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metadata
title: Summarizer Bart
emoji: 💻
colorFrom: blue
colorTo: gray
sdk: streamlit
sdk_version: 1.29.0
app_file: app.py
pinned: false

NLP Text Analyzer

This project is a Python-based Natural Language Processing (NLP) Text Analyzer that uses Streamlit for the user interface and leverages Hugging Face's transformers library to perform text summarization using the BART model, visualize word clouds, and display the most common words in a given text.

Overview

The NLP Text Analyzer consists of the following functionalities:

  • Text Summarization: Utilizes the BART model from Hugging Face's transformers library to generate a summary of the user-provided text.
  • Word Cloud Generation: Generates a word cloud visualization based on the input text.
  • Most Common Words: Displays the top 10 most common words and their frequencies in the input text.

Libraries Used

  • streamlit: Used for building the web-based user interface.
  • transformers (from Hugging Face): Provides pre-trained models for NLP tasks. Specifically, the BartForConditionalGeneration and BartTokenizer are used for text summarization.
  • nltk: Utilized for text processing tasks like tokenization and frequency analysis.
  • wordcloud: Enables the creation of word cloud visualizations.
  • matplotlib: Used for plotting word cloud and other visualizations.

Usage

Setup

  1. Install the necessary Python dependencies listed in requirements.txt.
  2. Run the Streamlit app locally using the command: streamlit run your_script.py.

Functionality

  1. Text Input: Enter your text in the provided text area.
  2. Summary: Displays a summary of the input text using the BART model.
  3. Word Cloud: Shows a visual representation of word frequency in the input text.
  4. Most Common Words: Provides a table showing the top 10 most common words and their frequencies.

Collab Notebook

Access the Colab notebook used for development here.

Acknowledgments

  • The project utilizes the power of Hugging Face's transformers library for NLP tasks.
  • The word cloud visualization is created using the wordcloud library.

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference