Insight

<h1 align="center">Project Insight</h1> <h2 align="center">NLP as a Service</h2> <p align="center"> <img alt="Project Insight" src="meta/Insight.png"> </p> <p align="center"> <a href="https://github.com/abhimishra91/insight/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/abhimishra91/insight"></a> <a href="https://github.com/abhimishra91/insight/network"><img alt="GitHub forks" src="https://img.shields.io/github/forks/abhimishra91/insight"></a> <a href="https://github.com/abhimishra91/insight/stargazers"><img alt="Github Stars" src="https://img.shields.io/github/stars/abhimishra91/insight"></a> <a href="https://github.com/abhimishra91/insight/blob/master/LICENSE"><img alt="GitHub license" src="https://img.shields.io/github/license/abhimishra91/insight"></a> <a href="https://github.com/abhimishra91/insight/"><img alt="Code style: black" src="https://img.shields.io/badge/code%20style-black-000000.svg"></a> </p>

Contents

1. Introduction
   • Features
2. Installation
   • Setup and Documentation
3. Project Details
   • Demonstration
   • Directory Details
   • How to Add a new Model
4. License

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Introduction

Project Insight is designed to create NLP as a service with code base for both front end GUI (streamlit) and backend server (FastApi) the usage of transformers models on various downstream NLP task.

The downstream NLP tasks covered:

• News Classification

• Entity Recognition

• Sentiment Analysis

• Summarization

• Information Extraction To Do

The user can select different models from the drop down to run the inference.

The users can also directly use the backend fastapi server to have a command line inference.

<a id='section01a'></a>

Features of the solution

• Python Code Base: Built using Fastapi and Streamlit making the complete code base in Python.
• Expandable: The backend is desinged in a way that it can be expanded with more Transformer based models and it will be available in the front end app automatically.
• Micro-Services: The backend is designed with a microservices architecture, with dockerfile for each service and leveraging on Nginx as a reverse proxy to each independently running service.
  • This makes it easy to update, manitain, start, stop individual NLP services.

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Installation

• Clone the Repo.
• Run the Docker Compose to spin up the Fastapi based backend service.
• Run the Streamlit app with the streamlit run command.

<a id='section02a'></a>

Setup and Documentation

1. Download the models

   • Download the models from here
   • Save them in the specific model folders inside the src_fastapi folder.

2. Running the backend service.

   • Go to the src_fastapi folder
   • Run the Docker Compose comnand

   $ cd src_fastapi
   src_fastapi:~$ sudo docker-compose up -d
   

3. Running the frontend app.

   <!--- - Front end is a **`WIP`** as a change in the backend architecture. - Should be up in a few days. --->
   • Go to the src_streamlit folder
   <!--- - Create the docker image from the `Docker File` - Then execute the docker image to spin up a container. ```console $ cd src_streamlit src_streamlit:~$ sudo docker build -t streamlit_app . src_streamlit:~$ sudo docker run -d --name streamlit_app streamlit_app ``` --->
   • Run the app with the streamlit run command

   $ cd src_streamlit
   src_streamlit:~$ streamlit run NLPfily.py
   

4. Access to Fastapi Documentation: Since this is a microservice based design, every NLP task has its own seperate documentation

   • News Classification: http://localhost:8080/api/v1/classification/docs
   • Sentiment Analysis: http://localhost:8080/api/v1/sentiment/docs
   • NER: http://localhost:8080/api/v1/ner/docs
   • Summarization: http://localhost:8080/api/v1/summary/docs

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Project Details

<a id='section03a'></a>

Demonstration

<p align="center"> <img alt="Project Insight Demo" src="meta/streamlit-NLPfiy.gif"> </p>

<a id='section03b'></a>

Directory Details

• Front End: Front end code is in the src_streamlit folder. Along with the Dockerfile and requirements.txt

• Back End: Back End code is in the src_fastapi folder.

  • This folder contains directory for each task: Classification, ner, summary...etc
  • Each NLP task has been implemented as a microservice, with its own fastapi server and requirements and Dockerfile so that they can be independently mantained and managed.
  • Each NLP task has its own folder and within each folder each trained model has 1 folder each. For example:

  - sentiment
      > app
          > api
              > distilbert
                  - model.bin
                  - network.py
                  - tokeniser files
              >roberta
                  - model.bin
                  - network.py
                  - tokeniser files
  

  • For each new model under each service a new folder will have to be added.

  • Each folder model will need the following files:

    • Model bin file.
    • Tokenizer files
    • network.py Defining the class of the model if customised model used.

  • config.json: This file contains the details of the models in the backend and the dataset they are trained on.

<a id='section03c'></a>

How to Add a new Model

1. Fine Tune a transformer model for specific task. You can leverage the transformers-tutorials

2. Save the model files, tokenizer files and also create a network.py script if using a customized training network.

3. Create a directory within the NLP task with directory_name as the model name and save all the files in this directory.

4. Update the config.json with the model details and dataset details.

5. Update the <service>pro.py with the correct imports and conditions where the model is imported. For example for a new Bert model in Classification Task, do the following:

   • Create a new directory in classification/app/api/. Directory name bert.

   • Update config.json with following:

     "classification": {
     "model-1": {
         "name": "DistilBERT",
         "info": "This model is trained on News Aggregator Dataset from UC Irvin Machine Learning Repository. The news headlines are classified into 4 categories: **Business**, **Science and Technology**, **Entertainment**, **Health**. [New Dataset](https://archive.ics.uci.edu/ml/datasets/News+Aggregator)"
     },
     "model-2": {
         "name": "BERT",
         "info": "Model Info"
     }
     }
     

   • Update classificationpro.py with the following snippets:

     Only if customized class used

     from classification.bert import BertClass
     

     Section where the model is selected

     if model == "bert":
         self.model = BertClass()
         self.tokenizer = BertTokenizerFast.from_pretrained(self.path)
     

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License

This project is licensed under the GPL-3.0 License - see the LICENSE.md file for details