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import streamlit as st
from rag_components import load_documents, split_documents, create_embeddings, setup_vector_store, create_qa_chain, create_streaming_response
import os
# Ensure cache directories exist
cache_dirs = ["/tmp/huggingface_cache", "/tmp/transformers_cache", "/tmp/hf_hub_cache", "/tmp/sentence_transformers_cache"]
for cache_dir in cache_dirs:
os.makedirs(cache_dir, exist_ok=True)
st.set_page_config(
page_title="Assistant",
page_icon="🤖",
layout="wide",
initial_sidebar_state="collapsed"
)
st.title("Juma's Assistant")
st.markdown("---")
@st.cache_resource
def initialize_rag_components(file_path="./me.txt"):
"""Initializes and caches RAG components with better error handling."""
try:
if not os.path.exists(file_path):
st.error(f"Error: Document file not found at {file_path}")
return None, None
with st.spinner("Loading documents..."):
documents = load_documents(file_path)
st.info(f"Loaded {len(documents)} documents")
with st.spinner("Splitting documents into chunks..."):
docs = split_documents(documents)
st.info(f"Split into {len(docs)} chunks")
with st.spinner("Creating embeddings (this may take a while)..."):
embeddings = create_embeddings()
with st.spinner("Setting up vector store..."):
retriever = setup_vector_store(docs, embeddings)
with st.spinner("Initializing QA chain..."):
qa_chain = create_qa_chain(retriever)
st.success("Welcome! Ask me anything about Juma.")
return qa_chain, retriever
except Exception as e:
st.error(f"Error: initializing: {e}")
st.info("This might be due to model download issues. Please try refreshing the page.")
return None, None
qa_chain, retriever = initialize_rag_components()
if qa_chain is not None:
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# React to user input
if prompt := st.chat_input("Ask me any question..."):
# Display user message in chat message container
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
st.write(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
message_placeholder = st.empty()
try:
# Use the new streaming response function
full_response = create_streaming_response(qa_chain, prompt, message_placeholder)
except Exception as e:
st.error(f"An error occurred: {e}")
full_response = "I apologize, but I encountered an error while processing your question. Please try again."
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": full_response})
else:
st.warning("RAG components could not be initialized. Please check the document file path.")
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