app.py

## Import necessary libraries
## Llamaindex library recently updated
import streamlit as st
import openai
import os

from llama_index.core import VectorStoreIndex, ServiceContext, Document
from llama_index.llms.openai import OpenAI
from llama_index.core import SimpleDirectoryReader


openai.api_key = os.getenv("OPENAI_API_KEY")
st.header("Chat with the Streamlit docs💬 📚")

if "messages" not in st.session_state.keys(): # Initialize the chat message history
    st.session_state.messages = [
        {"role": "assistant", "content": "Ask me a question"}
    ]
    
  
@st.cache_resource(show_spinner=False)
def load_data():
    with st.spinner(text="Loading and indexing the Streamlit docs – hang tight! This should take 1-2 minutes."):
        reader = SimpleDirectoryReader(input_files=["Self-Management.pdf"], recursive=True)
        docs = reader.load_data()
        service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-3.5-turbo", temperature=0.5, system_prompt="You are an expert on the Streamlit Python library and your job is to answer technical questions. Assume that all questions are related to the Streamlit Python library. Keep your answers technical and based on facts – do not hallucinate features."))
        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)


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 = 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