import streamlit as st from streamlit_chat import message from langchain_openai import OpenAI from langchain.chains import ConversationChain from langchain.chains.conversation.memory import (ConversationSummaryMemory) if 'conversation' not in st.session_state: st.session_state['conversation'] =None if 'messages' not in st.session_state: st.session_state['messages'] =[] # Setting page title and header st.set_page_config(page_title="Chat GPT Clone", page_icon=":robot_face:") st.markdown("

How can I assist you?

", unsafe_allow_html=True) st.sidebar.title("😎") 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) def getresponse(userInput): if st.session_state['conversation'] is None: llm = OpenAI( temperature=0, model_name='gpt-3.5-turbo-instruct' #we can also use 'gpt-3.5-turbo' ) st.session_state['conversation'] = ConversationChain( llm=llm, verbose=True, memory=ConversationSummaryMemory(llm=llm) ) 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: st.session_state['messages'].append(user_input) model_response=getresponse(user_input) 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')