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🚀 How to Run the Project

1. Clone this repo and navigate to the project directory

git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

2. Set up Python Virtual Environment (optional but recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install Backend Dependencies

pip install -r requirements.txt

4. Start the Backend Server

cd backend
python app.py

5. Start the Frontend App

Open another terminal and run:

cd frontend
npm install
npm start

Your app should now be live at http://localhost:3000


🎯 Features

  • Capture or upload facial images
  • Input skin concerns and preferences
  • Receive product recommendations instantly
  • Fully browser-based interface
  • Works locally with no third-party integrations

🗝️ Keywords

Recommendation System, Skin Tone, Skin Type, Acne, Web App, Flask, React


About

End-to-end deep-learning skincare recommendation engine. An EfficientNet-B0 CNN classifies skin type, tone, and acne severity from a face photo and serves personalised product recommendations in real time via a Flask API and React frontend.

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