A therapeutic coloring and tracing application that uses AI to generate personalized coloring book pages and track user progress.

• Docker and Docker Compose installed
• Gemini API key from Google AI Studio

1. Clone the repository

   git clone <your-repo-url>
   cd dubhacks25

2. Set up environment variables

   cp .env.example .env

   Edit .env and add your Gemini API key:

   GEMINI_API_KEY=your_actual_api_key_here
   

3. Build and start the application

   docker-compose up --build

   For background mode:

   docker-compose up -d --build

4. Access the application

   • Frontend: http://localhost:3000
   • Backend API: http://localhost:5000

docker-compose down

To also remove volumes:

docker-compose down -v

• Backend: Flask REST API with Gemini AI integration

  • Port: 5000
  • Generates coloring book pages using Gemini 2.0 Flash
  • Integrates Prompt Enhancer API for better therapeutic content
  • Provides scoring endpoints for user progress tracking

• Frontend: Next.js application

  • Port: 3000
  • React-based UI for coloring and tracing activities
  • Tailwind CSS for styling

dubhacks25/
├── backend/              # Flask API
│   ├── app.py           # Application factory
│   ├── routes/          # API endpoints
│   ├── services/        # Business logic (AI generation)
│   ├── models/          # Data models
│   ├── Dockerfile       # Backend container config
│   └── requirements.txt # Python dependencies
├── the-grounds/         # Next.js frontend
│   ├── src/            # Source code
│   ├── public/         # Static assets
│   ├── Dockerfile      # Frontend container config
│   └── package.json    # Node dependencies
└── docker-compose.yml  # Orchestration config

cd backend
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install -r requirements.txt
python app.py

cd the-grounds
npm install
npm run dev

Generate a coloring book page from a text prompt.

Request:

{
  "prompt": "a happy dog playing in a park"
}

Response:

{
  "id": "img_abc123",
  "url": "/static/img_abc123.png"
}

Score a user's traced image against the original.

Request:

{
  "original_id": "img_abc123",
  "traced_data": "base64_encoded_image_data"
}

Response:

{
  "score": 0.85,
  "feedback": "Great job! Very accurate tracing."
}

• GEMINI_API_KEY: Your Google Gemini API key (required)
• FLASK_ENV: Environment mode (production/development)

• NEXT_PUBLIC_API_URL: Backend API URL (defaults to http://backend:5000 in Docker)

MIT