app.py

import os
from threading import Thread
from typing import Iterator

import gradio as gr
import spaces
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
import time

MAX_MAX_NEW_TOKENS = 2048
DEFAULT_MAX_NEW_TOKENS = 1024
MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))


DESCRIPTION = """\
# Dorna-Llama3-8B-Instruct Chat
"""

PLACEHOLDER = """
<div style="padding: 30px; text-align: center; display: flex; flex-direction: column; align-items: center;">
   <img src="https://avatars.githubusercontent.com/u/39557177?v=4" style="width: 80%; max-width: 550px; height: auto; opacity: 0.80;  "> 
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Dorna-Llama3-8B-Instruct</h1>
</div>
"""

custom_css = """
@import url('https://fonts.googleapis.com/css2?family=Vazirmatn&display=swap');

body, .gradio-container, .gr-button, .gr-input, .gr-slider, .gr-dropdown, .gr-markdown {
    font-family: 'Vazirmatn', sans-serif !important;
}

._button {
    font-size: 20px;
}

pre, code {
    direction: ltr !important;
    unicode-bidi: plaintext !important;
}
"""


system_prompt = str(os.getenv("SYSTEM_PROMPT"))


def execution_time_calculator(start_time, log=True):
    delta = time.time() - start_time
    if log:
        print("--- %s seconds ---" % (delta))
    return delta

def token_per_second_calculator(tokens_count, time_delta):
    return tokens_count/time_delta

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 = "PartAI/Dorna-Llama3-8B-Instruct"
 #   model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
 #   model_id = "amirMohammadi/Dorna-Llama3-8B-Instruct-Quantized4Bit"
    model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", torch_dtype=torch.bfloat16)
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    
generation_speed = 0

def get_generation_speed():
    global generation_speed

    return generation_speed


@spaces.GPU
def generate(
    message: str,
    chat_history: list[tuple[str, str]],
    max_new_tokens: int = 1024,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
    do_sample: bool =True,
) -> Iterator[str]:
    global generation_speed
    global system_prompt

    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})

    input_ids = tokenizer.apply_chat_template(conversation, return_tensors="pt")
    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=do_sample,
        top_p=top_p,
        top_k=top_k,
        temperature=temperature,
        num_beams=1,
        repetition_penalty=repetition_penalty,
    )

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

    outputs = []
    sum_tokens = 0
    for text in streamer:
        num_tokens = len(tokenizer.tokenize(text))
        sum_tokens += num_tokens
        
        outputs.append(text)
        yield "".join(outputs)

    time_delta = execution_time_calculator(start_time, log=False)

    generation_speed = token_per_second_calculator(sum_tokens, time_delta)

    print(f"generation_speed: {generation_speed}")


chatbot = gr.Chatbot(placeholder=PLACEHOLDER, scale=1, show_copy_button=True, height="68%", rtl=True) #,  elem_classes=["chatbot"])
chat_input = gr.Textbox(show_label=False, lines=2, rtl=True, placeholder="ورودی", show_copy_button=True, scale=4)
submit_btn = gr.Button(variant="primary", value="ارسال", size="sm", scale=1, elem_classes=["_button"])


chat_interface = gr.ChatInterface(
    fn=generate,
    additional_inputs_accordion=gr.Accordion(label="ورودی‌های اضافی", open=False),
    additional_inputs=[
        gr.Slider(
            label="حداکثر تعداد توکن ها",
            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=0.01,
            value=0.5,
        ),
        gr.Slider(
            label="Top-p",
            minimum=0.05,
            maximum=1.0,
            step=0.01,
            value=0.9,
        ),
        gr.Slider(
            label="Top-k",
            minimum=1,
            maximum=1000,
            step=1,
            value=20,
        ),
        gr.Slider(
            label="جریمه تکرار",
            minimum=1.0,
            maximum=2.0,
            step=0.05,
            value=1.2,
        ),
        gr.Dropdown(
            label="نمونه‌گیری",
            choices=[False, True],
            value=True)
    ],
    stop_btn="توقف",
    chatbot=chatbot,
    textbox=chat_input,
    submit_btn=submit_btn,
    retry_btn="🔄 تلاش مجدد",
    undo_btn="↩️ بازگشت",
    clear_btn="🗑️ پاک کردن",
    title="درنا، محصول مرکز تحقیقات هوش مصنوعی پارت"
)

 
with gr.Blocks(css=custom_css, fill_height=False) as demo:
    gr.Markdown(DESCRIPTION)
    chat_interface.render()


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