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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')
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