Offers containerization through Docker for both backend and frontend components, allowing for consistent deployment environments.
Uses .ENV files for environment variable management in both the backend and frontend components.
Uses FastAPI as the backend framework for serving the CLIP-based fashion recommendation system, handling image uploads and providing recommendation endpoints.
Leverages npm for frontend dependency management and application startup.
Incorporates PostCSS for CSS processing in the frontend, as evidenced by the postcss.config.js file.
Provides a React-based frontend for the fashion recommendation system, allowing users to upload clothing images and view recommendations.
Integrates YOLO for clothing detection in user-uploaded images, which are then processed by CLIP for encoding and recommendation.
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
Quick Start Guide
Step 1: Clone the GitHub Project
Step 2: Set Up the Python Environment
Step 3: Install Dependencies
Step 4: Start the FastAPI Server (Backend)
Once the server is running and the database is connected, you should see the following message in the console:
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
This server cannot be installed
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.
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