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

Browse Source 
Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
dekun
2026-06-12 14:49:58 +08:00
parent f0bb40c605
commit 7e65349878
4 changed files with 338 additions and 285 deletions
Download Patch File Download Diff File Expand all files Collapse all files
app.py
+44 -13
View File
@@ -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
config.py
+90 -85
View File
@@ -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"
llm_service.py
+198 -162
View File
@@ -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
pwa/sw.js
+6 -25
View File
@@ -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("/"))
);
});