app.py

import streamlit as st
from llama_index import VectorStoreIndex, ServiceContext, Document
from llama_index.llms import OpenAI
import openai
from llama_index import SimpleDirectoryReader
# from PyPDF2 import PdfReader

st.set_page_config(page_title="Chat with the Bain Report (M&A)", page_icon="🦙", layout="centered", initial_sidebar_state="auto", menu_items=None)
openai.api_key = st.secrets.openai_key
st.title("Bain Reports (M&A, PE & Tech)")
         
if "messages" not in st.session_state.keys(): # Initialize the chat messages history
    st.session_state.messages = [
        {"role": "assistant", "content": "Ask me a question about Bain Report!"}
    ]

@st.cache_resource(show_spinner=False)
def load_data():
    with st.spinner(text="Loading and indexing the Bain Report docs – hang tight! This should take 1-2 minutes."):
        reader = SimpleDirectoryReader(input_dir="./data", recursive=True)
        docs = reader.load_data()
        service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-4", temperature=0.8, system_prompt="You are an expert on the Bain Reports. Please provide detailed insights from the Bain Reports on [specific topic or question]."))
        index = VectorStoreIndex.from_documents(docs, service_context=service_context)
        return index

index = load_data()
# chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True, system_prompt="You are an expert on the Bain Report and your job is to answer technical questions. Assume that all questions are related to the Bain Report. Keep your answers technical and based on facts – do not hallucinate features.")

if "chat_engine" not in st.session_state.keys(): # Initialize the chat engine
        st.session_state.chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)

if prompt := st.chat_input("Your question"): # Prompt for user input and save to chat history
    st.session_state.messages.append({"role": "user", "content": prompt})

for message in st.session_state.messages: # Display the prior chat messages
    with st.chat_message(message["role"]):
        st.write(message["content"])

# If last message is not from assistant, generate a new response
if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            response = st.session_state.chat_engine.chat(prompt)
            st.write(response.response)
            message = {"role": "assistant", "content": response.response}
            st.session_state.messages.append(message) # Add response to message history