FastMCP_RecSys
This is a CLIP-Based Fashion Recommender with MCP.
Mockup
A user uploads a clothing image → YOLO detects clothing → CLIP encodes → Recommend similar
Folder Structure
/project-root
│
├── /backend
│ ├── Dockerfile
│ ├── /app
│ │ ├── server.py # FastAPI app code
│ │ ├── /routes
│ │ │ └── clothing_routes.py
│ │ ├── /controllers
│ │ │ └── clothing_controller.py
│ │ │ └──clothing_tagging.py
│ │ │ └── tag_extractor.py # Pending: define core CLIP functionality
│ │ ├── schemas/
│ │ │ └── clothing_schemas.py
│ │ ├── config/
│ │ │ └── tag_list_en.py $ Tool for mapping: https://jsoncrack.com/editor
│ │ │ └── database.py
│ │ │ └── settings.py
│ │ │ └── api_keys.py
│ │ └── requirements.txt
│ └── .env
│
├── /fastmcp
│ └── app
│ └── server.py
│
├── /frontend
│ ├── Dockerfile
│ ├── package.json
│ ├── package-lock.json
│ ├── /public
│ │ └── index.html
│ ├── /src
│ │ ├── /components
│ │ │ ├── ImageUpload.jsx
│ │ │ ├── DetectedTags.jsx
│ │ │ └── Recommendations.jsx
│ │ ├── /utils
│ │ │ └── api.js
│ │ ├── App.js # Main React component
│ │ ├── index.js
│ │ ├── index.css
│ │ ├── tailwind.config.js
│ │ ├── postcss.config.js
│ │ └── .env
│ ├── .gitignore
│ ├── docker-compose.yml
│ └── README.md
└────── requirements.txt
Quick Start Guide
Step 1: Clone the GitHub Project
Step 2: Set Up the Python Environment
python -m venv venv
source venv/bin/activate # On macOS or Linux
venv\Scripts\activate # On Windows
Step 3: Install Dependencies
pip install -r requirements.txt
Step 4: Start the FastAPI Server (Backend)
uvicorn backend.app.server:app --reload
Once the server is running and the database is connected, you should see the following message in the console:
Database connected
INFO: Application startup complete.
Step 5: Install Dependencies
Database connected
INFO: Application startup complete.
Step 6: Start the Development Server (Frontend)
Once running, the server logs a confirmation and opens the app in your browser: http://localhost:3000/
📌 Sample Components for UI
- Image upload
- Submit button
- Display clothing tags + recommendations
What’s completed so far:
- FastAPI server is up and running (24 Apr)
- Database connection is set up (24 Apr)
- Backend architecture is functional (24 Apr)
- Basic front-end UI for uploading picture (25 Apr)
Next Step:
- Evaluate CLIP’s tagging accuracy on sample clothing images
- Fine-tune the tagging system for better recommendations
- Test the backend integration with real-time user data
- Set up monitoring for model performance
- Front-end demo