options: \"chroma\" or \"faiss\"\r\nCHROMA_DB_PATH=./chroma_db\r\nFAISS_DB_PATH=./faiss_db\r\n\r\n# AI Models\r\nEMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2\r\nEMOTION_MODEL=cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual\r\n\r\n# Memory Configuration\r\nMEMORY_PATH=./memory\r\nMAX_CONVERSATION_HISTORY=50\r\nMEMORY_WINDOW=10\r\n\r\n# Security (Production)\r\nRHYTHM_MASTER_KEY=your_secure_master_key_change_in_production\r\n\r\n# Hardware (Optional)\r\nUSE_GPU=false  # Set to true for CUDA-enabled GPU\r\n```\r\n\r\n### Step 5: Populate Vector Database\r\n\r\n```bash\r\npython scripts/populate_db.py\r\n```\r\n\r\nThis script will:\r\n- Fetch playlists from Deezer (configured moods)\r\n- Generate embeddings for songs\r\n- Store vectors in ChromaDB/FAISS\r\n- Takes approximately 5-10 minutes\r\n\r\n---\r\n\r\n## ⚙️ Configuration\r\n\r\n### Environment Variables\r\n\r\n| Variable | Description | Default |\r\n|----------|-------------|---------|\r\n| `VECTOR_STORE` | Vector database (\"chroma\" or \"faiss\") | `chroma` |\r\n| `CHROMA_DB_PATH` | ChromaDB storage path | `./chroma_db` |\r\n| `FAISS_DB_PATH` | FAISS storage path | `./faiss_db` |\r\n| `EMBEDDING_MODEL` | Sentence transformer model | `sentence-transformers/all-MiniLM-L6-v2` |\r\n| `EMOTION_MODEL` | Emotion analysis model | `cardiffnlp/twitter-xlm-roberta-base-sentiment-multilingual` |\r\n| `MEMORY_PATH` | User memory path | `./memory` |\r\n| `MAX_CONVERSATION_HISTORY` | Max stored conversations | `50` |\r\n| `MEMORY_WINDOW` | Context window size | `10` |\r\n| `USE_GPU` | Enable GPU acceleration | `false` |\r\n| `RHYTHM_MASTER_KEY` | Encryption master key | `default_key` |\r\n\r\n---\r\n\r\n## 💻 Usage\r\n\r\n### Starting the Application\r\n\r\n```bash\r\nstreamlit run app.py\r\n```\r\n\r\nApplication opens at: `http://localhost:8501`\r\n\r\n### Using the System\r\n\r\n#### Step 1: Describe Your Mood\r\n\r\nExamples of effective prompts:\r\n\r\n**Emotional States:**\r\n- \"I'm feeling energetic and want to dance!\"\r\n- \"I'm sad and need calming music\"\r\n- \"Feeling nostalgic about the past\"\r\n\r\n**Activity-Based:**\r\n- \"Need focus music for studying\"\r\n- \"High-intensity workout playlist\"\r\n- \"Relaxing music for meditation\"\r\n\r\n**Context-Specific:**\r\n- \"Driving on a long road trip\"\r\n- \"Hosting a dinner party\"\r\n- \"Getting ready for bed\"\r\n\r\n#### Step 2: Get Recommendations\r\n\r\nClick **\"🎵 Get Recommendations\"** button\r\n\r\nThe system will:\r\n1. Analyze your emotional state (2-3 seconds)\r\n2. Search vector database for similar songs\r\n3. Return personalized recommendations\r\n4. Update your preference profile\r\n\r\n#### Step 3: Explore Results\r\n\r\n**Emotional Analysis Section:**\r\n- Dominant emotion with confidence score\r\n- Energy and positivity metrics\r\n- Top 5 detected emotions\r\n- Recommended genres\r\n\r\n**Personalized Playlist:**\r\n- 8-10 recommended tracks\r\n- Album artwork\r\n- Artist information\r\n- 30-second audio previews\r\n- Direct Deezer links\r\n\r\n**Contextual Playlists:**\r\n- Curated playlists matching your mood\r\n- Context-specific suggestions\r\n\r\n---\r\n\r\n## 🧪 Testing\r\n\r\nRhythmAI includes comprehensive unit and integration tests to ensure code quality and reliability.\r\n\r\n### Running Tests\r\n\r\n```bash\r\n# Run all tests\r\npytest\r\n\r\n# Run with coverage report\r\npytest --cov=rhythmai --cov-report=html\r\n\r\n# Run specific test categories\r\npytest tests/unit/                    # Unit tests only\r\npytest tests/integration/             # Integration tests only\r\n```\r\n\r\n### Test Structure\r\n\r\n```\r\ntests/\r\n├── unit/                             # Unit tests\r\n│   ├── test_embeddings.py           # Embedding model tests\r\n│   ├── test_emotion_analyzer.py     # Emotion analysis tests\r\n│   └── test_vector_stores.py        # Vector store tests\r\n│\r\n└── integration/                      # Integration tests\r\n    ├── test_music_recommender.py    # Full system tests\r\n    └── test_end_to_end.py           # End-to-end workflows\r\n```\r\n\r\n### Running Tests by Marker\r\n\r\n```bash\r\n# Run only unit tests\r\npytest -m unit\r\n\r\n# Run only integration tests\r\npytest -m integration\r\n\r\n# Run slow tests\r\npytest -m slow\r\n```\r\n\r\n### Viewing Coverage Reports\r\n\r\n```bash\r\n# Generate HTML coverage report\r\npytest --cov=rhythmai --cov-report=html\r\n\r\n# Open report in browser (macOS)\r\nopen htmlcov/index.html\r\n\r\n# Open report in browser (Linux)\r\nxdg-open htmlcov/index.html\r\n```\r\n\r\n### Pre-commit Testing\r\n\r\nBefore committing code, ensure all tests pass:\r\n\r\n```bash\r\n# Install development dependencies\r\npip install -r requirements-dev.txt\r\n\r\n# Run linting and formatting checks\r\nflake8 rhythmai scripts app.py\r\nblack --check rhythmai scripts app.py\r\nisort --check-only rhythmai scripts app.py\r\n\r\n# Run tests\r\npytest --cov=rhythmai\r\n\r\n# All in one command\r\nflake8 rhythmai \u0026\u0026 black --check rhythmai \u0026\u0026 pytest --cov=rhythmai\r\n```\r\n\r\n---\r\n\r\n## 🔐 Security\r\n\r\n### Encryption Implementation\r\n\r\nRhythmAI implements military-grade encryption for sensitive data:\r\n\r\n#### Algorithm Details\r\n- **Cipher**: AES-256 (Advanced Encryption Standard)\r\n- **Mode**: Fernet (symmetric encryption)\r\n- **Key Derivation**: PBKDF2-HMAC-SHA256\r\n- **Iterations**: 100,000 (protection against brute force)\r\n- **Salt**: Static per installation (customize in production)\r\n\r\n### Usage Examples\r\n\r\n#### Encrypt String Data\r\n\r\n```python\r\nfrom rhythmai.utils.security import DataEncryption\r\n\r\nencryptor = DataEncryption(\"your_master_password\")\r\n\r\n# Encrypt\r\nsensitive_data = \"user_api_token_xyz123\"\r\nencrypted = encryptor.encrypt_string(sensitive_data)\r\n\r\n# Decrypt\r\ndecrypted = encryptor.decrypt_string(encrypted)\r\n```\r\n\r\n#### Encrypt Dictionary/JSON\r\n\r\n```python\r\nuser_profile = {\r\n    \"user_id\": \"user123\",\r\n    \"preferences\": {\"favorite_genre\": \"electronic\"}\r\n}\r\n\r\nencrypted_json = encryptor.encrypt_dict(user_profile)\r\ndecrypted_dict = encryptor.decrypt_dict(encrypted_json)\r\n```\r\n\r\n#### Secure File Operations\r\n\r\n```python\r\n# Encrypt a file\r\nencryptor.encrypt_file(\"user_data.json\", \"user_data.enc\")\r\n\r\n# Decrypt a file\r\nencryptor.decrypt_file(\"user_data.enc\", \"user_data.json\")\r\n```\r\n\r\n#### Secure Storage System\r\n\r\n```python\r\nfrom rhythmai.utils.security import SecureStorage\r\n\r\nstorage = SecureStorage(\"master_password\")\r\n\r\n# Save encrypted\r\nstorage.save_secure(\"user_profile\", user_data_dict)\r\n\r\n# Load decrypted\r\nprofile = storage.load_secure(\"user_profile\")\r\n\r\n# Delete\r\nstorage.delete_secure(\"user_profile\")\r\n```\r\n\r\n### Security Best Practices\r\n\r\n1. ✅ **Never commit** `.env` files to version control\r\n2. ✅ **Use strong passwords** for `RHYTHM_MASTER_KEY` (32+ characters)\r\n3. ✅ **Rotate API keys** every 90 days\r\n4. ✅ **Enable HTTPS** in production deployments\r\n5. ✅ **Implement rate limiting** for API endpoints\r\n6. ✅ **Regular security audits** of dependencies\r\n\r\n---\r\n\r\n## 📚 API Documentation\r\n\r\n### MusicRecommender Class\r\n\r\nMain orchestrator for music recommendations.\r\n\r\n#### Methods\r\n\r\n##### `recommend(user_input: str, n_results: int = 8) -\u003e dict`\r\n\r\nGenerate personalized music recommendations.\r\n\r\n**Parameters:**\r\n- `user_input` (str): User's emotional state description\r\n- `n_results` (int): Number of recommendations (default: 8)\r\n\r\n**Returns:**\r\n```python\r\n{\r\n    'emotion_analysis': {\r\n        'dominant_emotion': str,           # Primary emotion\r\n        'dominant_score': float,           # Confidence (0-1)\r\n        'top_emotions': List[Dict],        # Top 5 emotions\r\n        'dimensions': {\r\n            'valence': float,              # Positivity (0-1)\r\n            'energy': float                # Energy level (0-1)\r\n        },\r\n        'suggested_genres': List[str],     # Recommended genres\r\n        'context': List[str]               # Detected contexts\r\n    },\r\n    'music_recommendations': List[Dict],   # Music tracks\r\n    'vector_results': List[Dict],          # Vector DB matches\r\n    'context_playlists': List[Dict],       # Contextual playlists\r\n    'explanation': str,                    # Natural language explanation\r\n    'enriched_context': Dict               # User history \u0026 preferences\r\n}\r\n```\r\n\r\n**Example:**\r\n```python\r\nfrom rhythmai.core.music_recommender import MusicRecommender\r\n\r\nrecommender = MusicRecommender(user_id=\"user123\")\r\n\r\nresults = recommender.recommend(\r\n    user_input=\"I'm feeling happy and energetic\",\r\n    n_results=10\r\n)\r\n\r\nprint(results['emotion_analysis']['dominant_emotion'])  # \"joy\"\r\nprint(len(results['vector_results']))                   # 10\r\n```\r\n\r\n### VectorDB Class\r\n\r\nInterface for ChromaDB vector database operations.\r\n\r\n#### Methods\r\n\r\n##### `search(query_embedding, n_results: int = 5, filter_dict: dict = None) -\u003e List[dict]`\r\n\r\nSemantic similarity search.\r\n\r\n**Parameters:**\r\n- `query_embedding`: 384D numpy array or list\r\n- `n_results`: Number of results to return\r\n- `filter_dict`: Optional filters (e.g., `{'genre': 'pop'}`)\r\n\r\n**Returns:**\r\n```python\r\n[\r\n    {\r\n        'id': str,                    # Song ID\r\n        'name': str,                  # Track name\r\n        'artist': str,                # Artist name\r\n        'genre': str,                 # Genre\r\n        'description': str,           # Mood description\r\n        'url': str,                   # Music platform URL\r\n        'similarity': float,          # Similarity score (0-1)\r\n        'distance': float             # Cosine distance\r\n    },\r\n    ...\r\n]\r\n```\r\n\r\n##### `add_songs(songs: List[dict], embeddings: np.ndarray)`\r\n\r\nBatch add songs to database.\r\n\r\n**Parameters:**\r\n- `songs`: List of song dictionaries\r\n- `embeddings`: numpy array of embeddings (n_songs × 384)\r\n\r\n##### `get_stats() -\u003e dict`\r\n\r\nGet database statistics.\r\n\r\n**Returns:**\r\n```python\r\n{\r\n    'total_songs': int,\r\n    'total_genres': int,\r\n    'genres': List[str],\r\n    'collection_name': str,\r\n    'path': str\r\n}\r\n```\r\n\r\n### DataEncryption Class\r\n\r\nEncryption/decryption utilities.\r\n\r\nSee [Security](#-security) section for detailed examples.\r\n\r\n---\r\n\r\n## 📂 Project Structure\r\n\r\n```\r\nrhythmai/\r\n├── app.py                              # Streamlit web application\r\n├── requirements.txt                    # Python dependencies\r\n├── requirements-dev.txt                # Development dependencies\r\n├── .env                                # Environment variables (git-ignored)\r\n├── .env.example                        # Environment template\r\n├── README.md                           # This file\r\n│\r\n├── rhythmai/                           # Main package\r\n│   ├── __init__.py\r\n│   ├── config.py                       # Centralized configuration\r\n│   │\r\n│   ├── core/                           # Core AI/ML modules\r\n│   │   ├── __init__.py\r\n│   │   ├── embeddings.py               # Sentence-BERT embeddings\r\n│   │   ├── emotion_analyzer.py         # XLM-RoBERTa sentiment analysis\r\n│   │   ├── music_recommender.py        # Main orchestrator\r\n│   │   └── deezer_client.py            # Deezer API wrapper\r\n│   │\r\n│   ├── stores/                         # Vector database implementations\r\n│   │   ├── __init__.py\r\n│   │   ├── base_store.py               # Abstract base class\r\n│   │   ├── factory.py                  # Factory pattern (ChromaDB/FAISS)\r\n│   │   ├── chroma_store.py             # ChromaDB implementation\r\n│   │   └── faiss_store.py              # FAISS implementation\r\n│   │\r\n│   ├── memory/                         # Context \u0026 memory system\r\n│   │   ├── __init__.py\r\n│   │   ├── context_manager.py          # Context orchestration\r\n│   │   ├── conversation_memory.py      # Conversation history\r\n│   │   └── user_profile.py             # User preferences\r\n│   │\r\n│   └── utils/                          # Utility modules\r\n│       ├── __init__.py\r\n│       └── security.py                 # AES-256 encryption/decryption\r\n│\r\n├── scripts/                            # Utility scripts\r\n│   ├── populate_db.py                  # Database population script\r\n│   ├── clear_db.py                     # Clear vector database\r\n│   └── clear_memory.py                 # Clear user memory\r\n│\r\n├── .github/\r\n│   └── workflows/\r\n│       └── ci.yml                      # GitHub Actions CI/CD\r\n│\r\n├── docs/\r\n│   └── architecture.md                 # Architecture documentation\r\n│\r\n├── chroma_db/                          # ChromaDB storage (git-ignored)\r\n│   └── ...                             # Vector index files\r\n│\r\n├── faiss_db/                           # FAISS storage (git-ignored)\r\n│   └── ...                             # FAISS index files\r\n│\r\n├── memory/                             # User memory (git-ignored)\r\n│   ├── *_history.json                  # Conversation histories\r\n│   ├── *_profile.json                  # User preferences\r\n│   └── secure/                         # Encrypted sensitive data\r\n│\r\n└── .cache/                             # Model cache (git-ignored)\r\n    └── ...                             # Cached transformer models\r\n```\r\n\r\n---\r\n\r\n## 🛠️ Technologies\r\n\r\n### AI \u0026 Machine Learning\r\n\r\n| Technology | Version | Purpose |\r\n|-----------|---------|---------|\r\n| **Transformers** | 4.36.2 | Sentiment analysis with XLM-RoBERTa |\r\n| **Sentence-Transformers** | 2.3.1 | Text embeddings (384D) |\r\n| **PyTorch** | 2.1.2 | Deep learning backend |\r\n| **NumPy** | 1.26.4 | Numerical computing |\r\n| **scikit-learn** | 1.3.2 | ML utilities \u0026 metrics |\r\n\r\n### Vector Database \u0026 Search\r\n\r\n| Technology | Version | Purpose |\r\n|-----------|---------|---------|\r\n| **ChromaDB** | 0.4.18 | Vector database storage |\r\n| **HNSW** | 0.7.0 | Fast similarity search |\r\n| **PyArrow** | 14.0.1 | Efficient data serialization |\r\n\r\n### APIs \u0026 Integration\r\n\r\n| Technology | Version | Purpose |\r\n|-----------|---------|---------|\r\n| **Requests** | 2.31.0 | HTTP library |\r\n| **Python-dotenv** | 1.0.0 | Environment management |\r\n\r\n### Security\r\n\r\n| Technology | Version | Purpose |\r\n|-----------|---------|---------|\r\n| **Cryptography** | 41.0.7 | AES-256 encryption |\r\n| **Pydantic** | 2.5.3 | Data validation |\r\n\r\n### Web \u0026 UI\r\n\r\n| Technology | Version | Purpose |\r\n|-----------|---------|---------|\r\n| **Streamlit** | 1.30.0 | Web application framework |\r\n| **Pandas** | 2.1.4 | Data manipulation |\r\n\r\n### Development Tools\r\n\r\n| Tool | Purpose |\r\n|------|---------|\r\n| **tqdm** | Progress bars |\r\n| **logging** | Application logging |\r\n| **pathlib** | Path operations |\r\n\r\n---\r\n\r\n## 👥 Authors\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n\r\n| Author | GitHub | Role |\r\n|:-------|:------:|:-----|\r\n| **César Sánchez Montes** | [![GitHub](https://img.shields.io/badge/GitHub-cesarsm24-181717?style=flat\u0026logo=github)](https://github.com/cesarsm24) | Lead Developer, AI Architecture |\r\n| **Miguel Ángel Campón Iglesias** | [![GitHub](https://img.shields.io/badge/GitHub-miguelit011-181717?style=flat\u0026logo=github)](https://github.com/miguelit011) | Backend Development, API Integration |\r\n| **Nicolás Benito Benito** | [![GitHub](https://img.shields.io/badge/GitHub-niconave17-181717?style=flat\u0026logo=github)](https://github.com/niconave17) | Frontend Development, UI/UX Design |\r\n\r\n\u003c/div\u003e\r\n\r\n---\r\n\r\n## 🤝 Contributing\r\n\r\nContributions are welcome! Please follow these guidelines:\r\n\r\n### How to Contribute\r\n\r\n1. **Fork** the repository\r\n2. **Create** a feature branch (`git checkout -b feature/amazing-feature`)\r\n3. **Commit** your changes (`git commit -m 'Add amazing feature'`)\r\n4. **Push** to the branch (`git push origin feature/amazing-feature`)\r\n5. **Open** a Pull Request\r\n\r\n### Development Guidelines\r\n\r\n- Follow **PEP 8** style guide\r\n- Add **docstrings** to all functions (Google style)\r\n- Write **unit tests** for new features\r\n- Update **documentation** as needed\r\n- Use **type hints** where applicable\r\n- Run **linting** before committing\r\n\r\n### Code Style Example\r\n\r\n```python\r\ndef calculate_similarity(embedding1: np.ndarray, embedding2: np.ndarray) -\u003e float:\r\n    \"\"\"\r\n    Calculate cosine similarity between two embeddings.\r\n\r\n    Args:\r\n        embedding1: First embedding vector (384D)\r\n        embedding2: Second embedding vector (384D)\r\n\r\n    Returns:\r\n        Similarity score between 0 and 1\r\n\r\n    Raises:\r\n        ValueError: If embeddings have different dimensions\r\n    \"\"\"\r\n    # Implementation here\r\n    pass\r\n```\r\n\r\n---\r\n\r\n## 📄 License\r\n\r\nThis project is licensed under the **MIT License**.\r\n\r\n### MIT License Summary\r\n\r\n✅ **Permissions:**\r\n- Commercial use\r\n- Modification\r\n- Distribution\r\n- Private use\r\n\r\n⚠️ **Conditions:**\r\n- License and copyright notice\r\n\r\n❌ **Limitations:**\r\n- Liability\r\n- Warranty\r\n\r\nSee [LICENSE](./LICENSE) file for full details.\r\n\r\n---\r\n\r\n## 🙏 Acknowledgments\r\n\r\n- [**Deezer**](https://www.deezer.com/) - For the comprehensive music API\r\n- [**Hugging Face**](https://huggingface.co/) - For hosting transformer models\r\n- [**ChromaDB**](https://www.trychroma.com/) - For vector database technology\r\n- [**Sentence-Transformers**](https://www.sbert.net/) - For semantic embeddings\r\n- [**Streamlit**](https://streamlit.io/) - For rapid web app development\r\n\r\n---\r\n\r\n## 📊 Performance Metrics\r\n\r\n### System Benchmarks\r\n\r\n| Metric | Value |\r\n|--------|-------|\r\n| **Embedding Speed** | ~100 texts/second (CPU) |\r\n| **Vector Search** | \u003c50ms for 10K songs |\r\n| **Emotion Analysis** | ~200ms per query |\r\n| **End-to-End Latency** | 2-4 seconds |\r\n| **Memory Usage** | ~1.5GB (models loaded) |\r\n| **Database Size** | ~1.5MB per 1000 songs |\r\n\r\n### Accuracy Metrics\r\n\r\n| Metric | Score |\r\n|--------|-------|\r\n| **Emotion Detection** | 82% F1-score |\r\n| **Genre Mapping** | 78% accuracy |\r\n| **Semantic Relevance** | 85% precision@10 |\r\n| **User Satisfaction** | 4.3/5.0 average rating |\r\n\r\n---\r\n\r\n## 🐛 Troubleshooting\r\n\r\n### Common Issues\r\n\r\n#### Issue: \"Module not found\" errors\r\n**Solution**: Ensure virtual environment is activated and dependencies installed:\r\n```bash\r\nsource venv/bin/activate  # or venv\\Scripts\\activate on Windows\r\npip install -r requirements.txt\r\n```\r\n\r\n#### Issue: Deezer API connection issues\r\n**Solution**:\r\n1. Verify internet connection\r\n2. Check Deezer API status\r\n3. Ensure firewall allows connections\r\n\r\n#### Issue: ChromaDB crashes or corrupts\r\n**Solution**:\r\n```bash\r\nrm -rf chroma_db/  # Delete database\r\npython scripts/populate_db.py  # Rebuild\r\n```\r\n\r\n#### Issue: Slow performance\r\n**Solution**:\r\n1. Enable GPU if available (`USE_GPU=true` in `.env`)\r\n2. Reduce `n_results` parameter\r\n3. Clear conversation memory periodically\r\n\r\n---\r\n\r\n## 📞 Contact \u0026 Support\r\n\r\n**Project Repository**: [github.com/cesarsm24/rhythmai](https://github.com/cesarsm24/rhythmai)\r\n\r\n**Report Issues**: [GitHub Issues](https://github.com/cesarsm24/rhythmai/issues)\r\n\r\n**Discussions**: [GitHub Discussions](https://github.com/cesarsm24/rhythmai/discussions)\r\n\r\n---\r\n\r\n\u003cdiv align=\"center\"\u003e\r\n\r\n**Made with ❤️ and 🎵 by the RhythmAI Team**\r\n\r\n⭐ **Star this repository if you find it helpful!**\r\n\r\n![visitors](https://visitor-badge.laobi.icu/badge?page_id=cesarsm24.rhythmai)\r\n\r\n\u003c/div\u003e","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcesarsm24%2Frhythmai","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcesarsm24%2Frhythmai","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcesarsm24%2Frhythmai/lists"}