import gradio as gr
import spaces
import torch
from threading import Thread

from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer


TITLE = "<h1><center>LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory</center></h1>"

DESCRIPTION = "<h3><center>Visit <a href='' target='_blank'>LLaMA Factory</a> for details.</center></h3>"

CSS = r"""
.duplicate-button {
  margin: auto !important;
  color: white !important;
  background: black !important;
  border-radius: 100vh !important;
}
"""


tokenizer = AutoTokenizer.from_pretrained("shenzhi-wang/Llama3-8B-Chinese-Chat")
model = AutoModelForCausalLM.from_pretrained("shenzhi-wang/Llama3-8B-Chinese-Chat", device_map="auto")


@spaces.GPU(duration=120)
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int):
    conversation = []
    for prompt, answer in history:
        conversation.extend([{"role": "user", "content": prompt}, {"role": "assistant", "content": answer}])

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

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt").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,
        temperature=temperature,
        do_sample=True,
    )
    if temperature == 0:
        generate_kwargs["do_sample"] = False

    t = Thread(target=model.generate, kwargs=generate_kwargs)
    t.start()

    output = ""
    for new_token in streamer:
        outputs += new_token
        yield output


with gr.Blocks(fill_height=True, css=CSS) as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")

    gr.ChatInterface(
        fn=stream_chat,
        fill_height=True,
        additional_inputs_accordion=gr.Accordion(label="⚙️ Parameters", open=False, render=False),
        additional_inputs=[
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.95,
                label="Temperature",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=4096,
                step=1,
                value=512,
                label="Max new tokens",
                render=False,
            ),
        ],
        examples=[
            ['How to setup a human base on Mars? Give short answer.'],
            ['Explain theory of relativity to me like I’m 8 years old.'],
            ['What is 9,000 * 9,000?'],
            ['Write a pun-filled happy birthday message to my friend Alex.'],
            ['Justify why a penguin might make a good king of the jungle.']
        ],
        cache_examples=False,
    )


if __name__ == "__main__":
    demo.launch()