Project Title

App Link: Text Summarizer App

Video Link: Youtube Link

Overview

This project showcases a text summarization application built using Flask and hosted on Azure App Services. It demonstrates an auto-scaling container approach, ideal for scalable web-hosted applications. The core functionality of this app is based on OpenAI's GPT-3.5 model, which is adept at summarizing texts efficiently. This application is a practical application of Flask and GPT-3.5 technology, aiming to provide concise and meaningful summaries of extensive texts.

Introduction

The main script for our application is in main.py, which orchestrates the interaction with the OpenAI API for text summarization. The Flask framework is used to handle web requests and render templates. The key Flask route /summarize is designed to receive text and return its summarized version using GPT-3.5. For the web interface, idv4.html is the primary template, located in the templates directory. This file outlines the UI and UX of our application.

main.py Overview

• Environment Setup: load_dotenv() to load environment variables.
• Flask App Initialization: Flask(__name__).
• OpenAI API Key Configuration: Set using openai.api_key.
• Routes:
  • / for the index route to render the main template.
  • /summarize for processing text summarization requests.

Docker Configuration

The Dockerfile is structured as follows:

1. Base Image: python:3.11
2. Working Directory: /code
3. Dependencies Installation: pip3 install --no-cache-dir -r requirements.txt
4. Copying Source Files
5. Exposing Port: 50505
6. Entry Point: gunicorn with configuration for main:app

Azure Container App

The application is designed to be deployed as a container on Azure, utilizing Azure's capabilities for scalability and reliability.

Preparation and Deployment

To get started with the application:

1. Clone the Repository: Clone this repo to your local machine.
2. Install Dependencies: Use make install to install required packages.
3. API Key Setup: Acquire an OpenAI API key and save it to the .env file.
4. Local Testing: Run main.py locally to test its functionality.
5. Docker Image: Build the Docker image for deployment.
6. Azure CLI: Login to the Azure CLI.
7. Deploy on Azure: Deploy the application as an Azure web app.
8. Usage: Access the application using the provided link to start summarizing texts.