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Python Expo React Native Flask PyTorch Capstone

Overview

CU-Bytes is a comprehensive food tracking system consisting of:

  • Backend: Flask API server for data management
  • Frontend: React Native mobile application
  • Machine Learning: PyTorch food recognition and nutrition analysis

Developer Setup

  1. Clone the repository

    git clone https://github.com/GAchuzia/cu-bytes.git
    cd cu-bytes
  2. Set up backend

    python -m venv backenv
    backenv\Scripts\activate # Windows
    pip install -r requirements.txt
    python -m backend.database.init_user_settings_db
    python -m backend.database.init_auth_db
    python -m backend.database.init_food_db
    python -m backend.database.init_logging_db
    python -m backend.app
  3. Set up frontend

    cd frontend/cu-bytes
    npm install
    npm start
  4. Set up machine learning environment

    cd machine-learning
    python -m venv mlenv
    mlenv\Scripts\activate  # Windows
    pip install -r requirements.txt

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AI Food Tracking App for Carleton University

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