README.md

---
title: Text Summarization
emoji: 💬
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.29.1
app_file: app.py
pinned: false
license: mit
short_description: For Text Summarization
---
# 📘 AI Text Summarizer + Named Entity Recognition (NER)

This web app helps users quickly understand large blocks of text by:
- Generating concise summaries using the `bart-large-cnn` model
- Highlighting important entities such as people, organizations, and locations with `dslim/bert-base-NER`

Built with 🤗 Hugging Face Transformers and Gradio.

---

## 🚀 Features

- ✨ **Text Summarization**  
  Automatically condenses long-form text into short, meaningful summaries.

- 🧠 **Named Entity Recognition (NER)**  
  Highlights key entities (e.g., names, places, organizations) in the summary for better context.

- 🌐 **User-Friendly Interface**  
  Easy-to-use web interface with live examples. Just paste your text (100+ words), and get insights instantly!

---

## 🛠 Models Used

- [`facebook/bart-large-cnn`](https://huggingface.co/facebook/bart-large-cnn) – For text summarization  
- [`dslim/bert-base-NER`](https://huggingface.co/dslim/bert-base-NER) – For named entity recognition

---

## 🔧 How It Works

1. User inputs at least 100 words of text.
2. The app summarizes the input using the BART model.
3. The summary is passed to the BERT model to extract and highlight named entities.
4. Output is displayed with highlights over the summary text.

---

## 💻 Running Locally

```bash
git clone https://huggingface.co/spaces/YOUR-USERNAME/YOUR-APP-NAME
cd YOUR-APP-NAME
pip install -r requirements.txt
python app.py