import streamlit as st

from langchain.schema import AIMessage, HumanMessage, SystemMessage

# Set up Streamlit page
st.set_page_config(page_title="LangChain Demo", page_icon=":robot:")
st.header("DIBYAJYOTI'S PERSONAL GPT ASSISTANT")

# Check for session state and initialize session if not set
if "sessionMessages" not in st.session_state:
    st.session_state.sessionMessages = [
        SystemMessage(content="You are a helpful assistant.")
    ]

# Use HuggingFace GPT-2 model (Free and Open-Source)
chat = HuggingFaceLLM(model="gpt2")

# Function to get user input
def get_text():
    input_text = st.text_input("You: ")
    return input_text

# Function to process the chat and load an answer
def load_answer(question):
    st.session_state.sessionMessages.append(HumanMessage(content=question))
    
    # Get the response from the assistant (HuggingFace GPT-2 model)
    assistant_answer = chat.invoke(st.session_state.sessionMessages)

    # Add the assistant's response to the session state
    st.session_state.sessionMessages.append(AIMessage(content=assistant_answer.content))

    return assistant_answer.content

# Streamlit UI
user_input = get_text()
submit = st.button('CLICK HERE TO GET YOUR RESPONSE')

if submit:
    if user_input:
        response = load_answer(user_input)
        st.subheader("Answer:")
        st.write(response)
    else:
        st.warning("Please enter a question.")