Optimize tablet load: defer health check, lighten service worker, drop Google Fonts.

Co-authored-by: Cursor <cursoragent@cursor.com>
2026-06-12 14:49:58 +08:00
parent f0bb40c605
commit 7e65349878
4 changed files with 338 additions and 285 deletions
@@ -197,9 +197,14 @@ PWA_HEAD = """
  var deferredPrompt = null;

  if ("serviceWorker" in navigator) {
-    window.addEventListener("load", function () {
+    function registerSW() {
      navigator.serviceWorker.register("/sw.js", { scope: "/" }).catch(function () {});
-    });
+    }
+    if ("requestIdleCallback" in window) {
+      requestIdleCallback(registerSW, { timeout: 5000 });
+    } else {
+      setTimeout(registerSW, 3000);
+    }
  }

  function isStandalone() {
@@ -711,24 +716,37 @@ def _status_html(title: str, message: str, level: str = "warn") -> str:
    )


-def ui_check_ollama_html() -> str:
-    ok, msg = check_ollama_health()
+def ui_check_ollama_html(force: bool = False) -> str:
+    ok, msg = check_ollama_health(force=force)
    return _status_html("Ollama 节点", msg, "ok" if ok else "err")


+def ui_initial_load() -> tuple[str, str]:
+    """首屏立即返回，不发起网络请求，避免平板白屏等待。"""
+    return (
+        _status_html("Ollama 节点", "后台检测中，请稍候…", "warn"),
+        ui_speaker_status_html(),
+    )
+
+
+def ui_refresh_status_html(force: bool = False) -> tuple[str, str]:
+    """刷新 Ollama + 音色状态（供 Timer / 按钮调用）。"""
+    return ui_check_ollama_html(force=force), ui_speaker_status_html()
+
+
 def ui_speaker_status_html() -> str:
    ok, msg = speaker_is_ready()
    return _status_html("音色状态", msg, "ok" if ok else "warn")


 def build_theme() -> gr.themes.Base:
-    """高对比度暗色主题（Gradio 6.0 需在 launch() 传入）。"""
+    """高对比度暗色主题；使用系统字体，避免平板拉取 Google Fonts 卡顿。"""
    return gr.themes.Base(
        primary_hue="blue",
        secondary_hue="blue",
        neutral_hue="slate",
-        font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"],
-        font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "Consolas", "monospace"],
+        font=["system-ui", "-apple-system", "Segoe UI", "Roboto", "sans-serif"],
+        font_mono=["Consolas", "Monaco", "Courier New", "monospace"],
    ).set(
        body_background_fill="#0f1419",
        body_background_fill_dark="#0f1419",
@@ -780,7 +798,25 @@ def build_app() -> gr.Blocks:
            refresh_btn = gr.Button("🔄 刷新状态", variant="secondary", scale=0, min_width=120)

        refresh_btn.click(
-            fn=lambda: (ui_check_ollama_html(), ui_speaker_status_html()),
+            fn=lambda: ui_refresh_status_html(force=True),
+            outputs=[ollama_status, speaker_status],
+        )
+
+        # 首屏秒开：仅本地检测音色，Ollama 延后到 Timer
+        demo.load(
+            fn=ui_initial_load,
+            outputs=[ollama_status, speaker_status],
+        )
+
+        # 1 秒后后台检测 Ollama；之后每 30s 刷新（30s 内走缓存）
+        status_timer = gr.Timer(value=1, active=True)
+        status_timer.tick(
+            fn=lambda: ui_refresh_status_html(force=False),
+            outputs=[ollama_status, speaker_status],
+        )
+        status_timer_slow = gr.Timer(value=30, active=True)
+        status_timer_slow.tick(
+            fn=lambda: ui_refresh_status_html(force=True),
            outputs=[ollama_status, speaker_status],
        )

@@ -886,11 +922,6 @@ def build_app() -> gr.Blocks:
                    [pipe_raw, pipe_polished, pipe_output, pipeline_log],
                )

-        demo.load(
-            fn=lambda: (ui_check_ollama_html(), ui_speaker_status_html()),
-            outputs=[ollama_status, speaker_status],
-        )
-
    return demo


@@ -1,85 +1,90 @@
-"""
-Trading Studio 全局配置模块
-统一存放局域网节点、模型名称、固定 Prompt 及本地路径。
-"""
-
-from pathlib import Path
-
-# ---------------------------------------------------------------------------
-# 网络与服务
-# ---------------------------------------------------------------------------
-# 远程 Ollama 节点（局域网大模型审查润色）
-OLLAMA_HOST = "192.168.8.64"
-OLLAMA_PORT = 11434
-OLLAMA_URL = f"http://{OLLAMA_HOST}:{OLLAMA_PORT}/api/chat"
-
-# 指定无限制版 Gemma4 模型
-MODEL_NAME = "huihui_ai/gemma-4-abliterated:e4b"
-
-# Gradio 中控固定端口（硬性死规则）
-HOST = "0.0.0.0"
-PORT = 5683
-
-# HTTP 请求超时（秒）
-OLLAMA_TIMEOUT = 60
-
-# ---------------------------------------------------------------------------
-# LLM 系统 Prompt
-# ---------------------------------------------------------------------------
-SYSTEM_PROMPT = (
-    "你是一个冷静、极其严格的数字资产量化交易员。"
-    "请把下面这段口语化、包含结巴和逻辑混乱的交易复盘录音转写，"
-    "润色成一段逻辑清晰、行文通顺的 B 站长视频反思配音稿。"
-    "语气要内向、克制、严谨。"
-    "如果原视频中有由于心态不好、违背交易纪律（如手贱乱开仓、提前平仓）"
-    "导致少赚或亏损的部分，请用冷酷、严厉的语气狠狠地自我吐槽、反思该点。"
-    "去掉所有无意义的口头禅，字数不做删减。"
-)
-
-# ---------------------------------------------------------------------------
-# Faster-Whisper 配置
-# ---------------------------------------------------------------------------
-WHISPER_MODEL_SIZE = "small"
-WHISPER_DEVICE = "cuda"
-WHISPER_COMPUTE_TYPE = "float16"
-WHISPER_LANGUAGE = "zh"
-
-# ---------------------------------------------------------------------------
-# ChatTTS 配置
-# ---------------------------------------------------------------------------
-# 标准生产安装路径（/opt，root 部署）
-INSTALL_DIR = Path("/opt/Trading_Studio")
-
-# 项目根目录（开发/生产均自适应，以实际 app.py 所在目录为准）
-BASE_DIR = Path(__file__).resolve().parent
-
-# 固定音色 Embedding 存储路径
-SPEAKER_EMB_PATH = BASE_DIR / "speaker_emb.pt"
-
-# 合成音频输出目录
-OUTPUT_DIR = BASE_DIR / "outputs"
-OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-
-# ChatTTS 采样率（Hz）
-TTS_SAMPLE_RATE = 24000
-
-# 音色样本时长建议（秒）
-SPEAKER_SAMPLE_MIN_SEC = 10
-SPEAKER_SAMPLE_MAX_SEC = 30
-
-# TTS 推理默认参数（低 temperature 有助于音色稳定）
-TTS_TEMPERATURE = 0.3
-TTS_TOP_P = 0.7
-TTS_TOP_K = 20
-TTS_SPEED_PROMPT = "[speed_5]"
-
-# ---------------------------------------------------------------------------
-# 上传临时文件目录
-# ---------------------------------------------------------------------------
-UPLOAD_DIR = BASE_DIR / "uploads"
-UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
-
-# ---------------------------------------------------------------------------
-# Git 仓库（文档引用）
-# ---------------------------------------------------------------------------
-GIT_REPO_URL = "https://git.bz121.com/dekun/Trading_Studio.git"
+"""
+Trading Studio 全局配置模块
+统一存放局域网节点、模型名称、固定 Prompt 及本地路径。
+"""
+
+from pathlib import Path
+
+# ---------------------------------------------------------------------------
+# 网络与服务
+# ---------------------------------------------------------------------------
+# 远程 Ollama 节点（局域网大模型审查润色）
+OLLAMA_HOST = "192.168.8.64"
+OLLAMA_PORT = 11434
+OLLAMA_URL = f"http://{OLLAMA_HOST}:{OLLAMA_PORT}/api/chat"
+
+# 指定无限制版 Gemma4 模型
+MODEL_NAME = "huihui_ai/gemma-4-abliterated:e4b"
+
+# Gradio 中控固定端口（硬性死规则）
+HOST = "0.0.0.0"
+PORT = 5683
+
+# HTTP 请求超时（秒）
+OLLAMA_TIMEOUT = 60
+
+# 健康检查快速超时（秒）— 避免平板首屏被长时间阻塞
+HEALTH_CHECK_CONNECT_TIMEOUT = 2
+HEALTH_CHECK_READ_TIMEOUT = 3
+HEALTH_CHECK_CACHE_SECONDS = 30
+
+# ---------------------------------------------------------------------------
+# LLM 系统 Prompt
+# ---------------------------------------------------------------------------
+SYSTEM_PROMPT = (
+    "你是一个冷静、极其严格的数字资产量化交易员。"
+    "请把下面这段口语化、包含结巴和逻辑混乱的交易复盘录音转写，"
+    "润色成一段逻辑清晰、行文通顺的 B 站长视频反思配音稿。"
+    "语气要内向、克制、严谨。"
+    "如果原视频中有由于心态不好、违背交易纪律（如手贱乱开仓、提前平仓）"
+    "导致少赚或亏损的部分，请用冷酷、严厉的语气狠狠地自我吐槽、反思该点。"
+    "去掉所有无意义的口头禅，字数不做删减。"
+)
+
+# ---------------------------------------------------------------------------
+# Faster-Whisper 配置
+# ---------------------------------------------------------------------------
+WHISPER_MODEL_SIZE = "small"
+WHISPER_DEVICE = "cuda"
+WHISPER_COMPUTE_TYPE = "float16"
+WHISPER_LANGUAGE = "zh"
+
+# ---------------------------------------------------------------------------
+# ChatTTS 配置
+# ---------------------------------------------------------------------------
+# 标准生产安装路径（/opt，root 部署）
+INSTALL_DIR = Path("/opt/Trading_Studio")
+
+# 项目根目录（开发/生产均自适应，以实际 app.py 所在目录为准）
+BASE_DIR = Path(__file__).resolve().parent
+
+# 固定音色 Embedding 存储路径
+SPEAKER_EMB_PATH = BASE_DIR / "speaker_emb.pt"
+
+# 合成音频输出目录
+OUTPUT_DIR = BASE_DIR / "outputs"
+OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+
+# ChatTTS 采样率（Hz）
+TTS_SAMPLE_RATE = 24000
+
+# 音色样本时长建议（秒）
+SPEAKER_SAMPLE_MIN_SEC = 10
+SPEAKER_SAMPLE_MAX_SEC = 30
+
+# TTS 推理默认参数（低 temperature 有助于音色稳定）
+TTS_TEMPERATURE = 0.3
+TTS_TOP_P = 0.7
+TTS_TOP_K = 20
+TTS_SPEED_PROMPT = "[speed_5]"
+
+# ---------------------------------------------------------------------------
+# 上传临时文件目录
+# ---------------------------------------------------------------------------
+UPLOAD_DIR = BASE_DIR / "uploads"
+UPLOAD_DIR.mkdir(parents=True, exist_ok=True)
+
+# ---------------------------------------------------------------------------
+# Git 仓库（文档引用）
+# ---------------------------------------------------------------------------
+GIT_REPO_URL = "https://git.bz121.com/dekun/Trading_Studio.git"
@@ -1,162 +1,198 @@
-"""
-远程 Ollama LLM 润色服务
-通过局域网 HTTP 请求 Gemma4 模型，对交易复盘转写稿进行纪律审判式润色。
-"""
-
-from __future__ import annotations
-
-import logging
-from typing import Tuple
-
-import requests
-
-from config import MODEL_NAME, OLLAMA_TIMEOUT, OLLAMA_URL, SYSTEM_PROMPT
-
-logger = logging.getLogger(__name__)
-
-
-def _build_payload(raw_text: str) -> dict:
-    """构造 Ollama /api/chat 非流式请求体。"""
-    return {
-        "model": MODEL_NAME,
-        "messages": [
-            {"role": "system", "content": SYSTEM_PROMPT},
-            {
-                "role": "user",
-                "content": (
-                    "以下是我的交易复盘录音转写原文，请严格按系统要求润色：\n\n"
-                    f"{raw_text}"
-                ),
-            },
-        ],
-        "stream": False,
-        "options": {
-            "temperature": 0.7,
-            "num_predict": 4096,
-        },
-    }
-
-
-def _extract_content(response_json: dict) -> str:
-    """从 Ollama 响应 JSON 中提取 assistant 文本。"""
-    # /api/chat 标准格式
-    message = response_json.get("message")
-    if isinstance(message, dict):
-        content = message.get("content", "").strip()
-        if content:
-            return content
-
-    # 兼容 /api/generate 格式（部分旧版或代理）
-    if "response" in response_json:
-        content = str(response_json["response"]).strip()
-        if content:
-            return content
-
-    raise ValueError(f"无法从 Ollama 响应中解析文本内容: {response_json}")
-
-
-def polish_text(raw_text: str) -> Tuple[bool, str]:
-    """
-    调用远程 Ollama 对原始转写文本进行润色。
-
-    Args:
-        raw_text: Whisper 转写得到的原始口语文本
-
-    Returns:
-        (success, polished_text_or_error_message)
-    """
-    if not raw_text or not raw_text.strip():
-        return False, "润色输入为空，请先完成语音识别。"
-
-    payload = _build_payload(raw_text.strip())
-
-    try:
-        logger.info("正在请求 Ollama: %s, model=%s", OLLAMA_URL, MODEL_NAME)
-        response = requests.post(
-            OLLAMA_URL,
-            json=payload,
-            timeout=OLLAMA_TIMEOUT,
-        )
-        response.raise_for_status()
-
-        data = response.json()
-        polished = _extract_content(data)
-
-        if not polished:
-            return False, "Ollama 返回内容为空，请检查模型是否正常加载。"
-
-        logger.info("润色完成，输出字数: %d", len(polished))
-        return True, polished
-
-    except requests.exceptions.ConnectTimeout:
-        err = (
-            f"连接 Ollama 超时（>{OLLAMA_TIMEOUT}s）。"
-            f"请确认 {OLLAMA_URL} 可达且 Ollama 服务已启动。"
-        )
-        logger.error(err)
-        return False, err
-
-    except requests.exceptions.ReadTimeout:
-        err = (
-            f"Ollama 响应超时（>{OLLAMA_TIMEOUT}s）。"
-            "模型可能正在加载或生成长度过长，请稍后重试。"
-        )
-        logger.error(err)
-        return False, err
-
-    except requests.exceptions.ConnectionError as exc:
-        err = (
-            f"无法连接到 Ollama 节点 ({OLLAMA_URL})。"
-            "请检查局域网连通性、防火墙及 Ollama 是否监听 0.0.0.0:11434。\n"
-            f"详情: {exc}"
-        )
-        logger.error(err)
-        return False, err
-
-    except requests.exceptions.HTTPError as exc:
-        status = exc.response.status_code if exc.response is not None else "?"
-        body = exc.response.text[:500] if exc.response is not None else ""
-        err = (
-            f"Ollama HTTP 错误 ({status})。"
-            f"请确认模型 `{MODEL_NAME}` 已通过 ollama pull 下载。\n"
-            f"响应片段: {body}"
-        )
-        logger.error(err)
-        return False, err
-
-    except ValueError as exc:
-        logger.error("Ollama 响应解析失败: %s", exc)
-        return False, str(exc)
-
-    except requests.exceptions.RequestException as exc:
-        err = f"Ollama 请求异常: {exc}"
-        logger.exception(err)
-        return False, err
-
-    except Exception as exc:
-        err = f"润色过程发生未知错误: {exc}"
-        logger.exception(err)
-        return False, err
-
-
-def check_ollama_health() -> Tuple[bool, str]:
-    """
-    快速检测 Ollama 节点是否在线（不触发完整推理）。
-
-    Returns:
-        (online, message)
-    """
-    base_url = OLLAMA_URL.rsplit("/api/", 1)[0]
-    try:
-        resp = requests.get(f"{base_url}/api/tags", timeout=10)
-        resp.raise_for_status()
-        tags = resp.json().get("models", [])
-        model_names = [m.get("name", "") for m in tags]
-        if any(MODEL_NAME.split(":")[0] in name for name in model_names):
-            return True, f"Ollama 在线，已检测到模型: {MODEL_NAME}"
-        return True, (
-            f"Ollama 在线，但未找到模型 {MODEL_NAME}，"
-            f"请执行: ollama pull {MODEL_NAME}"
-        )
-    except Exception as exc:
-        return False, f"Ollama 不可达: {exc}"
+"""
+远程 Ollama LLM 润色服务
+通过局域网 HTTP 请求 Gemma4 模型，对交易复盘转写稿进行纪律审判式润色。
+"""
+
+from __future__ import annotations
+
+import logging
+import time
+from typing import Tuple
+
+import requests
+
+from config import (
+    HEALTH_CHECK_CACHE_SECONDS,
+    HEALTH_CHECK_CONNECT_TIMEOUT,
+    HEALTH_CHECK_READ_TIMEOUT,
+    MODEL_NAME,
+    OLLAMA_TIMEOUT,
+    OLLAMA_URL,
+    SYSTEM_PROMPT,
+)
+
+logger = logging.getLogger(__name__)
+
+# 健康检查短时缓存，避免平板/手机反复打开页面时重复等待
+_health_cache: dict = {"ts": 0.0, "ok": False, "msg": ""}
+
+
+def _build_payload(raw_text: str) -> dict:
+    """构造 Ollama /api/chat 非流式请求体。"""
+    return {
+        "model": MODEL_NAME,
+        "messages": [
+            {"role": "system", "content": SYSTEM_PROMPT},
+            {
+                "role": "user",
+                "content": (
+                    "以下是我的交易复盘录音转写原文，请严格按系统要求润色：\n\n"
+                    f"{raw_text}"
+                ),
+            },
+        ],
+        "stream": False,
+        "options": {
+            "temperature": 0.7,
+            "num_predict": 4096,
+        },
+    }
+
+
+def _extract_content(response_json: dict) -> str:
+    """从 Ollama 响应 JSON 中提取 assistant 文本。"""
+    # /api/chat 标准格式
+    message = response_json.get("message")
+    if isinstance(message, dict):
+        content = message.get("content", "").strip()
+        if content:
+            return content
+
+    # 兼容 /api/generate 格式（部分旧版或代理）
+    if "response" in response_json:
+        content = str(response_json["response"]).strip()
+        if content:
+            return content
+
+    raise ValueError(f"无法从 Ollama 响应中解析文本内容: {response_json}")
+
+
+def polish_text(raw_text: str) -> Tuple[bool, str]:
+    """
+    调用远程 Ollama 对原始转写文本进行润色。
+
+    Args:
+        raw_text: Whisper 转写得到的原始口语文本
+
+    Returns:
+        (success, polished_text_or_error_message)
+    """
+    if not raw_text or not raw_text.strip():
+        return False, "润色输入为空，请先完成语音识别。"
+
+    payload = _build_payload(raw_text.strip())
+
+    try:
+        logger.info("正在请求 Ollama: %s, model=%s", OLLAMA_URL, MODEL_NAME)
+        response = requests.post(
+            OLLAMA_URL,
+            json=payload,
+            timeout=OLLAMA_TIMEOUT,
+        )
+        response.raise_for_status()
+
+        data = response.json()
+        polished = _extract_content(data)
+
+        if not polished:
+            return False, "Ollama 返回内容为空，请检查模型是否正常加载。"
+
+        logger.info("润色完成，输出字数: %d", len(polished))
+        return True, polished
+
+    except requests.exceptions.ConnectTimeout:
+        err = (
+            f"连接 Ollama 超时（>{OLLAMA_TIMEOUT}s）。"
+            f"请确认 {OLLAMA_URL} 可达且 Ollama 服务已启动。"
+        )
+        logger.error(err)
+        return False, err
+
+    except requests.exceptions.ReadTimeout:
+        err = (
+            f"Ollama 响应超时（>{OLLAMA_TIMEOUT}s）。"
+            "模型可能正在加载或生成长度过长，请稍后重试。"
+        )
+        logger.error(err)
+        return False, err
+
+    except requests.exceptions.ConnectionError as exc:
+        err = (
+            f"无法连接到 Ollama 节点 ({OLLAMA_URL})。"
+            "请检查局域网连通性、防火墙及 Ollama 是否监听 0.0.0.0:11434。\n"
+            f"详情: {exc}"
+        )
+        logger.error(err)
+        return False, err
+
+    except requests.exceptions.HTTPError as exc:
+        status = exc.response.status_code if exc.response is not None else "?"
+        body = exc.response.text[:500] if exc.response is not None else ""
+        err = (
+            f"Ollama HTTP 错误 ({status})。"
+            f"请确认模型 `{MODEL_NAME}` 已通过 ollama pull 下载。\n"
+            f"响应片段: {body}"
+        )
+        logger.error(err)
+        return False, err
+
+    except ValueError as exc:
+        logger.error("Ollama 响应解析失败: %s", exc)
+        return False, str(exc)
+
+    except requests.exceptions.RequestException as exc:
+        err = f"Ollama 请求异常: {exc}"
+        logger.exception(err)
+        return False, err
+
+    except Exception as exc:
+        err = f"润色过程发生未知错误: {exc}"
+        logger.exception(err)
+        return False, err
+
+
+def check_ollama_health(force: bool = False) -> Tuple[bool, str]:
+    """
+    快速检测 Ollama 节点是否在线（不触发完整推理）。
+    默认 2+3 秒超时，结果缓存 30 秒，避免平板首屏长时间白屏。
+
+    Returns:
+        (online, message)
+    """
+    global _health_cache
+
+    now = time.time()
+    if (
+        not force
+        and _health_cache["msg"]
+        and (now - _health_cache["ts"]) < HEALTH_CHECK_CACHE_SECONDS
+    ):
+        return _health_cache["ok"], _health_cache["msg"]
+
+    base_url = OLLAMA_URL.rsplit("/api/", 1)[0]
+    timeout = (HEALTH_CHECK_CONNECT_TIMEOUT, HEALTH_CHECK_READ_TIMEOUT)
+
+    try:
+        resp = requests.get(f"{base_url}/api/tags", timeout=timeout)
+        resp.raise_for_status()
+        tags = resp.json().get("models", [])
+        model_names = [m.get("name", "") for m in tags]
+        if any(MODEL_NAME.split(":")[0] in name for name in model_names):
+            msg = f"Ollama 在线，已检测到模型: {MODEL_NAME}"
+            ok = True
+        else:
+            ok = True
+            msg = (
+                f"Ollama 在线，但未找到模型 {MODEL_NAME}，"
+                f"请执行: ollama pull {MODEL_NAME}"
+            )
+    except requests.exceptions.Timeout:
+        ok, msg = False, (
+            f"Ollama 检测超时（>{HEALTH_CHECK_READ_TIMEOUT}s）。"
+            "页面已加载，可稍后点击「刷新状态」重试。"
+        )
+    except Exception as exc:
+        ok, msg = False, f"Ollama 不可达: {exc}"
+
+    _health_cache.update({"ts": now, "ok": ok, "msg": msg})
+    return ok, msg
@@ -1,14 +1,10 @@
 /**
- * Trading Studio PWA Service Worker
- * 缓存应用壳，支持离线打开已访问页面；API 请求始终走网络。
+ * Trading Studio PWA Service Worker（轻量版）
+ * 仅处理导航请求，不拦截 Gradio 静态资源 — 避免平板端加载变慢。
 */
-const CACHE_NAME = "trading-studio-v1";
-const SHELL = ["/", "/manifest.webmanifest"];
+const CACHE_NAME = "trading-studio-v2";

 self.addEventListener("install", (event) => {
-  event.waitUntil(
-    caches.open(CACHE_NAME).then((cache) => cache.addAll(SHELL)).catch(() => {})
-  );
  self.skipWaiting();
 });

@@ -23,28 +19,13 @@ self.addEventListener("activate", (event) => {

 self.addEventListener("fetch", (event) => {
  const { request } = event;
-  const url = new URL(request.url);

-  // API / 文件上传 / Gradio 动态接口不走缓存
-  if (
-    request.method !== "GET" ||
-    url.pathname.startsWith("/gradio_api") ||
-    url.pathname.startsWith("/file=") ||
-    url.pathname.startsWith("/upload") ||
-    url.pathname.includes("call")
-  ) {
+  // 仅缓存页面导航，Gradio JS/CSS/API 全部直连网络
+  if (request.method !== "GET" || request.mode !== "navigate") {
    return;
  }

  event.respondWith(
-    fetch(request)
-      .then((response) => {
-        if (response.ok && url.origin === self.location.origin) {
-          const clone = response.clone();
-          caches.open(CACHE_NAME).then((cache) => cache.put(request, clone));
-        }
-        return response;
-      })
-      .catch(() => caches.match(request).then((r) => r || caches.match("/")))
+    fetch(request).catch(() => caches.match("/"))
  );
 });