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import streamlit as st
from streamlit_chat import message
from langchain.llms import OpenAI
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import (
ConversationBufferMemory,
ConversationSummaryMemory,
ConversationBufferWindowMemory
)
if 'conversation' not in st.session_state:
st.session_state['conversation'] = None
if 'message' not in st.session_state:
st.session_state['message'] = []
if 'API_Key' not in st.session_state:
st.session_state['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;'>I'm ur assistance, how can I help you </h1>",
unsafe_allow_html=True)
st.sidebar.title("😎")
st.session_state['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 ❤ \n\n")#+st.session_state['conversation'].memory.buffer)
# summarise_placeholder.write("Nice chatting with you ❤ \n\n" + st.session_state['conversation'].memory.buffer)
def getresponse(userInput, api_key):
if st.session_state['conversation'] is None:
llm = OpenAI(temperature=0, openai_api_key=api_key, model_name='gpt-3.5-turbo-instruct')
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['message'].append(user_input)
model_response=getresponse(user_input, st.session_state['API_Key'])
st.session_state['message'].append(model_response)
with response_container:
for i in range(len(st.session_state['message'])):
if(i%2)==0:
message(st.session_state['message'][i], is_user=True, key=str(i)+'_user')
else:
message(st.session_state['message'][i], key=str(i)+'_AI') |