# import torch
import pickle
import streamlit as st
from transformers import Conversation, pipeline
from upload import get_file, upload_file
from utils import clear_uploader, undo, restart


share_keys = ["messages", "model_name"]
MODELS = ["facebook/blenderbot-400M-distill", "facebook/blenderbot-90M"]

st.set_page_config(
    page_title="LLM",
    page_icon="📚",
)

if "model_name" not in st.session_state:
    st.session_state.model_name = "facebook/blenderbot-400M-distill"
    

def get_pipeline(model_name):
    # device = 0 if torch.cuda.is_available() else -1
    device = -1
    chatbot = pipeline(model=model_name, task="conversational", device=device)
    return chatbot

chatbot = get_pipeline(st.session_state.model_name)

if "messages" not in st.session_state:
    st.session_state.messages = []

if len(st.session_state.messages) == 0 and "id" in st.query_params:
    with st.spinner("Loading chat..."):
        id = st.query_params["id"]
        data = get_file(id)
        obj = pickle.loads(data)
        for k, v in obj.items():
            st.session_state[k] = v


def share():
    obj = {}
    for k in share_keys:
        if k in st.session_state:
            obj[k] = st.session_state[k]
    data = pickle.dumps(obj)
    id = upload_file(data)
    url = f"https://umbc-nlp-chat-llm.hf.space/?id={id}"
    st.markdown(f"[share](/?id={id})")
    st.success(f"Share URL: {url}")

with st.sidebar:
    st.title(":blue[LLM Only]")

    st.subheader("Model")
    model_name = st.selectbox("Model", MODELS, index=MODELS.index(st.session_state.model_name))

    if st.button("Share", use_container_width=True):
        share()

    cols = st.columns(2)
    with cols[0]:
        if st.button("Restart", type="primary", use_container_width=True):
            restart()
    
    with cols[1]:
        if st.button("Undo", use_container_width=True):
            undo()

    append = st.checkbox("Append to previous message", value=False)


for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])


def push_message(role, content):
    message = {"role": role, "content": content}
    st.session_state.messages.append(message)
    return message

if prompt := st.chat_input("Type a message", key="chat_input"):
    push_message("user", prompt)
    with st.chat_message("user"):
        st.markdown(prompt)

    if not append:
        with st.chat_message("assistant"):
            conversation = Conversation()
            for m in st.session_state.messages:
                conversation.add_message(m)
            print(conversation)
            with st.spinner("Generating response..."):
                response = chatbot(conversation)
                response = response[-1]["content"]
                st.write(response)

        push_message("assistant", response)
    clear_uploader()