AfroVBra is a cultural audio odyssey. We are building the definitive digital home for the sounds of the continent—pairing deep rhythmic analysis with high-fidelity streaming to create an ecosystem that respects the past and powers the future of African music.
Our mission is to create a platform that doesn't just play music but understands its provenance. AfroVBra bridges the gap between traditional storytelling and modern digital distribution, providing a space where artists are recognized and listeners are immersed.
| Technology | Purpose |
|---|---|
| FastAPI | High-performance async web framework |
| SQLAlchemy 2.0 | Async ORM with PostgreSQL |
| PyJWT + OAuth 2.1 | Secure authentication |
| Redis | Caching and message queue |
| Pydantic | Data validation |
| Service | Description |
|---|---|
| Emotion Detection | 8 Pan-African emotional states with multi-modal fusion |
| Predictive Analytics | User behavior prediction, content recommendations |
| Cultural Analytics | Regional content tracking, user profiles |
| Content Moderation | AI-powered with cultural awareness |
| Lyrics Alignment | CTC-based word-level sync with Whisper |
| ZoN MCP | Cultural AI companion with memory & RAG |
ZoN is AfroVBra's AI assistant built with FastAPI + OpenRouter:
- Chat Interface:
/zon/talkendpoint for natural conversation - Model Switching: Dynamic switch between OpenAI/OpenRouter models
- Memory System: FAISS vector-based persistent memory
- Knowledge Base: Embedded documents with multi-modal retrieval
- Mood Bridge: Connects AI conversations to AfroVBra's 8 emotional states
- VS Code Extension: IDE integration for AI-assisted development
Location: backend/zon_mcp/
| Technology | Purpose |
|---|---|
| React 18 | UI framework |
| TypeScript | Type safety |
| Vite | Fast builds |
| Tailwind CSS | Styling |
| face-api.js | Local mood detection |
| Service | Purpose |
|---|---|
| Railway | Backend deployment |
| Vercel | Frontend deployment |
| Graphify | Knowledge graph & code analysis |
- Initial architectural blueprints developed in Lagos.
- Established core principles: Cultural Authenticity, Technical Resilience, and User Empowerment.
- Built the first-generation metadata engine for Afrobeat classification.
- Launched "Project Vibe": A prototype exploration UI that visualizes the rhythmic complexity of African tracks.
- Initial migration to PostgreSQL to support enterprise-grade data integrity.
- Engineered the high-precision Lyrics Editor interface.
- Conducted pilot synchronization tests on 50+ tracks across diverse African genres.
- Relocated HQ to Abuja for closer integration with the local creative scene.
- The "Clean Slate" Workflow: Executed an enterprise-grade repository audit, resolving 50+ legacy security leaks and implementing strict gitleaks pre-commit hooks.
- Achieved stable Alpha status for the integrated lyrics and metadata management pipeline.
- Implemented Emotion Detection Engine with 8 Pan-African emotional states
- Built Predictive Analytics and Cultural Analytics services
- Integrated content moderation with cultural awareness
- Added Graphify knowledge graph for codebase analysis
cd backend
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
pip install -r requirements.txt
uvicorn app.main:app --reload --host 0.0.0.0 --port 8000cd vibra_ui
npm install
npm run dev# After core setup, install ML dependencies
pip install -r requirements-ml.txtThe Emotion Detection Engine recognizes these culturally-specific states:
| Emotion | Cultural Context | Genre Examples |
|---|---|---|
| JOY (Ubuntu) | Shared happiness | afrobeat, highlife, soukous |
| MELANCHOLY | Deep reflection | blues, spiritual, folk |
| ENERGY | Ancestral power | amapiano, gqom, dancehall |
| PEACE | Spiritual calm | ambient, meditation, acoustic |
| LONGING | Homeland connection | diaspora, gospel, folk |
| CELEBRATION | Community gathering | party, festival, highlife |
| CONTEMPLATION | Wisdom seeking | jazz, classical, instrumental |
| PASSION | Life force | soul, r&b, love songs |
AfroVBra uses Graphify for knowledge graph generation:
# Update the knowledge graph
"D:\my programming\AfroVBra\backend\venv\Scripts\python.exe" -m graphify update .
# Query the codebase
"D:\my programming\AfroVBra\backend\venv\Scripts\python.exe" -m graphify query "emotion detection"Output: graphify-out/graph.json, graphify-out/GRAPH_REPORT.md
AfroVBra/
├── backend/
│ ├── app/
│ │ ├── core/ # AI engines, security, settings
│ │ ├── models/ # SQLAlchemy models
│ │ ├── schemas/ # Pydantic schemas
│ │ ├── routers/ # API endpoints
│ │ ├── services/ # Business logic
│ │ └── crud/ # Database operations
│ ├── requirements.txt # Railway-safe (no ML)
│ ├── requirements-ml.txt # ML/Audio (local only)
│ └── venv/ # Python virtual environment
├── vibra_ui/ # React/TypeScript frontend
│ ├── src/
│ │ ├── components/ai/ # MoodDetector, MoodPulse
│ │ ├── context/ # AppCoreContext
│ │ └── pages/ # Listening, Dashboard
│ └── public/models/ # face-api.js models
├── graphify-out/ # Knowledge graph
└── docs/ # Architecture docs
- Lead Architect: Christopher Israel (OdiBà) Ahiome
- Location: Abuja, Nigeria
- Mission: Building the infrastructure for Africa's creative future
This project is proprietary. All rights are reserved by Christopher Israel Ahiome. See the LICENSE file for more details regarding technical review permissions.
AfroVBra: Where heritage meets innovation.