Update app.py

app.py

@@ -1,4 +1,6 @@
 import streamlit as st
+st.set_page_config(page_title="RAG Book Analyzer", layout="wide")  # Must be the first Streamlit command
+
 import torch
 import numpy as np
 import faiss
@@ -112,7 +114,6 @@ def generate_answer(query, context):
 # ------------------------
 # Streamlit UI
 # ------------------------
-st.set_page_config(page_title="RAG Book Analyzer", layout="wide")
 st.title("RAG-Based Book Analyzer")
 st.write("Upload a book (PDF, TXT, DOCX) to get a summary and ask questions about its content.")
 
@@ -139,7 +140,7 @@ if uploaded_file:
             # Retrieve top 3 relevant chunks as context
             query_embedding = embedder.encode([query])
             faiss.normalize_L2(query_embedding)
-            distances, indices = index.search(query_embedding, k=3)
+            distances, indices = st.session_state.index.search(query_embedding, k=3)
             retrieved_chunks = [chunks[i] for i in indices[0] if i < len(chunks)]
             context = "\n".join(retrieved_chunks)
             answer = generate_answer(query, context)