Update app.py

app.py

@@ -118,17 +118,12 @@ custom_css = """
 with gr.Blocks(css=custom_css,fill_width=True) as demo:
     gr.Markdown("""
     # I’m Shalini ☺️ # 
-    This chatbot uses a Retrieval-Augmented Generation (RAG) pipeline.  
-    It is built for the English textbook *Kaleidoscope* 📚.  
-    You can ask questions, and it will retrieve relevant answers from the selected chapter 📝.  
-    
+    This chatbot uses a Retrieval-Augmented Generation (RAG) pipeline, built for  English textbook *Kaleidoscope* 📚.  
+     
     How to use:
+    1️⃣ Pick a chapter 📂 2️⃣ Ask your question 💬 3️⃣ Get context-aware answers ⚡      3. Receive answers generated using RAG from the document content ⚡  
     
-    1. Select the chapter from the dropdown menu 📂  
-    2. Type your question in the chat box 💬  
-    3. Receive answers generated using RAG from the document content ⚡  
-    
-    Powered by LangChain 🛠️, Qdrant 🗄️, and LLaMA 🧠 for fast, accurate, and context-aware responses.
+    Powered by LangChain 🛠️, Qdrant 🗄️, and LLaMA 🧠 .
     """, elem_id="welcome_markdown")