# BART Text Summarizer Web App This is an application that uses the BART (Bidirectional and Auto-Regressive Transformers) model for text summarization. It allows users to input text and obtain a concise summary of the input. ## Features - User-friendly interface: The web app provides a simple and intuitive user interface for inputting text and viewing the generated summaries. - BART summarization: The app utilizes the BART model from Hugging Face's Transformers library to generate summaries. The model is pre-trained on a large corpus and has demonstrated excellent performance in text summarization tasks. - Summary customization: The app uses the BART model's capabilities to generate summaries with customizable length and beam search parameters. ## Setup and Usage 1. Clone the repository to your local machine. 2. Install the required Python packages: ``` pip install -r requirements.txt ``` 3. Run the Flask app: ``` python app.py ``` 4. Access the web app: ``` http://localhost:5000 ``` The web app will be running on your local machine. 5. Enter the text you want to summarize in the provided input field. 6. Click the "Summarize" button to generate the summary. ## Customization - Adjusting summary length: You can customize the length of the generated summary by modifying the `max_length` parameter in the `summarize_text()` function. - Modifying beam search: The beam search algorithm used in the BART model can be adjusted by changing the `num_beams` parameter in the `summarize_text()` function. - Styling the web app: You can modify the HTML and CSS in the `index.html` file to customize the appearance and layout of the web app. ## Dependencies The following libraries are used in this project: - Flask: Web framework for creating the web app. - Transformers: Hugging Face library for working with BART model. - Flask-ngrok: Provides a public URL for local web app testing. Please refer to the `requirements.txt` file for specific versions of the dependencies used. ## License This project is licensed under the [MIT License](LICENSE). Feel free to modify and adapt the code to suit your needs. ## Credits This project utilizes the BART model from Hugging Face's Transformers library for text summarization. For more information on BART, please refer to the official documentation: [BART Documentation](https://huggingface.co/transformers/model_doc/bart.html) ## Author Nikol Digital (https://github.com/nikoldigital777) If you have any questions or suggestions, feel free to reach out to me. --- Feel free to customize the README file according to your specific project requirements, adding additional sections or information as needed.