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sakhawatahmed/translator

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App README

AI Translator Gradio App

Overview

AI Translator is a web-based application built using Python and Gradio that provides machine translation functionality. The application uses the facebook/nllb-200-3.3B pre-trained model from Hugging Face Transformers, which supports translation between over 200 languages.

The app allows users to input text, select source and target languages, and get accurate translations interactively.

Purpose: This project demonstrates the integration of a pre-trained NLP model into an interactive web interface, fulfilling the requirements of a Gradio-based NLP assignment.

Features

  • —Text Translation: Translate between 200+ languages.
  • —Language Swap: Easily swap source and target languages with one click.
  • —Interactive Interface: Instant display of translations.
  • —User-Friendly UI: Clean design with input/output text boxes and dropdowns.
  • —Custom Styling: Modern look using custom CSS.

1. Application Structure Gradio-application/ │ ├─ app.py # Main Gradio app ├─ requirements.txt # Dependencies └─ README.md # Documentation

2. Key Components

  • —translate(text, src, tgt) function: Handles translation using NLLB-200 model. Converts language names to Hugging Face codes and generates the translated text.
  • —Gradio UI:
  • —Input textbox for the text to translate.
  • —Dropdown menus for source and target languages.
  • —Translate button to perform translation.
  • —Swap button to swap languages quickly.
  • —Custom CSS:
  • —Styles for input/output boxes, buttons, and overall layout.

Supported Languages

The app supports over 200 languages. Some examples include:

  • —English, Bengali, Hindi, Spanish, French, Chinese, Arabic, Russian, Japanese, German, Portuguese, Italian, Korean, Urdu, Tamil, Telugu, Malayalam, etc.
Full list is available in the LANGUAGES dictionary in app.py.

Setup Instructions

  1. 1.Clone the repository
bash
git clone <repository_url>
cd Gradio-application

*Create a virtual environment
python -m venv venv


*Activate the virtual environment
Linux/Mac:
source venv/bin/activate


Windows:
venv\Scripts\activate


*Install dependencies
pip install -r requirements.txt


*Run the application
python app.py