this is end to end NLP project with deployment using github action
The project addresses the challenge of condensing lengthy texts, such as articles, documents, or paragraphs, into concise summaries without losing the core message and key information. This involves identifying significant sentences or phrases from the original text and presenting them in a condensed form. The primary objective is to develop an efficient text summarization model that can accurately capture the essence of the input text and produce coherent summaries.
Gather a diverse dataset for training the summarization model and tokenize the text into sentences or words. Train the selected model on the preprocessed dataset to learn the relationships between input text and summaries.Assess the performance of the trained model using evaluation metrics. Deploy the trained model as a web service with CI/CD Deployment on AWS
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
Clone the repository https://github.com/NimraAslamkhan/text-summarizar-project
conda create -n summary python=3.12.1 -y conda activate summary
pip install -r requirements.txt
python app.py open up you local host and port Author: NimraAslmaKhan Data Scientist Email: [email protected]
#with specific access
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EC2 access : It is virtual machine
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ECR: Elastic Container registry to save your docker image in aws
#Description: About the deployment
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Build docker image of the source code
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Push your docker image to ECR
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Launch Your EC2
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Pull Your image from ECR in EC2
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Lauch your docker image in EC2
#Policy:
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AmazonEC2ContainerRegistryFullAccess
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AmazonEC2FullAccess
#optinal
sudo apt-get update -y
sudo apt-get upgrade
#required
curl -fsSL https://get.docker.com -o get-docker.sh
sudo sh get-docker.sh
sudo usermod -aG docker ubuntu
newgrp docker
setting>actions>runner>new self hosted runner> choose os> then run command one by one
AWS_ACCESS_KEY_ID=
AWS_SECRET_ACCESS_KEY=
AWS_REGION = us-east-1
AWS_ECR_LOGIN_URI = demo>> 566373416292.dkr.ecr.ap-south-1.amazonaws.com
ECR_REPOSITORY_NAME = simple-app