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Text Summarizer App

This is a simple Text Summarizer App built with Python using the Streamlit library.

Overview

The Text Summarizer App allows users to input text and receive a summarized version of the text, you can either upload or copy and paste text. Additionally, the app provides sentiment analysis for the entered text. Note: Only pdf,docx,txt files can be uploaded for now.

Features

  • Text Summarization: The app uses natural language processing techniques to generate a concise summary of the input text.

  • Sentiment Analysis: Users can perform sentiment analysis on the entered text to determine if it is positive, negative, or neutral.

Getting Started

Prerequisites

Python (version 3.10.13)

Install the required packages by running: pip install -r requirements.txt

How to Run

  1. Clone the repository:
  1. Navigate to the project directory:
  • cd Text_summarizer
  1. Run the app:
  • streamlit run TextAnalyzer.py
  1. Access the app in your web browser at http://localhost:8501.

Usage

  • Enter text in the provided text area.
  • Click the "Generate Summary" button to get a summarized version of the text.
  • Optionally, click the "Perform Sentiment Analysis" button to analyze the sentiment of the entered text.

Dependencies

  • NLTK: Natural Language Toolkit for natural language processing.
  • Streamlit: Open-source Python library for creating web applications.
  • BART: Bidirectional and Auto-Regressive Transformers, a transformer-based model developed by Facebook AI. It is designed for various natural language processing tasks, including text generation and summarization.

Limitations

  • Sentiment analysis function performs poorly when dealing with sarcastic text (best used for academic or more formal text).
  • Only docx,txt and pdf files can be uploaded for now

Contributing

If you have suggestions or found a bug, feel free to open an issue or create a pull request.

License

This project is licensed under the MIT License.

About

A streamlit app that uses BART and NLTK for text summarization and sentiment analysis.

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