Fashion Recommendation System

🚀 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 connected to the database, you should see the following messages in the console:

Database connected
INFO:     Application startup complete.

Step 5: Install Frontend Dependencies

npm install

Step 6: Start the Development Server (Frontend)

npm start

Once running, the server logs will show confirmation messages, and the application will open in your browser at: http://localhost:3000/.

Completed Tasks:

1. FastAPI server is up and running (Apr 24)
2. Database connection is set up (Apr 24)
3. Backend architecture is functioning properly (Apr 24)
4. The basic UI of the frontend has completed the image upload function (Apr 25)

Next Steps:

1. Evaluate the tagging accuracy of CLIP on clothing sample images.
2. Adjust the tagging system to improve recommendation effectiveness.
3. Test the integration of the backend with real - time user data.
4. Set up model performance monitoring.
5. Frontend demonstration