Text Summarisation

Welcome to the text summarisation project! This project walks you through a complete pipeline for summarising text, covering everything from data ingestion to evaluating the final model. We've used GitHub Actions for CI/CD and deployed everything on Azure Cloud to keep things running smoothly and continuously integrated. The modular design makes it easy to maintain and scale. Plus, we've created a user-friendly web application with HTML, CSS, and FastAPI so you can easily input text and get summaries right away.

Workflows

1. Update config.yaml
2. Update params.yaml
3. Update entity
4. Update the configuration manager in src config
5. update the conponents
6. update the pipeline
7. update the main.py
8. update the app.py

Project Architecture

1. Data Ingestion:

   • In this step, we handle data preprocessing and data cleaning.

2. Data Validation:

   • Ensuring the text data's integrity and correctness.

3. Data Transformation:

   • Cleaning and transforming text data into a suitable format for summarisation.

4. Model Training:

   • Training machine learning models for text summarisation

5. Model Evaluation:

   • Evaluating the performance of the text summarisation models.

6. Web Interface:

   • A user-friendly interface for inputting text and displaying summaries.

7. CI/CD:

   • Automated testing and deployment using GitHub Actions.

8. Cloud Deployment:

   • Deploying the text summarisation model and web interface on Azure Cloud.

Key Features

• Modular pipeline structure
• Automated CI/CD pipeline
• Scalable and maintainable codebase
• Web interface for user interaction
• Deployment on Azure Cloud

How to Run

Create a conda environment after opening the repository:

conda create -n summary python=3.8 -y
conda activate summary

Install Dependencies:

pip install -r requirements.txt

Run the application:

python app.py

Azure Deployment Steps:

1. Prepare Your Environment:

• Log in to Azure Portal

2. Create and Configure Azure Resources

• Create a Resource Group

az group create --name <Your Resource Group Name> --location eastus

• Create an Azure Container Registry (ACR)

az acr create --resource-group <Your Resource Group Name> --name TextSummaryRegistry --sku Basic

• Create an Azure App Service Plan

az appservice plan create --name <Azure App Service Plan Name> --resource-group <Your Resource Group Name> --sku B1 --is-linux

3. Build and Push Docker Image

• Build Docker Image

docker build -t <Your Image Name> .

• Tag Docker Image

docker tag <Your Image Name> <Your ACR Login Server>/<Your Image Name>:latest

• Login to ACR

az acr login --name <Your ACR Name>

• Push Docker Image to ACR

docker push <Your ACR Login Server>/<Your Image Name>:latest

4. Configure Continuous Deployment

Set Up GitHub Actions for CI/CD

• Create the workflow file in .github/workflows/main.yml

5. Set Up GitHub Secrets

   ACR_NAME: The name of your Azure Container Registry.

   RESOURCE_GROUP: The name of your Azure resource group.

   AZURE_CREDENTIALS: Credentials required to authenticate with Azure.

6. Deploy and Verify

• Push your code to the main branch.
• GitHub Actions will automatically build and deploy your Docker image to Azure.
• Verify the deployment by visiting your Azure Web App URL.

Results

The final output of the project is a trained text summarisation model deployed on Azure Cloud, accessible via a web interface where users can input text and receive summarised content.