Update app.py

app.py

@@ -1,25 +1,60 @@
 import streamlit as st
-from rag_engine import process_query
+from rag_engine import process_query, load_model
 from utils import setup_all_auth
 
+# Page configuration
 st.set_page_config(page_title="Indian Spiritual RAG")
 
+# Display title
+st.title("Indian Spiritual Texts Q&A")
+
 # Setup all authentication
-setup_all_auth()
+try:
+    setup_all_auth()
+except Exception as e:
+    st.error(f"Authentication error: {str(e)}")
 
-st.title("Indian Spiritual Texts Q&A")
+# Preload the model to avoid session state issues
+try:
+    with st.spinner("Initializing... This may take a minute."):
+        # Force model loading at startup to avoid session state issues
+        load_model()
+        st.success("System initialized successfully!")
+except Exception as e:
+    st.error(f"Error initializing: {str(e)}")
+
+# Query input
 query = st.text_input("Ask your question:")
-top_k = st.slider("Number of sources:", 3, 10, 5)
-word_limit = st.slider("Word limit:", 50, 500, 200)
 
+# Sliders for customization
+col1, col2 = st.columns(2)
+with col1:
+    top_k = st.slider("Number of sources:", 3, 10, 5)
+with col2:
+    word_limit = st.slider("Word limit:", 50, 500, 200)
+
+# Process button
 if st.button("Get Answer"):
     if query:
         with st.spinner("Processing..."):
-            result = process_query(query, top_k=top_k, word_limit=word_limit)
-        
-        st.subheader("Answer:")
-        st.write(result["answer_with_rag"])
-        
-        st.subheader("Sources:")
-        for citation in result["citations"].split("\n"):
-            st.write(citation)
+            try:
+                result = process_query(query, top_k=top_k, word_limit=word_limit)
+                
+                st.subheader("Answer:")
+                st.write(result["answer_with_rag"])
+                
+                st.subheader("Sources:")
+                for citation in result["citations"].split("\n"):
+                    st.write(citation)
+            except Exception as e:
+                st.error(f"Error processing query: {str(e)}")
+    else:
+        st.warning("Please enter a question first.")
+
+# Add helpful information
+st.markdown("---")
+st.markdown("""
+### About this app
+This application uses a Retrieval-Augmented Generation (RAG) system to answer questions about Indian spiritual texts. 
+It searches through a database of texts to find relevant passages and generates answers based on those passages.
+""")