Unlimited Translator (ULT)

README on languages: English, Russian

Unlimited Translator (ULT) is a FastAPI-based service offering unlimited text translation capabilities. Powered by deep_translator, ULT supports automatic language detection and a wide range of target languages. Its design ensures scalability, making it ideal for applications requiring high-volume or continuous translations without constraints. ULT seamlessly integrates into modern workflows, providing fast and reliable translations for users worldwide.

Features

• Translate text into any supported language.
• Automatic source language detection.
• RESTful API for hassle-free integration.

Requirements

• Python 3.8 or later
• FastAPI
• Pydanttic
• Uvicorn
• Deep Translator

Installation

1. Clone the repository:

   git clone https://ilyazheproghub.ru/project/ilyazheprog/ult.git
   cd ult
   

2. Create and activate a virtual environment:

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

3. Install the dependencies:

   pip install -r requirements.txt
   

Running ULT

To start the server, simply run:

python main.py

This will launch the application on http://127.0.0.1:8000.

Here’s the updated section with a detailed explanation of how to choose the number of workers based on workload:

How Does It Work?

• The main.py file contains the entry point for the application:

  if __name__ == "__main__":
    uvicorn.run("api:app", host="0.0.0.0", port=8000, workers=6)
  

• By default:
  • The application binds to all network interfaces (0.0.0.0).
  • It listens on port 8000.
  • Six worker processes are started for handling concurrent requests.

Choosing the Number of Workers

The number of workers (--workers) defines how many processes will run your FastAPI application. More workers allow the application to handle more simultaneous requests but use more server resources. The choice depends on:

1. Type of Tasks:

   • I/O-bound tasks (like this translation service):
     • Tasks like calling external APIs or reading files often leave the CPU idle while waiting for a response.
     • Use more workers than CPU cores to maximize resource utilization.
   • CPU-bound tasks:
     • Heavy calculations or data processing require significant CPU time.
     • Match the number of workers to the number of available CPU cores.

2. Expected Traffic:

   • Low traffic:
     • 1-2 workers are sufficient for lightweight, low-traffic applications.
   • Moderate traffic:
     • Use 1 worker per CPU core (e.g., 4 workers for a 4-core server).
   • High traffic:
     • For I/O-bound workloads, use 1.5-2x the number of CPU cores to handle bursts of requests.

3. Server Resources:

   • Ensure the server has enough RAM to support the number of workers.
   • Each worker is a separate process and consumes memory.

Why Six Workers?

In this case:

• The translation service is I/O-bound, making it suitable to oversubscribe workers relative to CPU cores.
• Six workers ensure efficient handling of concurrent translation requests, especially on multi-core servers.

Rule of Thumb

• The higher the number of simultaneous requests, the more workers you should configure (within the limits of your server’s resources).
• Monitor performance (e.g., CPU usage, response times) and adjust the number of workers as needed.

API Endpoints

POST /translate

Translate text into the specified language.

Request Body:

{
  "text": "Hello, world!",
  "to": "es"
}

Response:

• Success:

  {
    "translatedText": "Hola, mundo!",
    "status": true,
    "message": "Translation successful"
  }
  

• Error:

  {
    "translatedText": null,
    "status": false,
    "message": "An unexpected error occurred during translation."
  }
  

License

This project is licensed under the GNU General Public License (GPL). See the LICENSE file for details.