import os

os.environ["HF_HOME"] = "/scratch/sroydip1/cache/hf/"
os.environ["HUGGINGFACEHUB_API_TOKEN"] = ""
# import torch
import pickle
import torch
import streamlit as st
from transformers import Conversation, pipeline
from upload import get_file, upload_file
from utils import clear_uploader, undo, restart

from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_community.llms import HuggingFaceHub


share_keys = ["messages", "model_name"]
MODELS = [
    "mistralai/Mistral-7B-Instruct-v0.2",
    "google/flan-t5-small",
    "google/flan-t5-base",
    "google/flan-t5-large",
    "google/flan-t5-xl",
    "google/flan-t5-xxl",
]
default_model = "mistralai/Mistral-7B-Instruct-v0.2"
# default_model = "meta-llama/Llama-2-7b-chat-hf"

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

if "model_name" not in st.session_state:
    st.session_state.model_name = default_model

template = """You are a friendly chatbot engaging in a conversation with a human.

Previous conversation:
{chat_history}

New human question: {question}
Response:"""


def get_pipeline(model_name):
    llm = HuggingFaceHub(
        repo_id=model_name,
        task="text-generation",
        model_kwargs={
            "max_new_tokens": 512,
            "top_k": 30,
            "temperature": 0.1,
            "repetition_penalty": 1.03,
        },
    )
    return llm


chatbot = get_pipeline(st.session_state.model_name)
memory = ConversationBufferMemory(memory_key="chat_history")
prompt_template = PromptTemplate.from_template(template)
conversation = LLMChain(llm=chatbot, prompt=prompt_template, verbose=True, memory=memory)


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"):
            print(conversation)
            with st.spinner("Generating response..."):
                response = conversation({"question": prompt})
                print(response)
                response = response["text"]
                st.write(response)

        push_message("assistant", response)
    clear_uploader()