224 lines
8.1 KiB
Python
224 lines
8.1 KiB
Python
import json, datetime, textwrap, requests, gradio as gr
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from pathlib import Path
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from collections import deque
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import queue, threading, time
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# ────────────────── 基础配置 ──────────────────
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API_KEY = "token-abc123"
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MODEL_PATH = Path("/root/.cradle/Alibaba/Qwen3-30B-A3B-Base")
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def model_name(path: Path):
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cfg = path / "config.json"
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if cfg.exists():
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data = json.load(cfg.open())
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return data.get("architectures", [None])[0] or data.get("model_type") or path.name
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return path.name
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MODEL_NAME = model_name(MODEL_PATH)
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now = lambda: datetime.datetime.now().strftime("%H:%M:%S")
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# ────────────────── 日志队列 ──────────────────
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LOG_Q: "queue.Queue[str]" = queue.Queue()
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LOG_TXT = ""
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def log(msg):
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print(msg, flush=True)
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LOG_Q.put(msg)
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prev_log_value = ""
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def consume_logs(dummy=None):
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global LOG_TXT, prev_log_value
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buf = deque(LOG_TXT.splitlines(), maxlen=400)
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while not LOG_Q.empty():
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buf.append(LOG_Q.get())
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LOG_TXT = "\n".join(buf)
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if LOG_TXT != prev_log_value:
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prev_log_value = LOG_TXT
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return gr.update(value=LOG_TXT)
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return gr.update()
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# ────────────────── 后端调用 ──────────────────
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def backend(text, sampling, api_suffix):
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url = f"http://localhost:30000{api_suffix}"
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if api_suffix == "/generate":
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payload = {"model": MODEL_NAME, "text": text, "sampling_params": sampling}
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elif api_suffix == "/v1/completions":
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payload = {
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"model": MODEL_NAME,
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"prompt": text,
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**sampling
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}
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elif api_suffix == "/v1/chat/completions":
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payload = {
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"model": MODEL_NAME,
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"messages": text, # ← 这里 text 实际是 messages list
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**sampling
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}
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log(f"\n🟡 [{now()}] POST {url}\n{json.dumps(payload, ensure_ascii=False, indent=2)}")
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try:
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r = requests.post(url,
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headers={"Authorization": f"Bearer {API_KEY}",
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"Content-Type": "application/json"},
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json=payload, timeout=180)
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try:
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data = r.json()
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except Exception:
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data = {}
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if api_suffix == "/generate":
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txt = data.get("text", "").strip()
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meta = data.get("meta_info", {})
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fr = meta.get("finish_reason")
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ctok = meta.get("completion_tokens")
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elif api_suffix == "/v1/completions":
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choice = data.get("choices", [{}])[0]
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txt = choice.get("text", "").strip()
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fr = choice.get("finish_reason")
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ctok = data.get("usage", {}).get("completion_tokens")
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elif api_suffix == "/v1/chat/completions":
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choice = data.get("choices", [{}])[0]
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msg = choice.get("message", {})
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txt = msg.get("content", "").strip()
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# 新增：从 usage 获取 completion_tokens
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ctok = data.get("usage", {}).get("completion_tokens")
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fr = choice.get("finish_reason") # 如果后续需要 finish reason
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log(f"🟢 [{now()}] HTTP {r.status_code} tokens={ctok} finish={fr}\n"
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f"🟢 resp800={r.text[:800]!r}")
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if r.status_code != 200:
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return f"[HTTP {r.status_code}] {r.text[:300]}"
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return txt or "[⚠ 空]"
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except Exception as e:
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log(f"[❌ 请求异常] {e}")
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return f"[❌ 请求异常] {e}"
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# ────────────────── Chat 回调 ──────────────────
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def chat(
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user_msg, history,
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max_new, temp, top_p, top_k,
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rep_pen, pres_pen, stop_raw,
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api_suffix, log_state
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):
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from queue import Queue, Empty
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# 解析传入的 ChatInput 格式
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user = user_msg["text"] if isinstance(user_msg, dict) and "text" in user_msg else user_msg
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# 构造 OpenAI 风格 messages，仅用于 /v1/chat/completions
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if api_suffix == "/v1/chat/completions":
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messages = []
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# for u, a in history:
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# messages.append({"role": "user", "content": u})
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# messages.append({"role": "assistant", "content": a})
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messages = history[:] # 正确使用 OpenAI 格式
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messages.append({"role": "user", "content": user})
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prompt_input = messages
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# user_input = user_msg["text"] if isinstance(user_msg, dict) and "text" in user_msg else user_msg
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# messages.append({"role": "user", "content": user_input})
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# prompt_input = messages
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else:
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prompt_input = user # 原来的单轮文本 prompt
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stop = [s.strip() for s in stop_raw.split(",") if s.strip()] or None
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samp = {
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("max_tokens" if api_suffix == "/v1/completions" else "max_new_tokens"): int(max_new),
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"temperature": temp,
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"top_p": top_p,
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"top_k": int(top_k),
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"repetition_penalty": rep_pen,
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"presence_penalty": pres_pen,
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**({"stop": stop} if stop else {})
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}
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result_q = Queue()
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def worker():
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#out = backend(user, samp, api_suffix)
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out = backend(prompt_input, samp, api_suffix)
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result_q.put(out)
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# threading.Thread(target=worker).start()
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thread = threading.Thread(target=worker, daemon=True)
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thread.start()
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# yield "⏳ 正在生成中...", log_state
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while True:
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# ⚠️ 线程已结束且队列已空 → 直接 return 让生成器终止
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if not thread.is_alive() and result_q.empty():
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break
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# return # ← 新增这一行
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try:
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result = result_q.get(timeout=0.1)
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except Empty:
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continue
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# 统一格式
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if isinstance(result, str):
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result = {"text": result}
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elif not isinstance(result, dict) or "text" not in result:
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result = {"text": str(result)}
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# yield result, log_state # 第一次真正把模型回复丢给前端
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if api_suffix == "/v1/chat/completions":
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history.append({"role": "assistant", "content": result["text"]})
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yield history, log_state
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else:
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yield result["text"], log_state
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return # ← 把旧的 break 换成 return
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# ────────────────── Gradio UI ──────────────────
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with gr.Blocks(title="调试界面") as demo:
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gr.Markdown(f"## 💬 调试界面 \n权重 **{MODEL_PATH.name}**")
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with gr.Row():
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api_choice = gr.Dropdown(choices=["/generate", "/v1/completions", "/v1/chat/completions"],
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value="/generate", label="选择推理接口")
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with gr.Row():
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max_new = gr.Slider(32, 32768, 128, label="max_new_tokens")
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temp = gr.Slider(0, 1.5, 0.8, step=0.05, label="temperature")
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with gr.Row():
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top_p = gr.Slider(0, 1, 0.95, step=0.01, label="top_p")
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top_k = gr.Slider(0, 200, 50, step=1, label="top_k")
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with gr.Row():
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rep_pen = gr.Slider(0.8, 2, 1.05, step=0.01, label="repetition_penalty")
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pres_pen= gr.Slider(0, 2, 0.0, step=0.05, label="presence_penalty")
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stop_txt = gr.Textbox("", label="stop 序列（逗号分隔）")
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log_state = gr.State("")
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dbg_chk = gr.Checkbox(label="📜 显示 Debug 面板", value=False)
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log_box = gr.Textbox(label="实时日志", lines=20, interactive=False, visible=False)
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chat = gr.ChatInterface(
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fn=chat,
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additional_inputs=[max_new, temp, top_p, top_k,
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rep_pen, pres_pen, stop_txt,
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api_choice, log_state],
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additional_outputs=[log_state],
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type="messages"
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)
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timer = gr.Timer(1.0, render=True)
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timer.tick(
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fn=consume_logs,
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inputs=[],
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outputs=[log_box],
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)
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log_state.change(lambda txt: gr.update(value=txt), log_state, log_box)
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dbg_chk.change(lambda v: gr.update(visible=v), dbg_chk, log_box)
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demo.launch(server_name="0.0.0.0", server_port=30001)
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