import os
from threading import Thread
from typing import Iterator
import json
from datetime import datetime
from pathlib import Path
from uuid import uuid4

import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer, AutoModel
from pathlib import Path
from pinecone.grpc import PineconeGRPC as Pinecone
import torch
from huggingface_hub import CommitScheduler

HF_UPLOAD = os.environ.get("HF_UPLOAD")

JSON_DATASET_DIR = Path("json_dataset")
JSON_DATASET_DIR.mkdir(parents=True, exist_ok=True)

JSON_DATASET_PATH = JSON_DATASET_DIR / f"train-{uuid4()}.json"

scheduler = CommitScheduler(
    repo_id="psyche/llama3-mrc-chat-log",
    repo_type="dataset",
    folder_path=JSON_DATASET_DIR,
    path_in_repo="data",
    token=HF_UPLOAD
)

pc = Pinecone(api_key=os.environ.get("PINECONE"))
index = pc.Index("commonsense")
"""
device = torch.device("cuda" if torch.cuda.is_available() else "CPU")
retriever_tokenizer = AutoTokenizer.from_pretrained("psyche/dpr-longformer-ko-4096")
retriever = AutoModel.from_pretrained("psyche/dpr-longformer-ko-4096")
retriever.eval()
retriever.to(device)
"""
def save_json(question: str, answer: str) -> None:
    with scheduler.lock:
        with JSON_DATASET_PATH.open("a") as f:
            json.dump({"question": question, "answer": answer, "datetime": datetime.now().isoformat(), "label":""}, f, ensure_ascii=False)
            f.write("\n")


MAX_MAX_NEW_TOKENS = 8192
DEFAULT_MAX_NEW_TOKENS = 4096
MAX_INPUT_TOKEN_LENGTH = 2048

DESCRIPTION = """\
# Llama-3 8B Korean QA Chatbot \
"""

if not torch.cuda.is_available():
    DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>"


if torch.cuda.is_available():
    model_id = "psyche/llama3-8b-instruct-ko"
    #model_id = "psyche/meta-llama3-experiments"
    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", load_in_4bit=True, revision="v4.3")
    tokenizer = AutoTokenizer.from_pretrained(model_id, revision="v4.3")



@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    system_prompt: str,
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:
    conversation = []
    if system_prompt:
        conversation.append({"role": "system", "content": system_prompt})
    for user, assistant in chat_history:
        conversation.extend([{"role": "user", "content": user}, {"role": "assistant", "content": assistant}])

    """    
    retriever_inputs = retriever_tokenizer([message], max_length=1024, truncation=True, return_tensors="pt")
    retriever_inputs = {k:v.to(retriever.device) for k,v in retriever_inputs.items()}
    with torch.no_grad():
        embeddings = retriever(**retriever_inputs).pooler_output
        embeddings = embeddings.cpu().numpy()

    results = index.query(
        vector=embeddings[0],
        top_k=1,
        include_values=False,
        include_metadata=True
    )

    results = [result for result in results["matches"] if result["score"] > 0.6]
    if len(results) > 0:
        message = results[0]["metadata"]["text"] + f"\n\n위 문맥을 참고하여 질문 '{message}'에 답하면?"
    """
    conversation.append({"role": "user", "content": message })
    
    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt", add_generation_prompt=True)
    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    input_ids = input_ids.to(model.device)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        {"input_ids": input_ids},
        streamer=streamer,
        max_new_tokens=max_new_tokens,
        do_sample=True,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )
    
    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()
    
    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)
        
   
    save_json(message, "".join(outputs))
    

    
chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs=[
        gr.Textbox(label="System prompt", lines=6),
        gr.Slider(
            label="Max new tokens",
            minimum=1,
            maximum=MAX_MAX_NEW_TOKENS,
            step=1,
            value=DEFAULT_MAX_NEW_TOKENS,
        ),
        gr.Slider(
            label="Temperature",
            minimum=0.01,
            maximum=4.0,
            step=0.1,
            value=0.01,
        ),
        gr.Slider(
            label="Top-p (nucleus sampling)",
            minimum=0.05,
            maximum=1.0,
            step=0.05,
            value=0.9,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=50,
        ),
        gr.Slider(
            label="Repetition penalty",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.15,
        ),
    ],
    stop_btn=None,
    examples=[
        ["안녕?"],
        ["너가 할 수 있는게 뭐야?"],
        ["파이썬에 대해서 알려줘"],
        ["대한민국의 수도는?"],
        ["독도는 어느나라 땅이야?"],
    ],
)

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    chat_interface.render()

if __name__ == "__main__":
    demo.queue(max_size=20).launch()