app.py

import os
import streamlit as st
import pickle
import time
from langchain_openai import OpenAI
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import UnstructuredURLLoader
from langchain_openai import OpenAIEmbeddings


from openai import OpenAI

from dotenv import load_dotenv
load_dotenv()



st.title("ChatGPT-like clone")

# Set OpenAI API key from Streamlit secrets
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Set a default model
if "openai_model" not in st.session_state:
    st.session_state["openai_model"] = "gpt-3.5-turbo"  #  we've added a default model to st.session_state

# 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"):
        stream = client.chat.completions.create(
            model=st.session_state["openai_model"],
            messages=[
                {"role": m["role"], "content": m["content"]}
                for m in st.session_state.messages
            ],
            stream=True,
        )
        response = st.write_stream(stream)  
    st.session_state.messages.append({"role": "assistant", "content": response})