Textly-DRF-API

An open-source API for text correction.

All-in-one API for grammar correction, spell check, sentiment analysis,Text Summary,Text Generation, Neural Machine Translation (German to English). Textly was build with Django-Rest-Framework and is based on Natural Language Processing. It uses many Deep Learning models like GPT2, BERT for text analysis.

Deep Learning Models

• Bidirectional Encoder Representations from Transformers (BERT)
• Generative Pretrained Transformer 2 (GPT-2)
• Seq2Seq LSTM Model built using PyTorch
• Bart-Large-CNN

Get Started

Use a virtual env

• Clone repo : $ git clone https://github.com/farazkhanfk7/textly-drf-api
• $ cd django-rest-api
• Create a virtualenv: $ mkvirtualenv env or $ python -m venv env
• Activate env : $ workon env or $ source env/bin/activate

Download Models

Run this shell script to download models to their respective folders.

$ sh load_model.sh

Install dependencies

$ pip install -r requirements.txt

Run project locally

$ python manage.py runserver

Run project with Docker

• Build Docker Image

$ docker-compose build

• Run Container

$ docker-compose up

API Endpoints

http://127.0.0.1:8000/gcapi/check/

Takes a sentence through POST request and performs Grammer-Check and sentiment analysis.

http://127.0.0.1:8000/gcapi/generate/

Takes a sentence through POST request and runs text-generation model to predict next words.

http://127.0.0.1:8000/gcapi/spell/

Takes a sentence through POST request and correct spellings.

http://127.0.0.1:8000/gcapi/summary/

Returns the summary of a large text entered by user.

http://127.0.0.1:8000/gcapi/translate-german/

Translates German text to English

Contribution

• You are welcome to contribute to the python package as it is still in early development phase and needs more contribution.
• Feel free to submit PR's and open issues.

Working on your first Pull Request? You can learn how from this free series How to Contribute to an Open Source Project on GitHub