🎬 Darky Hatthevas Studio

AI-powered creative platform for generating images, videos, and content. Built with FastAPI, Streamlit, MongoDB, and PyTorch/TensorFlow.

🏗️ Architecture

Frontend (Streamlit)
        ↓ HTTP API
Backend (FastAPI) ←→ MongoDB Atlas
        ↓
    AI Models (GPU Render)

🚀 Quick Start

Local Development

Prerequisites

• Python 3.11+
• Git
• MongoDB (local or MongoDB Atlas)

Installation

1. Clone the repository

git clone https://github.com/wisawakorn/-Dragy-ai.git
cd dragy-ai

2. Create virtual environment

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

3. Install dependencies

pip install -r requirements.txt

4. Setup environment variables

cp .env.example .env
# Edit .env with your configuration

5. Run MongoDB (if using local)

docker run -d -p 27017:27017 --name mongodb mongo:latest

6. Start backend

uvicorn main:app --reload --port 8000

7. Start frontend (in another terminal)

streamlit run streamlit_app.py

Visit:

• Backend API: http://localhost:8000
• API Docs: http://localhost:8000/docs
• Frontend: http://localhost:8501

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🌐 Cloud Deployment

Deploy on Render

1. Create a new Web Service on Render

   • Connect your GitHub repository
   • Select this repo: wisawakorn/-Dragy-ai

2. Configure Build & Start Commands

   • Build: pip install -r requirements.txt
   • Start: uvicorn main:app --host 0.0.0.0 --port $PORT

3. Add Environment Variables (Render Dashboard → Environment)

   MONGO_URI = mongodb+srv://user:pass@cluster.mongodb.net/...
   DATABASE_URL = postgresql://...
   OPENAI_API_KEY = sk-...
   

4. Deploy

   • Push to main branch
   • Render auto-deploys

Backend URL: https://darky-hatthevas-backend.onrender.com

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📁 Project Structure

dragy-ai/
├── main.py                      # FastAPI backend
├── database/
│   ├── connection.py            # DB connection
│   └── models.py                # SQLAlchemy models
├── streamlit_app.py             # Frontend (if exists)
├── templates/                   # HTML templates
├── static/                       # CSS, JS
├── requirements.txt             # Python dependencies
├── render.yaml                  # Render deployment config
├── .env.example                 # Example environment variables
└── README.md                    # This file

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🔧 API Endpoints

Core Endpoints

Method	Endpoint	Description
POST	/generate	Create a new generation job
GET	/assets	List all generated assets
GET	/job/{job_id}	Check job status
GET	/asset/{asset_id}/file	Download asset file
POST	/chat	Chat with AI assistant

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🎨 Features

✅ AI Content Generation

• Image generation (via Stable Diffusion)
• Video generation (via Video Diffusion models)
• Real-time progress tracking

✅ Asset Management

• Organize by category
• Thumbnail generation
• Reusable asset library

✅ AI Chat Assistant

• Thai language support
• Context-aware responses
• Integration with OpenAI or Ollama

✅ Security

• Rate limiting
• Request validation
• Input sanitization

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🔐 Environment Variables

# Database
DATABASE_URL=postgresql://localhost/hatthevas_db
MONGO_URI=mongodb://localhost:27017/studio_db

# AI Services
OPENAI_API_KEY=sk-your-key
OPENAI_MODEL=gpt-4o-mini
OLLAMA_MODEL=llama3.2
OLLAMA_BASE_URL=http://127.0.0.1:11434/api/generate

# Server
PORT=8000
DEBUG=false

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📚 Technology Stack

• Backend: FastAPI, Uvicorn
• Frontend: Streamlit
• Database: MongoDB Atlas, PostgreSQL
• AI Models: PyTorch, TensorFlow, Stable Diffusion
• Deployment: Render.com
• Authentication: OAuth 2.0 (Google)

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🛠️ Development

Run Tests

pytest tests/

Format Code

black . && isort .

Lint

pylint main.py database/

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📝 License

MIT License - see LICENSE file for details

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🤝 Contributing

Contributions welcome! Please:

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📧 Contact

For issues and questions, please open an issue on GitHub.

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Made with ❤️ by Darky Hatthevas Team