app.py

import os
from threading import Thread
from typing import Iterator

import gradio as gr
import spaces
import torch
from transformers import pipeline, AutoTokenizer

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

DESCRIPTION = """\
# ZhongJing 2 1.8B Merge
This Space demonstrates model [CMLL/ZhongJing-2-1_8b-merge](https://huggingface.co/CMLL/ZhongJing-2-1_8b-merge) for text generation. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).
"""

LICENSE = """
<p/>
---
As a derivative work of [CMLL/ZhongJing-2-1_8b-merge](https://huggingface.co/CMLL/ZhongJing-2-1_8b-merge),
this demo is governed by the original [license](https://huggingface.co/CMLL/ZhongJing-2-1_8b-merge/LICENSE).
"""

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 = "CMLL/ZhongJing-2-1_8b-merge"
    pipe = pipeline("text-generation", model=model_id)
    tokenizer = AutoTokenizer.from_pretrained(model_id)
    tokenizer.use_default_system_prompt = False

@spaces.GPU
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,
) -> Iterator[str]:
    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_text = "\n".join([f"{entry['role']}: {entry['content']}" for entry in conversation])
    inputs = tokenizer(input_text, return_tensors="pt")
    if inputs.input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:
        inputs = {k: v[:, -MAX_INPUT_TOKEN_LENGTH:] for k, v in inputs.items()}
        gr.Warning(f"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.")
    inputs = inputs.to(pipe.device)

    generate_kwargs = {
        "max_new_tokens": max_new_tokens,
        "do_sample": True,
        "top_p": top_p,
        "top_k": top_k,
        "temperature": temperature,
        "repetition_penalty": repetition_penalty,
    }

    def run_generation():
        return pipe(inputs.input_ids, **generate_kwargs)

    t = Thread(target=run_generation)
    t.start()

    outputs = []
    for text in run_generation():
        outputs.append(text['generated_text'])
        yield "".join(outputs)

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=[
        ["请问气虚体质有哪些症状表现?"],
        ["简单介绍一下中医的五行学说。"],
        ["桑螵蛸是什么?有什么功效作用?"],
        ["Write a 100-word article on 'Benefits of Open-Source in AI research'"],
    ],
)

with gr.Blocks(css="style.css") as demo:
    gr.Markdown(DESCRIPTION)
    gr.DuplicateButton(value="Duplicate Space for private use", elem_id="duplicate-button")
    chat_interface.render()
    gr.Markdown(LICENSE)

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