Text Summarization LLM APP

• License: (MIT), Copyright (C) 2024, Author Phil Chen
  • This is a example application the author of this repository is not liable for damages or losses arising from your use or inability to use the code.

Description

This repo is an example app showing text summarization leveraging Minstral Large model available from AWS Bedrock. For the frontend UI Streamlit is being used.

Prerequisites for macOS local setup

• Amazon Web Services Account
• Enable Amazon Bedrock Access (Specifically Mistral-Large) see: Manage access to Amazon Bedrock foundation models
• AWS CLI installed
• AWS CLI IAM user with Full Amazon Bedrock Access
• Verified on Python 3.10, 3.11, 3.12
• Anaconda or Miniconda installed
• AWS Default Region is set to us-east-1 you can change the region in the text_summarization_llm_app.py file under region_name='us-east-1'

Prerequisites for EC2 Ubuntu Linux instance setup

• Amazon Web Services Account
• Enable Amazon Bedrock Access (Specifically Mistral-Large) see: Manage access to Amazon Bedrock foundation models
• EC2 Instance Role with AmazonBedrockFullAccess Policy Attached (note you can make this more secure by making a custom policy)
• Verified on Python 3.10, 3.11, 3.12
• Verified on EC2 Instance Ubuntu 22.04 and Ubuntu 24.04
• Virtualenv
• AWS Default Region is set to us-east-1 you can change the region in the text_summarization_llm_app.py file under region_name='us-east-1'

AWS Resource Cost

As with most AWS services you will incur costs for usage.

• Pricing:
  • https://aws.amazon.com/bedrock/pricing/
  • https://aws.amazon.com/ec2/pricing/on-demand/

macOS laptop local setup

conda create -n "text-summarization-llm-app" python=3.11.0

git clone git@github.com:nethacker/text-summarization-llm-app.git

cd text-summarization-llm-app

pip install -r requirements.txt

Run macOS laptop local setup

To run text summarization leveraging AWS Bedrock (Mistral-Large)

streamlit run text_summarization_llm_app.py

You can reach the app at http://localhost:8501/

EC2 Ubuntu linux instance setup steps

(assumes you have a ubuntu user with /home/ubuntu)

Install some dependencies

sudo apt -y update

sudo apt -y install build-essential openssl

sudo apt -y install libpq-dev libssl-dev libffi-dev zlib1g-dev

sudo apt -y install python3-pip python3-dev

sudo apt -y install nginx

sudo apt -y install virtualenvwrapper

Clone the GIT Repository

cd /home/ubuntu

git clone https://github.com/nethacker/text-summarization-llm-app.git

Setup the Python environment

virtualenv text-summarization-llm-app_env

source text-summarization-llm-app_env/bin/activate

Install the Text Summarization LLM application package dependencies

cd /home/ubuntu/text-summarization-llm-app

pip install -r requirements.txt

Setup systemd to daemonize the Text Summarization LLM application (Port 8080)

sudo cp systemd/text-summarization-llm-app.service /etc/systemd/system/

sudo systemctl start text-summarization-llm-app

sudo systemctl enable text-summarization-llm-app.service

Install NGINX to help scale and handle connections (Port 80)

sudo cp nginx/nginx_text-summarization-llm-app.conf /etc/nginx/sites-available/nginx_text-summarization-llm-app.conf

sudo rm /etc/nginx/sites-enabled/default

sudo ln -s /etc/nginx/sites-available/nginx_text-summarization-llm-app.conf /etc/nginx/sites-enabled

sudo systemctl restart nginx

You can reach the app at http://{yourhost}.

Notes

• Make sure to open up port 80 in your EC2 Security Group associated to the instance.
• For HTTPS (TLS) you can use AWS ALB or AWS CloudFront
• This application does not take into consideration security controls, that is your responsibility.
• Please read Amazon Bedrock FAQ's for general questions about AWS LLM resources used.