Document Summarizer

The Document Summarizer leverages Hugging Face’s facebook/bart-large-cnn model to simplify the process of summarizing lengthy documents. This app allows users to upload PDF or plain text files and generates concise, human-readable summaries. It combines a ReactJS (Vite) frontend with a Flask backend for efficient processing.

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Features

• File Upload Support: Accepts both .pdf and .txt files for summarization.
• Concise Summaries: Generates readable and concise summaries of lengthy documents.
• Secure Processing: Files are temporarily stored during processing and deleted after use.
• Modern Technology Stack:
  • Frontend: ReactJS and Vite.
  • Backend: Flask.
  • Summarization: Hugging Face’s facebook/bart-large-cnn.

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Tech Stack

Frontend

• ReactJS with Vite for responsive and fast UI.
• Axios for API requests.

Backend

• Flask for API handling and text processing.
• pdfplumber for extracting text from PDF files.

AI Model

• Hugging Face’s facebook/bart-large-cnn for abstractive text summarization.

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Installation and Setup

1. Clone the Repository

git clone git@github.com:allanninal/document-summarizer.git
cd document-summarizer

2. Backend Setup

1. Create and activate a virtual environment:

   python3.12 -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate     # Windows

2. Install dependencies using requirements.txt from the backend folder:

   pip install -r backend/requirements.txt

3. Run the Flask backend:

   python backend/app.py

3. Frontend Setup

1. Navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the React development server:

   npm run dev

Visit the app at http://localhost:5173.

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How It Works

1. Upload a Document: Upload .pdf or .txt files through the frontend.
2. Text Extraction: The backend extracts text using pdfplumber or reads plain text files.
3. Summarization: The extracted text is processed through Hugging Face’s facebook/bart-large-cnn model to generate a concise summary.
4. Display Results: The frontend displays the summary in real time.

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Future Enhancements

1. Support for Large Documents: Chunk large files and summarize them section by section.
2. Multilingual Summarization: Add support for non-English texts using multilingual models.
3. Save Summaries: Enable users to download summaries in .txt or .pdf formats.
4. Enhanced UI/UX: Add drag-and-drop file upload and progress indicators.
5. Customizable Summaries: Allow users to choose between short, medium, or detailed summaries.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Support

If you find this project helpful, consider supporting me on Ko-fi:
ko-fi.com/allanninal

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Explore More Projects

For more exciting projects, check out my list of AI Mini Projects:
Mini AI Projects GitHub List