Update app.py

app.py

@@ -118,18 +118,16 @@ custom_css = """
 with gr.Blocks(css=custom_css,fill_width=True) as demo:
     gr.Markdown("""
     # I’m Shalini ☺️ # 
-   This is a chatbot built on a Retrieval-Augmented Generation (RAG) pipeline for a specific document — the NCERT Class 12 English textbook Kaleidoscope 📚.
-Ask questions, and the chatbot will retrieve relevant information directly from the selected chapter 📝.
-
-How to use:
-
-Select the chapter from the dropdown menu 📂.
-
-Type your question in the chat box 💬.
-
-Receive answers generated using RAG from the document content ⚡.
-
-Powered by LangChain 🛠️, Qdrant 🗄️, and LLaMA 3.3 🧠 for fast, accurate, and context-aware responses.
+    This is a chatbot built on a Retrieval-Augmented Generation (RAG) pipeline for a specific document — the English textbook Kaleidoscope 📚.
+    Ask questions, and the chatbot will retrieve relevant information directly from the selected chapter 📝.
+    
+    How to use:
+    
+    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.
     """,elem_id="welcome_markdown")
     
     chapter_dir={"Broken Images":"Dataset/Drama/Broken_images.pdf",