import os
import pickle
from json import dumps, loads
from typing import Any, List, Mapping, Optional

import numpy as np
import openai
import pandas as pd
import streamlit as st
from dotenv import load_dotenv
from huggingface_hub import HfFileSystem
from langchain.llms.base import LLM
from llama_index import (
    Document,
    GPTVectorStoreIndex,
    LLMPredictor,
    PromptHelper,
    ServiceContext,
    SimpleDirectoryReader,
    StorageContext,
    load_index_from_storage,
)
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# from utils.customLLM import CustomLLM

load_dotenv()
# openai.api_key = os.getenv("OPENAI_API_KEY")
fs = HfFileSystem()

# define prompt helper
# set maximum input size
CONTEXT_WINDOW = 2048
# set number of output tokens
NUM_OUTPUT = 525
# set maximum chunk overlap
CHUNK_OVERLAP_RATION = 0.2


@st.cache_resource
def load_model(model_name: str):
    # llm_model_name = "bigscience/bloom-560m"
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    model = AutoModelForCausalLM.from_pretrained(model_name, config="T5Config")

    pipe = pipeline(
        task="text-generation",
        model=model,
        tokenizer=tokenizer,
        # device=0, # GPU device number
        # max_length=512,
        do_sample=True,
        top_p=0.95,
        top_k=50,
        temperature=0.7,
    )

    return pipe


class CustomLLM(LLM):
    llm_model_name: str
    pipeline: Any

    def __init__(self, llm_model_name: str):
        super().__init__()
        
        self.llm_model_name = llm_model_name
        self.pipeline = load_model(mode_name=llm_model_name)

    def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
        prompt_length = len(prompt)
        response = self.pipeline(prompt, max_new_tokens=525)[0]["generated_text"]

        # only return newly generated tokens
        return response[prompt_length:]

    @property
    def _identifying_params(self) -> Mapping[str, Any]:
        return {"name_of_model": self.llm_model_name}

    @property
    def _llm_type(self) -> str:
        return "custom"


class LlamaCustom:
    def __init__(self, model_name: str) -> None:
        self.vector_index = self.initialize_index(model_name=model_name)

    @st.cache_resource
    def initialize_index(_self, model_name: str):
        index_name = model_name.split("/")[-1]

        file_path = f"./vectorStores/{index_name}"
        if os.path.exists(path=file_path):
            # rebuild storage context
            storage_context = StorageContext.from_defaults(persist_dir=file_path)

            # local load index access
            index = load_index_from_storage(storage_context)

            # huggingface repo load access
            # with fs.open(file_path, "r") as file:
            #     index = pickle.loads(file.readlines())
            return index
        else:
            # define llm
            prompt_helper = PromptHelper(
                context_window=CONTEXT_WINDOW,
                num_output=NUM_OUTPUT,
                chunk_overlap_ratio=CHUNK_OVERLAP_RATION,
            )

            llm_predictor = LLMPredictor(llm=CustomLLM(llm_model_name=model_name))
            service_context = ServiceContext.from_defaults(
                llm_predictor=llm_predictor, prompt_helper=prompt_helper
            )

            # documents = prepare_data(r"./assets/regItems.json")
            documents = SimpleDirectoryReader(input_dir="./assets/pdf").load_data()

            index = GPTVectorStoreIndex.from_documents(
                documents, service_context=service_context
            )

            # local write access
            index.storage_context.persist(file_path)

            # huggingface repo write access
            # with fs.open(file_path, "w") as file:
            #     file.write(pickle.dumps(index))
            return index

    def get_response(self, query_str):
        print("query_str: ", query_str)
        query_engine = self.vector_index.as_query_engine()
        response = query_engine.query(query_str)
        return str(response)