/Research-Asist-Tool

This project aims to simplify and summarize scientific data , convert it to a audio format as a podcast , and create a power point presentation from the paper. This helps researchers, academics and students altogether.

Primary LanguageJavaScript

Research Assist Tool

Overview

Research Assist Tool simplifies and summarizes scientific data, converts it into audio podcasts, and creates PowerPoint presentations. Ideal for researchers, academics, and students.

Features

  • Automated Text Mining: Extracts relevant segments using the TF-IDF algorithm.
  • Content Generation: Summarizes with BART model.
  • PowerPoint Presentation: Creates slides with Python pptx.
  • Text-to-Speech: Converts summaries to audio using VidLab API.

Setup

  1. Clone Repository: 
    git clone https://github.com/xreedev/Research-Asist-Tool.git
  2. Install Dependencies: 
    pip install -r requirements.txt
  3. Start Backend API: 
    python app.py
  4. Run Frontend: 
    npm install
cd Frontend
npm start

Usage

  1. Pre-trained Models: Download and configure BART models.
  2. Summarization: Provide the link through the React website.
  3. Output: Access summaries, presentations, and audio files in the Outputs folder.

File Structure

Research-Asist-Tool/
│
├── app.py                     # Backend API script
├── requirements.txt           # Dependencies
├── README.md                  # Project documentation
│
├── Frontend/                  # Frontend files
│   ├── src/
│   │   ├── App.js             # Main React component
│   │   ├── index.js           # Entry point for React
│   │   ├── components/        # React components
│   │   ├── services/          # API service functions
│   │   └── styles/            # CSS files
│   ├── public/
│   │   ├── index.html         # Main HTML file
│   │   └── ...
│   └── package.json           # Node.js dependencies
│
├── Models/                    # Pre-trained models
│   ├── bart/                  # BART models
│   ├── tf-idf/                # TF-IDF models
│   └── ...
│
├── Outputs/                   # Generated outputs
│   ├── summaries/             # Text summaries
│   ├── presentations/         # PowerPoint files
│   └── audio/                 # Audio files
│
└── Utils/                     # Utility scripts
    ├── text_mining.py         # Text mining functions
    ├── summarization.py       # Summarization functions
    ├── ppt_creation.py        # PowerPoint generation
    └── text_to_speech.py      # Text to speech conversion

Data Flow

  1. Text Mining:

    • Input: Scientific paper (PDF/URL)
    • Process: Extracts key segments using text_mining.py
    • Output: Relevant text segments

  2. Summarization:

    • Input: Extracted text segments
    • Process: Summarizes using BART model (summarization.py)
    • Output: Summarized text

  3. PowerPoint Creation:

    • Input: Summarized text
    • Process: Generates slides using ppt_creation.py
    • Output: PowerPoint file

  4. Text-to-Speech:

    • Input: Summarized text
    • Process: Converts to audio using text_to_speech.py
    • Output: Audio file

Contribution