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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