SummaHub

Welcome to SummaHub, an innovative platform designed to revolutionize the way you interact with text. Utilizing cutting-edge natural language processing (NLP) technologies, SummaHub offers powerful tools for text summarization and question generation. Our goal is to enhance learning, comprehension, and efficiency by providing users with concise summaries and insightful questions derived from extensive texts.

Features

SummaHubAI is packed with features aimed at improving your productivity and understanding of text materials:

• Text Summarization: Quickly condense long articles, papers, and documents into short, digestible summaries that retain the core information and insights.
• Question Generation: Enhance comprehension and engagement by generating relevant questions based on the summarized content, fostering deeper understanding.
• User Authentication and Authorization: Secure access to your account with robust authentication mechanisms, ensuring your data's privacy and security.
• Note Taking: Seamlessly integrate note-taking capabilities to jot down important points, annotations, and personal insights directly within the platform.
• User-friendly Interface: Navigate through the platform with ease thanks to an intuitive and straightforward user interface designed for optimal user experience.

Getting Started

To get started with SummaHubAI, follow these simple steps:

1. Clone the Repository

   git clone https://github.com/bereketo/SummaHub.git

2. Install Dependencies

   Navigate to the project directory and install the necessary dependencies.

   • For the backend:

     cd web
     npm install

   • For the frontend:

     cd web/frontend
     npm install

3. Running the Application

   • To run the backend server:

     npm run server

   • To run the frontend application:

     npm run client

   • To run both concurrently:

     npm run dev

4. Accessing the Application

   Open http://localhost:3000 in your browser to view the SummaHubAI platform.

Project Structure

The project is structured as follows:

• notebooks/: Contains Jupyter notebooks for model training and experimentation.
• src/: Houses the core backend logic and data processing scripts.
• web/: Contains both the backend and frontend code for the web application.
  • backend/: Backend server and API implementation.
  • frontend/: React-based frontend application.