import os

import streamlit as st
import streamlit.components.v1 as components
from css import load_css
from langchain import OpenAI
from langchain.callbacks import get_openai_callback
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.conversation.memory import ConversationBufferMemory
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores.pgvector import PGVector
from message import Message

CONNECTION_STRING = "postgresql+psycopg2://localhost/sorbobot"
COLLECTION_NAME = ""


def initialize_session_state():
    if "history" not in st.session_state:
        st.session_state.history = []
    if "token_count" not in st.session_state:
        st.session_state.token_count = 0
    if "conversation" not in st.session_state:
        embeddings = OpenAIEmbeddings()

        store = PGVector(
            collection_name=COLLECTION_NAME,
            connection_string=CONNECTION_STRING,
            embedding_function=embeddings,
        )

        retriever = store.as_retriever()

        llm = OpenAI(
            temperature=0,
            openai_api_key=os.environ["OPENAI_API_KEY"],
            model="text-davinci-003",
        )

        st.session_state.memory = ConversationBufferMemory()
        st.session_state.conversation = ConversationalRetrievalChain.from_llm(
            llm=llm, retriever=retriever
        )


def on_click_callback():
    with get_openai_callback() as cb:
        human_prompt = st.session_state.human_prompt
        llm_response = st.session_state.conversation.run(
            {
                "question": human_prompt,
                "chat_history": st.session_state.memory.buffer,
            }
        )
        st.session_state.history.append(Message("human", human_prompt))
        st.session_state.history.append(Message("ai", llm_response))
        st.session_state.token_count += cb.total_tokens


load_css()
initialize_session_state()

st.title("Sorbobot - Le futur de la recherche scientifique interactive")

chat_placeholder = st.container()
prompt_placeholder = st.form("chat-form")
information_placeholder = st.empty()

with chat_placeholder:
    for chat in st.session_state.history:
        div = f"""
<div class="chat-row 
    {'' if chat.origin == 'ai' else 'row-reverse'}">
    <img class="chat-icon" src="./app/static/{
        'ai_icon.png' if chat.origin == 'ai' 
                      else 'user_icon.png'}"
         width=32 height=32>
    <div class="chat-bubble
    {'ai-bubble' if chat.origin == 'ai' else 'human-bubble'}">
        &#8203;{chat.message}
    </div>
</div>
        """
        st.markdown(div, unsafe_allow_html=True)

    for _ in range(3):
        st.markdown("")

with prompt_placeholder:
    st.markdown("**Chat**")
    cols = st.columns((6, 1))
    cols[0].text_input(
        "Chat",
        value="Hello bot",
        label_visibility="collapsed",
        key="human_prompt",
    )
    cols[1].form_submit_button(
        "Submit",
        type="primary",
        on_click=on_click_callback,
    )

information_placeholder.caption(
    f"""
Used {st.session_state.token_count} tokens \n
Debug Langchain conversation: 
{st.session_state.memory.buffer}
"""
)

components.html(
    """
<script>
const streamlitDoc = window.parent.document;

const buttons = Array.from(
    streamlitDoc.querySelectorAll('.stButton > button')
);
const submitButton = buttons.find(
    el => el.innerText === 'Submit'
);

streamlitDoc.addEventListener('keydown', function(e) {
    switch (e.key) {
        case 'Enter':
            submitButton.click();
            break;
    }
});
</script>
""",
    height=0,
    width=0,
)