Update README.md

README.md

@@ -1,41 +1,48 @@
 ---
-title: WebScraping with Selenium + RAG
+title: Web Scraping with Selenium + RAG
 emoji: 🕷️
 colorFrom: red
 colorTo: red
-sdk: streamlit
+sdk: docker
 app_file: app.py
 app_port: 8501
 tags:
-- streamlit
-- selenium
-- rag
-- flan-t5
-- web-scraping
+  - streamlit
+  - selenium
+  - rag
+  - flan-t5
+  - web-scraping
 pinned: true
-short_description: A Streamlit RAG Selenium + FLAN-T5-small. 
+short_description: Selenium RAG using FLAN-T5-small
 ---
 
+# 🕷️ Web Scraping + RAG Chatbot
 
-# 🕷️ Web Scraping + RAG Chatbot 🚀
+This project combines **Selenium web scraping** with **Retrieval-Augmented Generation (RAG)** to build an intelligent chatbot that can extract information from websites and answer questions about the content.
 
-This project combines **Selenium web scraping** with **Retrieval-Augmented Generation (RAG)** to build an intelligent chatbot.  
-It can scrape live websites, index the content into a **FAISS vector store**, and let you ask natural language questions.  
+![Demo](https://img.shields.io/badge/Demo-Live%20Demo-blue) 
+![Python](https://img.shields.io/badge/Python-3.10%2B-blue) 
+![License](https://img.shields.io/badge/License-MIT-green)
 
-### 🔹 Features
-- 🌐 Scrape dynamic websites using **Selenium**  
-- 📚 Store & retrieve content with **FAISS embeddings**  
-- 🧠 Answer questions using **FLAN-T5-small** (runs on CPU)  
-- 🎨 Simple **Streamlit UI** for interaction  
+## ✨ Features
 
-### 🚀 Usage
-1. Enter a URL to scrape.  
-2. The system extracts + indexes the text.  
-3. Ask questions — the chatbot answers using RAG.  
+- 🌐 **Web Scraping**: Extract content from dynamic websites using Selenium
+- 📚 **Vector Storage**: Index and retrieve content using FAISS embeddings
+- 🧠 **Question Answering**: Generate answers using FLAN-T5-small model
+- 🎨 **User-Friendly Interface**: Simple Streamlit UI for interaction
+- 🐳 **Dockerized**: Ready for deployment on Hugging Face Spaces
 
----
+## 🚀 Quick Start
+
+### Prerequisites
 
-👩‍💻 **Tech Stack**: `Streamlit`, `Selenium`, `LangChain`, `FAISS`, `Transformers`  
+- Python 3.10+
+- Docker (for containerized deployment)
+- Hugging Face account (for deployment)
 
-📖 Check the [docs](https://docs.streamlit.io) for customizing your Streamlit app.  
+### Local Installation
 
+1. Clone the repository:
+```bash
+git clone https://huggingface.co/spaces/your-username/your-space-name
+cd your-space-name