https://github.com/dnyaneshvn/text-summarizer
Text Summarizer: Conquer information overload with this smart app that distills key points from lengthy texts and PDFs.
https://github.com/dnyaneshvn/text-summarizer
bart pypdf2 streamlit text-summarizer transformers
Last synced: 6 months ago
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Text Summarizer: Conquer information overload with this smart app that distills key points from lengthy texts and PDFs.
- Host: GitHub
- URL: https://github.com/dnyaneshvn/text-summarizer
- Owner: Dnyaneshvn
- Created: 2024-09-11T15:44:26.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-09-11T15:57:00.000Z (almost 2 years ago)
- Last Synced: 2025-01-22T08:37:32.755Z (over 1 year ago)
- Topics: bart, pypdf2, streamlit, text-summarizer, transformers
- Language: Python
- Homepage:
- Size: 79.1 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# 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.