app.py

import gradio as gr
import subprocess
import time
import requests
from openai import OpenAI
from huggingface_hub import login, snapshot_download
import os
import stat
import tarfile
import io

TITLE = "Zero-shot Anime Knowledge Optimizer"
DESCRIPTION = """

"""

hf_token = os.getenv("HF_TOKEN")
if hf_token:
    login(token=hf_token)
else:
    raise ValueError("environment variable HF_TOKEN not found.")

repo_id = "Johnny-Z/ZAKO-0.6B"
repo_dir = snapshot_download(repo_id, repo_type='dataset')

tar_path = os.path.join(repo_dir, "llama-b7972-bin-ubuntu-x64.tar.gz")

current_dir = os.path.dirname(os.path.abspath(__file__))

with tarfile.open(tar_path, mode="r:gz") as tar:
    try:
        tar.extractall(path=current_dir, filter="data")
    except TypeError:
        tar.extractall(path=current_dir)

def _find_llama_server(base_dir: str) -> str:
    for root, _, files in os.walk(base_dir):
        if "llama-server" in files:
            return os.path.join(root, "llama-server")
    raise FileNotFoundError(f"未找到 llama-server，可执行文件不在 {base_dir} 及其子目录中")

def get_predicted_tokens_seconds() -> str:
    try:
        resp = requests.get("http://localhost:8188/metrics", timeout=2)
        resp.raise_for_status()
        for line in resp.text.splitlines():
            if line.startswith("llamacpp:predicted_tokens_seconds"):
                parts = line.split()
                if len(parts) >= 2:
                    return parts[-1]
        return "N/A"
    except requests.RequestException:
        return "N/A"

PATH_TO_SERVER_BINARY = _find_llama_server(current_dir)
PATH_TO_MODEL = os.path.join(repo_dir, "ZAKO-0.6B-Q4KM.gguf")

st = os.stat(PATH_TO_SERVER_BINARY)
os.chmod(PATH_TO_SERVER_BINARY, st.st_mode | stat.S_IXUSR | stat.S_IXGRP | stat.S_IXOTH)

def wait_for_server(url: str, timeout_s: int = 180, interval_s: float = 0.5, process: subprocess.Popen | None = None) -> None:
    start = time.time()
    while time.time() - start < timeout_s:
        if process and process.poll() is not None:
            stderr = process.stderr.read().decode("utf-8", errors="ignore") if process.stderr else ""
            raise RuntimeError(f"本地推理引擎启动失败，退出码={process.returncode}\n{stderr}")
        try:
            resp = requests.get(url, timeout=2)
            if resp.status_code == 200:
                return
        except requests.RequestException:
            pass
        time.sleep(interval_s)
    raise TimeoutError("本地推理引擎启动超时")

server_process = subprocess.Popen(
    [
        PATH_TO_SERVER_BINARY,
        "-m", PATH_TO_MODEL,
        "--ctx-size", "1280",
        "--port", "8188",
        "--metrics"
    ],
    stdout=subprocess.DEVNULL,
    stderr=subprocess.PIPE
)

print("正在启动本地推理引擎...")

wait_for_server("http://localhost:8188/health", process=server_process)

client = OpenAI(
    base_url="http://localhost:8188/v1",
    api_key="sk-no-key-required"
)

def chat(question, tags, preference_level):

    prompt = f"""

# Role

Act as an image prompt writer. Your goal is to transform inputs into **objective, physical descriptions**. You must convert abstract concepts into concrete scenes, specifying composition, lighting, and textures. Any text to be rendered must be enclosed in double quotes `""` with its typography described. **Strictly avoid** subjective adjectives or quality tags (e.g. "8K", "Masterpiece", "Best Quality"). Output **only** the final visual description.



# User Input



Prompt Quality: {preference_level}

"""
    if len(tags.strip()) > 0:
        prompt += f"\nTags: {tags}"

    if len(question.strip()) > 0:
        prompt += f"\nQuestion: {question}"

    messages = [
        {"role": "user", "content": prompt}
    ]

    response = client.chat.completions.create(
        model="ZAKO",
        messages=messages,
        top_p=0.8,
        temperature=0.8,
        stream=True
    )

    output = ""
    for chunk in response:
        if chunk.choices[0].delta.content:
            output += chunk.choices[0].delta.content
            predicted_tokens_seconds = get_predicted_tokens_seconds()
            yield output, predicted_tokens_seconds

def main():
    with gr.Blocks(title=TITLE) as demo:
        with gr.Column():
            gr.Markdown(
                value=f"<h1 style='text-align: center; margin-bottom: 1rem'>{TITLE}</h1>"
            )
            with gr.Row():
                with gr.Column(variant="panel"):
                    submit = gr.Button(value="Submit", variant="primary", size="lg")
                    stop = gr.Button(value="Stop", variant="secondary", size="lg")
                    with gr.Row():
                        text = gr.Textbox(
                            label="Simple Description",
                            value="",
                            lines=4,
                        )
                    with gr.Row():
                        tags = gr.Textbox(
                            label="Tags",
                            value="",
                            lines=2,
                        )
                    with gr.Row():    
                        preference_level = gr.Dropdown(choices=["very high", "high", "normal"], value="very high", label="Prompt Quality")
                    with gr.Row():
                        clear = gr.ClearButton(
                            components=[],
                            variant="secondary",
                            size="lg",
                        )
                    gr.Markdown(value=DESCRIPTION)
                with gr.Column(variant="panel"):
                    generated_text = gr.Textbox(label="Output", lines=20)
                    metrics_text = gr.Textbox(label="predicted_tokens_seconds", lines=1, interactive=False)
                clear.add([text, tags, generated_text, metrics_text])
        stream_evt = submit.click(
            chat,
            inputs=(text, tags, preference_level),
            outputs=(generated_text, metrics_text),
            queue=True
        )
        stop.click(fn=None, inputs=None, outputs=None, cancels=[stream_evt])

    demo.queue(max_size=10)
    demo.launch()

if __name__ == "__main__":
    main()