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')