app.py

import streamlit as st
from streamlit_chat import message
from langchain_openai import ChatOpenAI
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import (ConversationBufferMemory, 
                                                  ConversationSummaryMemory, 
                                                  ConversationBufferWindowMemory
               
                                                  )


# 3 variables in session_state
if 'conversation' not in st.session_state:
    st.session_state['conversation'] =None
if 'messages' not in st.session_state:
    st.session_state['messages'] =[]
if 'API_Key' not in st.session_state:
    st.session_state['OPENAI_API_KEY'] =''

# Setting page title and header
st.set_page_config(page_title="Chat GPT Clone", page_icon=":robot_face:")
st.markdown("<h1 style='text-align: center;'>How can I assist you? </h1>", unsafe_allow_html=True)


st.sidebar.title("😎")
st.session_state['OPENAI_API_KEY']= st.sidebar.text_input("What's your API key?",type="password")
summarise_button = st.sidebar.button("Summarise the conversation", key="summarise")
if summarise_button:
    summarise_placeholder = st.sidebar.write("Nice chatting with you my friend ❤️:\n\n"+st.session_state['conversation'].memory.buffer)
    #summarise_placeholder.write("Nice chatting with you my friend ❤️:\n\n"+st.session_state['conversation'].memory.buffer)


def getresponse(userInput, api_key):

    if st.session_state['conversation'] is None:

        # create a LLM
        llm = ChatOpenAI(
            temperature=0,
            openai_api_key=api_key,
            model_name='gpt-3.5-turbo'
        )

        # store the placeholder as "conversation" in session_state
        st.session_state['conversation'] = ConversationChain(
            llm=llm,
            verbose=True,
            memory=ConversationSummaryMemory(llm=llm)
        )

    # call st.session_state['conversation'] to create dialogues
    response=st.session_state['conversation'].predict(input=userInput)
    print(st.session_state['conversation'].memory.buffer)

    return response

response_container = st.container()
# Here we will have a container for user input text box
container = st.container()

with container:
    with st.form(key='my_form', clear_on_submit=True):
        user_input = st.text_area("Your question goes here:", key='input', height=100)
        submit_button = st.form_submit_button(label='Send')

        if submit_button:
            # store the user input text into st.session_state['messages']
            st.session_state['messages'].append(user_input)
            # getresponse is the function we just created, a wrapper of predict()
            # model_response = getresponse(user_input,st.session_state['API_Key'])
            model_response = getresponse(user_input,st.session_state['OPENAI_API_KEY'])
            st.session_state['messages'].append(model_response)

            with response_container:
                for i in range(len(st.session_state['messages'])):
                        if (i % 2) == 0:
                            message(st.session_state['messages'][i], is_user=True, key=str(i) + '_user')
                        else:
                            message(st.session_state['messages'][i], key=str(i) + '_AI')