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import torch
import gradio as gr
import spaces
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import os
from threading import Thread
import random
from datasets import load_dataset

HF_TOKEN = os.environ.get("HF_TOKEN", None)
MODEL_ID = "CohereForAI/c4ai-command-r7b-12-2024"
MODELS = os.environ.get("MODELS")
MODEL_NAME = MODEL_ID.split("/")[-1]

TITLE = "<h1><center>์ƒˆ๋กœ์šด ์ผ๋ณธ์–ด LLM ๋ชจ๋ธ ์›น UI</center></h1>"

DESCRIPTION = f"""
<h3>๋ชจ๋ธ: <a href="https://huggingface.co/CohereForAI/c4ai-command-r7b-12-2024">CohereForAI/c4ai-command-r7b-12-2024</a></h3>
<center>
<p>
<br>
cc-by-nc
</p>
</center>
"""

CSS = """
.duplicate-button {
    margin: auto !important;
    color: white !important;
    background: black !important;
    border-radius: 100vh !important;
}
h3 {
    text-align: center;
}
.chatbox .messages .message.user {
    background-color: #e1f5fe;
}
.chatbox .messages .message.bot {
    background-color: #eeeeee;
}
"""

# ๋ชจ๋ธ๊ณผ ํ† ํฌ๋‚˜์ด์ € ๋กœ๋“œ
model = AutoModelForCausalLM.from_pretrained(
    MODEL_ID,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)

# ๋ฐ์ดํ„ฐ์…‹ ๋กœ๋“œ
dataset = load_dataset("elyza/ELYZA-tasks-100")
print(dataset)

split_name = "train" if "train" in dataset else "test"
examples_list = list(dataset[split_name])
examples = random.sample(examples_list, 50)
example_inputs = [[example['input']] for example in examples]

@spaces.GPU
def stream_chat(message: str, history: list, temperature: float, max_new_tokens: int, top_p: float, top_k: int, penalty: float):
    print(f'message is - {message}')
    print(f'history is - {history}')
    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, tokenize=False, add_generation_prompt=True)
    inputs = tokenizer(input_ids, return_tensors="pt").to(0)
    
    streamer = TextIteratorStreamer(tokenizer, timeout=10., skip_prompt=True, skip_special_tokens=True)

    generate_kwargs = dict(
        inputs, 
        streamer=streamer,
        top_k=top_k,
        top_p=top_p,
        repetition_penalty=penalty,
        max_new_tokens=max_new_tokens, 
        do_sample=True, 
        temperature=temperature,
        eos_token_id=[255001],
    )
    
    thread = Thread(target=model.generate, kwargs=generate_kwargs)
    thread.start()

    buffer = ""
    for new_text in streamer:
        buffer += new_text
        yield buffer

chatbot = gr.Chatbot(height=500)

with gr.Blocks(css=CSS) as demo:
    gr.HTML(TITLE)
    gr.HTML(DESCRIPTION)
    gr.ChatInterface(
        fn=stream_chat,
        chatbot=chatbot,
        fill_height=True,
        theme="soft",
        additional_inputs_accordion=gr.Accordion(label="โš™๏ธ ๋งค๊ฐœ๋ณ€์ˆ˜", open=False, render=False),
        additional_inputs=[
            gr.Slider(
                minimum=0,
                maximum=1,
                step=0.1,
                value=0.8,
                label="์˜จ๋„",
                render=False,
            ),
            gr.Slider(
                minimum=128,
                maximum=1000000,
                step=1,
                value=100000,
                label="์ตœ๋Œ€ ํ† ํฐ ์ˆ˜",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=1.0,
                step=0.1,
                value=0.8,
                label="์ƒ์œ„ ํ™•๋ฅ ",
                render=False,
            ),
            gr.Slider(
                minimum=1,
                maximum=20,
                step=1,
                value=20,
                label="์ƒ์œ„ K",
                render=False,
            ),
            gr.Slider(
                minimum=0.0,
                maximum=2.0,
                step=0.1,
                value=1.0,
                label="๋ฐ˜๋ณต ํŒจ๋„ํ‹ฐ",
                render=False,
            ),
        ],
        examples=[
            ["์•„์ด์˜ ์—ฌ๋ฆ„๋ฐฉํ•™ ๊ณผํ•™ ํ”„๋กœ์ ํŠธ๋ฅผ ์œ„ํ•œ 5๊ฐ€์ง€ ์•„์ด๋””์–ด๋ฅผ ์ฃผ์„ธ์š”."],
            ["๋งˆํฌ๋‹ค์šด์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ธŒ๋ ˆ์ดํฌ์•„์›ƒ ๊ฒŒ์ž„ ๋งŒ๋“ค๊ธฐ ํŠœํ† ๋ฆฌ์–ผ์„ ์ž‘์„ฑํ•ด์ฃผ์„ธ์š”."],
            ["์ดˆ๋Šฅ๋ ฅ์„ ๊ฐ€์ง„ ์ฃผ์ธ๊ณต์˜ SF ์ด์•ผ๊ธฐ ์‹œ๋‚˜๋ฆฌ์˜ค๋ฅผ ์ž‘์„ฑํ•ด์ฃผ์„ธ์š”. ๋ณต์„  ์„ค์ •, ํ…Œ๋งˆ์™€ ๋กœ๊ทธ๋ผ์ธ์„ ๋…ผ๋ฆฌ์ ์œผ๋กœ ์‚ฌ์šฉํ•ด์ฃผ์„ธ์š”"],
            ["์•„์ด์˜ ์—ฌ๋ฆ„๋ฐฉํ•™ ์ž์œ ์—ฐ๊ตฌ๋ฅผ ์œ„ํ•œ 5๊ฐ€์ง€ ์•„์ด๋””์–ด์™€ ๊ทธ ๋ฐฉ๋ฒ•์„ ๊ฐ„๋‹จํžˆ ์•Œ๋ ค์ฃผ์„ธ์š”."],
            ["ํผ์ฆ ๊ฒŒ์ž„ ์Šคํฌ๋ฆฝํŠธ ์ž‘์„ฑ์„ ์œ„ํ•œ ์กฐ์–ธ ๋ถ€ํƒ๋“œ๋ฆฝ๋‹ˆ๋‹ค"],
            ["๋งˆํฌ๋‹ค์šด ํ˜•์‹์œผ๋กœ ๋ธ”๋ก ๊นจ๊ธฐ ๊ฒŒ์ž„ ์ œ์ž‘ ๊ต๊ณผ์„œ๋ฅผ ์ž‘์„ฑํ•ด์ฃผ์„ธ์š”"],
            ["์‹ค๋ฒ„ ๅทๆŸณ๋ฅผ ์ƒ๊ฐํ•ด์ฃผ์„ธ์š”"],
            ["์ผ๋ณธ์–ด ๊ด€์šฉ๊ตฌ, ์†๋‹ด์— ๊ด€ํ•œ ์‹œํ—˜ ๋ฌธ์ œ๋ฅผ ๋งŒ๋“ค์–ด์ฃผ์„ธ์š”"],
            ["๋„๋ผ์—๋ชฝ์˜ ๋“ฑ์žฅ์ธ๋ฌผ์„ ์•Œ๋ ค์ฃผ์„ธ์š”"],
            ["์˜ค์ฝ”๋…ธ๋ฏธ์•ผํ‚ค ๋งŒ๋“œ๋Š” ๋ฐฉ๋ฒ•์„ ์•Œ๋ ค์ฃผ์„ธ์š”"],
            ["๋ฌธ์ œ 9.11๊ณผ 9.9 ์ค‘ ์–ด๋Š ๊ฒƒ์ด ๋” ํฐ๊ฐ€์š”? step by step์œผ๋กœ ๋…ผ๋ฆฌ์ ์œผ๋กœ ์ƒ๊ฐํ•ด์ฃผ์„ธ์š”."],
        ],
        cache_examples=False,
    )

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