Nipun Agarwal

Wanna be Software Engineer.

Want to be an entrepreneur.

Summariser

I developed a web application that leverages generative AI to summarize lengthy articles, reports, or transcripts into concise summaries..

Project Title: AI-Driven Event Summarizer Platform
Submission Date: 20/12/2024
Hosted Application Link: AI-Driven Event Summarizer
GitHub Repository: Summariser Repository

Overview

As part of the internship selection process at Nebula9.ai, I developed a web application that leverages generative AI to summarize lengthy articles, reports, or transcripts into concise summaries. This project provided an excellent opportunity to showcase my full-stack development skills while integrating cutting-edge AI technologies.

Objectives

  1. AI Integration: Utilize generative AI models for text summarization.
  2. User Authentication: Implement JWT-based secure user authentication.
  3. Data Storage: Enable saving and retrieving past summaries in a database.
  4. Cloud Deployment: Deploy the application on AWS using Docker and GitHub Actions.

Key Features

  • Frontend: Built a responsive user interface using React.
  • Backend: Developed RESTful APIs with Node.js and Express.
  • Database: Used PostgreSQL for managing user data and summary history.
  • AI Integration: Leveraged Hugging Face Transformers for text summarization.
  • Authentication: Implemented JWT-based authentication for secure user sessions.
  • Deployment: Hosted the application on AWS, ensuring scalability and reliability.

Technical Stack

Frontend

  • React: For building a dynamic and responsive user interface.
  • Tailwind CSS: For styling and achieving a clean, minimal design.

Backend

  • Node.js (Express): For creating RESTful APIs to handle file uploads and interact with the AI model.
  • MongoDB: For data storage.

AI and Machine Learning

  • Hugging Face Transformers: For text summarization using pre-trained models.

Deployment

  • AWS: Used AWS EC2 for hosting the application.
  • Docker: Containerized the application for consistent deployment.
  • GitHub Actions: Automated the CI/CD pipeline for seamless deployment.

Challenges and Solutions

  1. Integrating Generative AI:
    • Researched and implemented Hugging Face Transformers for efficient text summarization.
  2. Secure Authentication:
    • Used JWT tokens to protect user data and manage sessions effectively.
  3. Cloud Deployment:
    • Set up AWS EC2 for hosting and used Docker to ensure consistent runtime environments.

Key Learnings

  • Gained experience in integrating AI models into full-stack applications.
  • Improved my understanding of secure authentication and role-based access control (RBAC).
  • Developed expertise in cloud deployment with AWS, Docker, and GitHub Actions.

Outcome

The project successfully met all requirements, including:

  • A functional, user-friendly interface.
  • AI-driven summarization capabilities.
  • Secure authentication and data management.
  • Deployment on AWS with robust performance.

This assignment not only challenged my technical skills but also allowed me to explore practical applications of AI in real-world scenarios. I am grateful for the opportunity and look forward to applying these learnings in future projects.