import os
import uuid

import gradio as gr

import torch
from transformers import AutoTokenizer
from vllm import AsyncLLMEngine, AsyncEngineArgs, SamplingParams

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
MODEL_ID = "neuralmagic/OpenHermes-2.5-Mistral-7B-pruned50"

DESCRIPTION = f"""\
# NM vLLM Chat
Model: {MODEL_ID}
"""

if not torch.cuda.is_available():
    raise ValueError("Running on CPU 🥶 This demo does not work on CPU.")

engine_args = AsyncEngineArgs(
    model=MODEL_ID, 
    sparsity="sparse_w16a16", 
    max_model_len=MAX_INPUT_TOKEN_LENGTH
)
engine = AsyncLLMEngine.from_engine_args(engine_args)

tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
tokenizer.use_default_system_prompt = False


async 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,
):
    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},
            ]
        )
    conversation.append({"role": "user", "content": message})

    formatted_conversation = tokenizer.apply_chat_template(
        conversation, tokenize=False, add_generation_prompt=True
    )

    sampling_params = SamplingParams(
        max_tokens=max_new_tokens,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        repetition_penalty=repetition_penalty,
    )

    stream = await engine.add_request(
        uuid.uuid4().hex, formatted_conversation, sampling_params
    )

    async for request_output in stream:
        text = request_output.outputs[0].text
        yield text


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=0.1,
            maximum=4.0,
            step=0.1,
            value=0.6,
        ),
        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=1.2,
        ),
    ],
    stop_btn=None,
    examples=[
        ["Hello there! How are you doing?"],
        ["Can you explain briefly to me what is the Python programming language?"],
        ["Explain the plot of Cinderella in a sentence."],
        ["How many hours does it take a man to eat a Helicopter?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
)

# with gr.Blocks(css="style.css") as demo:
with gr.Blocks() 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()