import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.schema import SystemMessage, HumanMessage, AIMessage
import instruct

# From here down is all the StreamLit UI.
st.set_page_config(page_title="Entz's LLM LangChain-OpenAI", page_icon=":robot_face:")
st.markdown("<h1 style='text-align: center; color: navy;'>My Kidbot</h1>", unsafe_allow_html=True)
st.markdown("<h4 style='text-align: center;'>Chat with my 5-Year-Old droid</h4>", unsafe_allow_html=True)
st.markdown("<p style='text-align: right'>By <a href='https://entzyeung.github.io/portfolio/index.html'>Lorentz Yeung</a></p>", unsafe_allow_html=True)

# put a presumptions for ai to the streamlit session state
# st.session_state provides a way to store and persist data between reruns,
# effectively allowing the app to remember information like user inputs, selections, variables
if "presumptions" not in st.session_state:
     st.session_state.presumptions = [
        SystemMessage(content=instruct.instruct)
    ]

def load_answer(question):

    st.session_state.presumptions.append(HumanMessage(content=question))
    assistant_answer  = chat(st.session_state.presumptions )
    # store the new answer the presumption list
    st.session_state.presumptions.append(AIMessage(content=assistant_answer.content))
    return assistant_answer.content


def get_text():
    input_text = st.text_input("Ask me question please~ ", "How old are you little one?", key= input)
    return input_text

chat = ChatOpenAI(temperature=0)

user_input=get_text()
submit = st.button('Little girl answers: ')  

if submit:
    
    response = load_answer(user_input)
    st.subheader("Answer:")

    st.write(response,key= 1)