update.Readme

README.md

@@ -1,14 +1,59 @@
 ---
 title: Text Summarization
 emoji: 💬
-colorFrom: yellow
+colorFrom: blue
 colorTo: purple
 sdk: gradio
-sdk_version: 5.0.1
+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
 
-An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).