FastMCP_RecSys

This is a CLIP-Based Fashion Recommender with MCP.

📌 Sample Components for UI

1. Image upload
2. Submit button
3. Display clothing tags + recommendations

Mockup

A user uploads a clothing image → YOLO detects clothing → CLIP encodes → Recommend similar

Folder Structure

/project-root
│
├── /backend
│   ├── Dockerfile            
│   ├── /app
│   ├── /aws
│   │   │   └── rekognition_wrapper.py         # AWS Rekognition logic
│   │   ├── /utils
│   │   │   └── image_utils.py                 # Bounding box crop utils
│   │   ├── /controllers
│   │   │   └── clothing_detector.py           # Coordinates Rekognition + cropping
│   │   ├── /tests
│   │   │   ├── test_rekognition_wrapper.py
│   │   │   └── test_clothing_tagging.py
│   │   ├── 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                      
│                      
├── /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                                
├── docker-compose.yml                     
└── README.md 

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.

npm install

Step 6: Start the Development Server (Frontend)

npm start

Once running, the server logs a confirmation and opens the app in your browser: http://localhost:3000/

What’s completed so far:

1. FastAPI server is up and running (24 Apr)
2. Database connection is set up (24 Apr)
3. Backend architecture is functional (24 Apr)
4. Basic front-end UI for uploading picture (25 Apr)

5. Mock Testing for AWS Rekognition -> bounding box (15 May)

PYTHONPATH=. pytest backend/app/tests/test_rekognition_wrapper.py

• Tested Rekognition integration logic independently using a mock → verified it correctly extracts bounding boxes only when labels match the garment set
• Confirmed the folder structure and PYTHONPATH=. works smoothly with pytest from root

6. Mock Testing for AWS Rekognition -> CLIP (20 May)

PYTHONPATH=. pytest backend/app/tests/test_clothing_tagging.py

• Detecting garments using AWS Rekognition

• Cropping the image around detected bounding boxes

• Tagging the cropped image using CLIP

7. Mock Testing for full image tagging pipeline (Image bytes → AWS Rekognition (detect garments) → Crop images → CLIP (predict tags) + Error Handling (25 May)

PYTHONPATH=. pytest backend/app/tests/test_clothing_tagging.py

• detect_garments: simulates AWS Rekognition returning one bounding box: {"Left": 0.1, "Top": 0.1, "Width": 0.5, "Height": 0.5}
• crop_by_bounding_box: simulates the cropping step returning a dummy "cropped_image" object
• get_tags_from_clip: simulates CLIP returning a list of tags: ["T-shirt", "Cotton", "Casual"]

8. Run Testing for CLIP Output (30 May)

python3 -m venv venv
pip install -r requirements.txt
pip install git+https://github.com/openai/CLIP.git
python -m backend.app.tests.test_tag_extractor

Next Step:

1. Evaluate CLIP’s tagging accuracy on sample clothing images
2. Fine-tune the tagging system for better recommendations
3. Test the backend integration with real-time user data
4. Set up monitoring for model performance
5. Front-end demo