njnjnj;;.;.;;.;.# SkillBridge
🌐 Live Demo: https://skillbridge9.vercel.app/ n n n n n n n n n n n n n A full-stack community web app where users post skills they can teach or want to learn. An ML embedding model matches teachers with learners in real-time using semantic similarity (pgvector cosine search). When matched, both users enter a live chat room where an AI Coach (Groq LLaMA 3.3 70B) observes and jumps in every 4th message to clarify, give examples, or suggest free resources.
| Layer | Technology |
|---|---|
| Frontend | Next.js 14 App Router + TypeScript + Tailwind CSS |
| Backend | FastAPI + Python 3.11 |
| Database | Supabase (PostgreSQL + pgvector + Realtime) |
| Auth | Supabase Auth (email/password) |
| ML Model | sentence-transformers all-MiniLM-L6-v2 (384-dim) |
| LLM | Groq API llama-3.3-70b-versatile |
| Automation | n8n (daily digest emails + weekly skill gap reports) |
| Deployment | Vercel (frontend) + HuggingFace Spaces (backend) |
skillbridge/
├── frontend/ # Next.js 14 App Router
│ ├── app/
│ ├── components/
│ └── lib/
├── backend/ # FastAPI
│ ├── routes/
│ ├── services/
│ └── db/
- User posts a skill they need → FastAPI embeds the text into a 384-dim vector
- pgvector cosine similarity runs against all offered skills in Supabase
- If best match score > 0.5 → a match is created and both users are notified via Supabase Realtime
- Users open a live chat session — every 4th message, the AI Coach streams a response via SSE
- n8n automation sends daily digest emails of unmatched needs and weekly skill gap reports
cd backend
python -m venv .venv
.venv\Scripts\activate # Windows
pip install -r requirements.txt
cp .env.example .env # fill in your keys
uvicorn main:app --reload --port 8000cd frontend
npm install
cp .env.local.example .env.local # fill in your keys
npm run devSUPABASE_URL=
SUPABASE_SERVICE_KEY=
GROQ_API_KEY=
NEXT_PUBLIC_SUPABASE_URL=
NEXT_PUBLIC_SUPABASE_ANON_KEY=
NEXT_PUBLIC_BACKEND_URL=http://localhost:8000