FaceFit AI Hair Accessory Recommender

FaceFit is an AI-powered hair accessory recommendation app that analyzes a user's selfie and suggests matching products based on hair color, face shape, and style preferences.

Features

• Upload a selfie to get smart product recommendations
• Uses Python (MediaPipe, TFLite) to analyze selfies
• Spring Boot backend handles image upload and runs the Python model
• React + Tailwind frontend built with ShadCN UI
• Realistic product data powered by curated Temu/Etsy accessories
• Tag-based filtering: style, occasion, face shape, hair color

Technologies

• Java 17+, Spring Boot
• Python (OpenCV, TFLite, MediaPipe)
• React + Next.js (TypeScript)
• Tailwind CSS, ShadCN UI
• Puppeteer/Node.js for scraping
• JSON for product dataset

Setup

1. Clone the repo
2. Run Spring Boot backend: ./mvnw spring-boot:run
3. Run frontend: npm install && npm run dev
4. Upload selfie from UI or use Postman to test /api/upload
5. To regenerate products: edit products.json or run the scraper

Folder Structure

/FaceFit
├── backend (Spring Boot)
│   ├── src/main/java/com/example/facefit
│   ├── resources/products.json
├── frontend (React)
│   ├── public/products.json
│   ├── pages/index.tsx
├── scrape-products.js

License

This project is dual-licensed under either:

• MIT License
• Apache License 2.0

You may choose either license for your use case.