chatbotUI.py

import streamlit as st


import os
os.environ["OPENAI_API_KEY"] = "sk-"
import openai
openai.api_key = "sk-"


from llama_index import VectorStoreIndex
from llama_index.vector_stores import ChromaVectorStore
from llama_index.storage.storage_context import StorageContext
import chromadb

db2 = chromadb.PersistentClient(path="./storage/chroma")
chroma_collection = db2.get_or_create_collection("andrew_sleep_db")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_vector_store(
    vector_store=vector_store,
    storage_context=storage_context,
)

chat_engine = index.as_chat_engine(chat_mode="condense_question")

st.title("Chat with Andrew")

if "messages" not in st.session_state.keys():
    st.session_state.messages = [{"role": "assistant", "content": "How may I help you?"}]

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.write(message["content"])

if prompt := st.chat_input():
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.write(prompt)


def generate_response(prompt_input):
    return chat_engine.chat(prompt_input).response      




if st.session_state.messages[-1]["role"] != "assistant":
    with st.chat_message("assistant"):
        with st.spinner("Thinking ... "):
            response = generate_response(prompt)
            st.write(response)
    message = {"role": "assistant", "content": response}
    st.session_state.messages.append(message)