app.py

import os
from threading import Thread
from typing import Iterator

import gradio as gr
from langfuse import Langfuse
from langfuse.decorators import observe
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;">
   <h1 style="font-size: 28px; margin-bottom: 2px; opacity: 0.55;">Test 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"))

secret_key = str(os.getenv("LANGFUSE_SECRET_KEY"))
public_key = str(os.getenv("LANGFUSE_PUBLIC_KEY"))
host = str(os.getenv("LANGFUSE_HOST"))

langfuse = Langfuse(
  secret_key=secret_key,
  public_key=public_key,
  host=host
)


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 = 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

@observe()
def log_to_langfuse(message, chat_history, max_new_tokens, temperature, top_p, top_k, repetition_penalty, do_sample, generation_speed, model_outputs):
    print(f"generation_speed: {generation_speed}")
    return  "".join(model_outputs) 


@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)

    log_function = log_to_langfuse(message, chat_history, max_new_tokens, temperature, top_p, top_k, repetition_penalty, do_sample, generation_speed, outputs)





chatbot = gr.Chatbot(placeholder=PLACEHOLDER, scale=1, show_copy_button=True, height="5%", 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="تست llama3"
)


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()