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| import streamlit as st | |
| from streamlit_chat import message | |
| from langchain import HuggingFaceHub, ConversationChain | |
| from langchain.chains.conversation.memory import ConversationBufferMemory, ConversationSummaryMemory | |
| from langchain.memory import ConversationBufferWindowMemory | |
| import os | |
| # Get API key from environment variable | |
| hf_api_key = os.environ.get("HUGGINGFACEHUB_API_TOKEN") | |
| # Initialize session state variables | |
| 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'] = '' # No need for API key input in this case | |
| # 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) | |
| # Sidebar (no API key input needed) | |
| st.sidebar.title("Options") | |
| # Summarization Button | |
| summarise_button = st.sidebar.button("Summarise the conversation", key="summarise") | |
| if summarise_button: | |
| with st.spinner("Summarizing..."): # Add a spinner for visual feedback | |
| # Access the buffer for ConversationBufferMemory | |
| summary = st.session_state['conversation'].memory.buffer | |
| st.sidebar.write("Conversation Summary:\n\n" + summary) | |
| # Defining the get_response function | |
| def get_response(user_input, api_key): | |
| if st.session_state['conversation'] is None: | |
| llm = HuggingFaceHub( | |
| repo_id="google/gemini-pro-flash", # Use Gemini Pro Flash | |
| model_kwargs={"temperature": 0.1, "max_new_tokens": 512} | |
| ) | |
| # Use ConversationBufferMemory for summarization | |
| st.session_state['conversation'] = ConversationChain( | |
| llm=llm, | |
| verbose=True, | |
| memory=ConversationBufferMemory() | |
| ) | |
| response = st.session_state['conversation'].predict(input=user_input) | |
| return response | |
| # Chat UI | |
| response_container = st.container() | |
| 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 and user_input: # Check if user_input is not empty | |
| st.session_state['messages'].append(user_input) | |
| with st.spinner("Thinking..."): # Add a spinner | |
| model_response = get_response(user_input, hf_api_key) | |
| st.session_state['messages'].append(model_response) | |
| with response_container: | |
| if st.session_state['messages']: # Check if there are messages to display | |
| 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') |