Text Summarizer

Text Summarizer is a Streamlit web application that uses the BART (Bidirectional and Auto-Regressive Transformers) model to generate concise summaries of long texts. It supports both direct text input and PDF file uploads.

Features

• Summarize text input directly
• Summarize content from uploaded PDF files
• Uses the state-of-the-art BART model for high-quality summaries
• User-friendly interface built with Streamlit

Requirements

• Python 3.7+
• Streamlit
• PyTorch
• Transformers (Hugging Face)
• PyPDF2

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/text-summarizer.git
   cd text-summarizer
   

2. Install the required packages:

   pip install -r requirements.txt
   

   Note: Make sure to create a requirements.txt file with the following content:

   streamlit
   torch
   transformers
   PyPDF2
   

Usage

1. Run the Streamlit app:

   streamlit run app.py
   

2. Open your web browser and go to http://localhost:8501.

3. Choose your input method:

   • "Enter Text": Type or paste your text directly into the text area.
   • "Upload PDF": Upload a PDF file to summarize its content.

4. Click the "Summarize" button to generate the summary.

How it Works

1. The app uses the BART model (facebook/bart-large-cnn) for text summarization.
2. For PDF inputs, it extracts text using PyPDF2 before summarization.
3. The model generates a summary with a maximum length of 150 tokens and a minimum length of 50 tokens.

Customization

You can adjust the summarization parameters in the summarize_text function:

• max_length: Maximum length of the generated summary (default: 150)
• min_length: Minimum length of the generated summary (default: 50)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.