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import os
import json
import streamlit as st
from ind_checklist_stlit import load_preprocessed_data, init_vector_store, create_rag_chain
# Prevent Streamlit from auto-reloading on file changes
os.environ["STREAMLIT_WATCHER_TYPE"] = "none"
# Define the preprocessed file path
PREPROCESSED_FILE = "preprocessed_docs.json"
# Caching function to prevent redundant RAG processing
@st.cache_data
def cached_response(question: str):
"""Retrieve cached response if available, otherwise compute response."""
return st.session_state.rag_chain.invoke({"question": question})["response"]
def main():
st.title("Appian IND Application Assistant")
st.markdown("Chat about Investigational New Drug Applications")
# Button to clear chat history
if st.button("Clear Chat History"):
st.session_state.messages = []
if hasattr(st, "rerun"):
st.rerun()
else:
st.experimental_rerun()
# Initialize session state
if "messages" not in st.session_state:
st.session_state.messages = []
# Load preprocessed data and initialize the RAG chain
if "rag_chain" not in st.session_state:
if not os.path.exists(PREPROCESSED_FILE):
st.error(f"❌ Preprocessed file '{PREPROCESSED_FILE}' not found. Please run preprocessing first.")
return # Stop execution if preprocessed data is missing
with st.spinner("🔄 Initializing knowledge base..."):
documents = load_preprocessed_data(PREPROCESSED_FILE)
vectorstore = init_vector_store(documents)
st.session_state.rag_chain = create_rag_chain(vectorstore.as_retriever())
# Display chat history
for message in st.session_state.messages:
role = message["role"]
content = message["content"]
if hasattr(st, "chat_message"):
with st.chat_message(role):
st.markdown(content)
else:
st.write(f"**{role.capitalize()}:** {content}")
# Chat input and response handling
# Check if st.chat_input is available (Streamlit 1.2 or higher)
if hasattr(st, "chat_input"):
prompt = st.chat_input("Ask about IND requirements")
else:
prompt = st.text_input("Ask about IND requirements")
if prompt:
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message
if hasattr(st, "chat_message"):
with st.chat_message("user"):
st.markdown(prompt)
else:
st.write(f"**User:** {prompt}")
# Generate response (cached if already asked before)
response = cached_response(prompt)
if hasattr(st, "chat_message"):
with st.chat_message("assistant"):
st.markdown(response)
else:
st.write(f"**Assistant:** {response}")
# Store bot response in chat history
st.session_state.messages.append({"role": "assistant", "content": response})
if __name__ == "__main__":
main()
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