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import streamlit as st
# FIRST: Set page config before ANY other Streamlit command
st.set_page_config(page_title="Indian Spiritual RAG")
# THEN: Import your modules
from rag_engine import process_query, load_model
from utils import setup_all_auth
# Display title
st.title("Indian Spiritual Texts Q&A")
# Setup all authentication
try:
setup_all_auth()
except Exception as e:
st.error(f"Authentication error: {str(e)}")
# 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:")
# 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..."):
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.
""") |