Open Translate

Welcome to Open-Translate, where innovation meets seamless communication. Our project serves as a user-friendly interface for machine translation, complemented by a robust Restful API implementation.

Open Translate

Frontend

The provided code is intentionally generic, allowing you to seamlessly integrate the frontend with your unique project requirements.

Installation and Setup

git clone https://github.com/danielinux7/open-translate.git
cd open-translate
npm install
ng serve

Customization

To tailor the frontend to your specific requirements, consider the following customization options:

Adding Languages

Expand the language options by adding a new language to the src/app/languages.ts file. Simply include it in the languages object list.

API Configuration

Adjust API calls to suit your needs by modifying the relevant sections in the src/app/translate.service.ts file. For local testing of the API server, set the endpoint to localhost.

Backend

The backend comprises two servers: a Flask server responsible for handling RESTful API calls, and a TensorFlow server dedicated to managing Neural Machine Translation (NMT) models. When a call is initiated, the Flask server manages the requests and seamlessly passes them to the TensorFlow server.

Setup and Run

git clone https://github.com/danielinux7/open-translate.git
cd open-translate/server
bash setup_prod.sh # use setup.sh for localhost
bash run_prod.sh # use run.sh for localhost

You should configure your production server name in setup_prod.sh and run_prod.sh. You can stop the servers by running bash stop.sh.

API Payload

The payload for API calls includes the following key components:

langSrc: Source language identifier.
langTgt: Target language identifier.
source: Representing the source content.

The response includes a target key-value pair, providing the translated content.

Language Capabilities Enhancement

Expanding the server's language capabilities is a straightforward process. To integrate your TensorFlow saved model:

Place the model under the /models/<langSrc-langTgt>/<Version> folder.
- Example: /models/en-fr/1/ for English to French translation, version 1.
If you introduce a new version, TensorFlow serves the latest version.

For tokenization, the backend uses Sentencepiece. Include the src.model and tgt.model models in the /models/<langSrc-langTgt>/<Version>/assets folder.

You can unpuck server/models.zip to get the available models running.

User-Submitted Translations

The /starred folder serves as the destination for user-submitted edited or high-quality translations via the frontend.

danielinux7/open-translate