Monlamai FastAPI

A FastAPI-based backend service for Monlam AI models, providing various AI services including translation, OCR, speech-to-text, text-to-speech, chat, and more.

Table of Contents

• Overview
• Features
• Prerequisites
• Environment Setup
• Installation
• Running the Application
• API Documentation
• Deployment
• Testing
• Project Structure
• Contributing
• License

Overview

Monlamai FastAPI is a RESTful API service that provides access to various AI models developed by Monlam AI. The service includes endpoints for translation, OCR, speech-to-text, text-to-speech, chat, and more.

Features

• Translation Services: API endpoints for text translation
• OCR (Optical Character Recognition): Convert images to text
• Speech-to-Text: Convert audio to text (currently under maintenance)
• Text-to-Speech: Convert text to audio (currently under maintenance)
• Chat: Conversational AI interface
• User Management: User authentication and management
• File Upload: S3 integration for file uploads
• Rate Limiting: API rate limiting to prevent abuse
• Authentication: Token-based authentication

Prerequisites

• Python 3.8+
• PostgreSQL database
• AWS S3 account (for file uploads)
• Docker (optional, for containerized deployment)

Environment Setup

Create a .env file in the root directory with the following variables:

# Server Configuration
PORT=8000

# Database Configuration
DATABASE_URL=postgresql://username:password@localhost:5432/dbname

# Authentication
MODEL_AUTH=your_model_auth_token
API_KEY=your_api_key

# Model URLs
MT_MODEL_URL=your_translation_model_url

# AWS S3 Configuration (for file uploads)
AWS_ACCESS_KEY_ID=your_aws_access_key
AWS_SECRET_ACCESS_KEY=your_aws_secret_key
AWS_REGION=your_aws_region
S3_BUCKET=your_s3_bucket_name

# Other Services
# Add any other service-specific environment variables here

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/monlamai-fast-api.git
   cd monlamai-fast-api

2. Create and activate a virtual environment:

   # On Windows
   python -m venv .venv
   .venv\Scripts\activate
   
   # On macOS/Linux
   python -m venv .venv
   source .venv/bin/activate

3. Install the dependencies:

   pip install -r requirements.txt

4. Generate Prisma client:

   prisma generate

Running the Application

Development Mode

Run the application in development mode with auto-reload:

python main.py

Or using uvicorn directly:

uvicorn main:app --host 0.0.0.0 --port 8000 --reload

Production Mode

For production, it's recommended to use a production ASGI server like Gunicorn with Uvicorn workers:

gunicorn -w 4 -k uvicorn.workers.UvicornWorker main:app --bind 0.0.0.0:8000

API Documentation

Once the application is running, you can access the API documentation at:

• Swagger UI: http://localhost:8000/docs
• ReDoc: http://localhost:8000/redoc

Deployment

Cloud Deployment

Render (Direct Deployment)

Note: This project is deployed directly to Render.com without using Docker.

1. Create a new Web Service on Render:

   • Sign in to your Render account and go to the dashboard
   • Click on "New" and select "Web Service"
   • Connect your GitHub/GitLab repository or use the manual deploy option

2. Configure the service:

   • Name: Choose a name for your service (e.g., monlamai-api)
   • Runtime: Select Python
   • Build Command: pip install -r requirements.txt && prisma generate
   • Start Command: uvicorn main:app --host 0.0.0.0 --port $PORT

3. Set environment variables:

   • Go to the "Environment" tab
   • Add all required environment variables from your .env file:
     • MODEL_AUTH
     • API_KEY
     • MT_MODEL_URL
     • DATABASE_URL
     • AWS credentials if using S3
     • Any other required variables

4. Configure database (if needed):

   • Create a PostgreSQL database in Render
   • Render will automatically set the DATABASE_URL environment variable
   • Or connect to an external database by setting the DATABASE_URL manually

5. Deploy:

   • Click "Create Web Service"
   • Render will automatically build and deploy your application
   • Your API will be available at https://your-service-name.onrender.com

6. Monitoring and Logs:

   • Monitor your application's performance in the Render dashboard
   • View logs by clicking on the "Logs" tab

Project Structure

monlamai-fast-api/
├── main.py                  # Main application entry point
├── requirements.txt         # Python dependencies
├── .env                     # Environment variables (not in version control)
├── .gitignore               # Git ignore file
├── prisma/                  # Prisma ORM configuration
├── v1/                      # API version 1
│   ├── auth/                # Authentication modules
│   ├── Config/              # Configuration modules
│   ├── dict-data/           # Dictionary data
│   ├── libs/                # Library modules
│   ├── model/               # Data models
│   ├── models.py            # Pydantic models
│   ├── routes/              # API routes
│   ├── test/                # Test modules
│   └── utils/               # Utility functions
└── README.md                # This file

Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature-name
3. Commit your changes: git commit -m 'Add some feature'
4. Push to the branch: git push origin feature-name
5. Submit a pull request

License

[Specify your license here]

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For more information, contact officials@monlam.com or visit https://monlam.ai.