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import streamlit as st
from rag_components import load_documents, split_documents, create_embeddings, setup_vector_store, create_qa_chain
import os
st.set_page_config(page_title="Document Chatbot")
st.title("Chat with your Documents")
@st.cache_resource
def initialize_rag_components(file_path="src/me.txt"):
"""Initializes and caches RAG components."""
if not os.path.exists(file_path):
st.error(f"Error: Document file not found at {file_path}")
return None, None
documents = load_documents(file_path)
docs = split_documents(documents)
embeddings = create_embeddings()
retriever = setup_vector_store(docs, embeddings)
qa_chain = create_qa_chain(retriever)
return qa_chain, retriever
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 a question about the document"):
# Display user message in chat message container
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
message_placeholder = st.empty()
full_response = ""
try:
# Assuming qa_chain.stream() yields dictionaries with a 'result' key
for chunk in qa_chain.stream(prompt):
if 'result' in chunk:
full_response += chunk['result']
message_placeholder.markdown(full_response + "▌")
message_placeholder.markdown(full_response)
except Exception as e:
st.error(f"An error occurred: {e}")
full_response = "Sorry, I could not process your request."
# 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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