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FastMCP_RecSys

by attarmau

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.

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/

📌 Sample Components for UI

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

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)

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
-
security - not tested
A
license - permissive license
-
quality - not tested

hybrid server

The server is able to function both locally and remotely, depending on the configuration or use case.

A CLIP-Based Fashion Recommender system with MCP that provides fashion recommendations based on uploaded images.

  1. Mockup
    1. Folder Structure
      1. Quick Start Guide
    2. What’s completed so far:

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