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import os
import streamlit as st
import random
import time
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.document_loaders import DataFrameLoader
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
# Get OpenAI setup
openai_api_key = os.getenv("openapi_token")
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key)
# Setup vector database
persist_directory = './chroma_db'
vectordb = Chroma(persist_directory=persist_directory, embedding_function=embedding)
# Streamed response emulator
def response_generator():
response = random.choice(
[
"Hello there! How can I assist you today?",
"Hi, human! Is there anything I can help you with?",
"Do you need help?",
]
)
for word in response.split():
yield word + " "
time.sleep(0.05)
st.title("Simple chat")
# 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"])
# Accept user input
if prompt := st.chat_input("What is up?"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
response = st.write_stream(response_generator())
# Add assistant response to chat history
st.session_state.messages.append({"role": "assistant", "content": response})
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