import streamlit as st
import time
# 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
# Define state for input box style
if 'input_focused' not in st.session_state:
st.session_state.input_focused = False
# Function to update input focus state
def set_input_focus(focused):
st.session_state.input_focused = focused
# Display title with custom styling
st.markdown("""
Indian Spiritual Texts Q&A
""", unsafe_allow_html=True)
# Initialize session state
if 'initialized' not in st.session_state:
st.session_state.initialized = False
if 'last_query' not in st.session_state:
st.session_state.last_query = ""
if 'submit_clicked' not in st.session_state:
st.session_state.submit_clicked = False
# Setup all authentication
if not st.session_state.initialized:
try:
setup_all_auth()
except Exception as e:
st.error(f"Authentication error: {str(e)}")
# Preload the model to avoid session state issues
if not st.session_state.initialized:
try:
init_message = st.empty()
init_message.info("Hang in there! We are setting the system up for you. 😊")
# Force model loading at startup to avoid session state issues
load_model()
# Keep the message for 2 seconds before replacing it
time.sleep(2)
init_message.success("System initialized successfully!")
st.session_state.initialized = True
except Exception as e:
st.error(f"Error initializing: {str(e)}")
# Function to process when enter is pressed or button is clicked
def process_input():
st.session_state.last_query = st.session_state.query_input
st.session_state.query_input = ""
st.session_state.submit_clicked = True
# Query input with callback for Enter key
query = st.text_input(
"Ask your question:",
key="query_input",
on_change=process_input
)
# Add JavaScript to change input border color
st.markdown("""
""", unsafe_allow_html=True)
# Display the current question if there is one
if st.session_state.last_query:
st.markdown("### Current Question:")
st.info(st.session_state.last_query)
# 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") or st.session_state.submit_clicked:
if st.session_state.last_query:
# Reset the submit flag
st.session_state.submit_clicked = False
# Single processing spinner for the entire operation
with st.spinner("Processing your question..."):
try:
result = process_query(st.session_state.last_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.
""")