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| import streamlit as st | |
| from streamlit_chat import message | |
| # from langchain.llms import OpenAI #This import has been replaced by the below import please | |
| #from langchain_openai import OpenAI | |
| from langchain_community.llms import HuggingFaceEndpoint | |
| 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 'messages' not in st.session_state: | |
| st.session_state['messages'] =[] | |
| 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;'>How can I assist 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 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) | |
| #import os | |
| #os.environ["OPENAI_API_KEY"] = "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" | |
| def getresponse(userInput, api_key): | |
| if st.session_state['conversation'] is None: | |
| llm = HuggingFaceEndpoint( | |
| temperature=0.7, | |
| HUGGINGFACEHUB_API_TOKEN=api_key, | |
| repo_id="mistralai/Mistral-7B-Instruct-v0.2" # 'text-davinci-003' model is depreciated now, so we are using the openai's recommended model | |
| ) | |
| 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['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') | |