This project implements a web application for summarizing long text articles using an AI-based text summarization model. It consists of a frontend interface for user input, a backend API for text summarization, and a MongoDB database to store the results.

• Frontend: HTML, CSS, JavaScript
• Backend: Python, Flask
• Database: MongoDB
• AI Model: Transformer's model (summarization pipeline)
• Deployment: Waitress (for serving Flask app)

• The frontend is designed to be simple, focusing on usability for inputting long text and displaying the summarization result.
• The backend encapsulates the AI logic for text summarization within a single endpoint /summarize.
• MongoDB Atlas is chosen for creating the database cluster for storing summarized text and insights. The results are saved with a timestamp for reference.
• Transformer's model is used for text summarization.
• The application is hosted locally using Waitress for serving the Flask app. Deployment to a cloud provider like AWS is optional and not implemented in this version.
• The maximum and minimum word length fosr the summarised output is specified as 130 and 30 respectively.
• Pushing .env file to github is usually not a good practise, but I have included it to avoid such configurations to be directly defined in the code itself. This is just for the sake of this assignment and the .env file just contains the mongodb URI, so should be okay.

Before running the project, ensure you have the following installed:

• Python (3.7 or higher)

1. Clone the repository:

git clone https://github.com/yourusername/text-summarizer.git
cd text-summarizer

2. Install backend dependencies:

pip install -r requirements.txt

2. Start the backend server:

python main.py

3. Access the application in your web browser:

http://localhost:3000

4. Enter a long text article into the text input field and click "Summarize Text" button.
5. Wait for the summarization process to complete. You'll see a spinner while the process is ongoing.
6. Once the summary is generated, it will be displayed below the input field.

Changing AI Model: The backend uses an AI model for text summarization. You can modify the summarization logic in the app.py file or replace it with another AI service. Database Configuration: If you want to use a different database or configure MongoDB settings, update the app.py file accordingly. Frontend Styling: Customize the frontend appearance by modifying the HTML and CSS files in the frontend/public directory.

To deploy the application to a cloud provider (e.g., AWS, Heroku), follow their respective deployment guides and ensure the necessary configurations are made for the backend, frontend, and database.