7ab150537d
按要求废弃手写外壳，改用 Framework7 React（theme=ios）。参照 PeakWatch 的信息架构与卡片语言。 保留（这些是资产，不该重来）: - 数据层 services/api.ts、聚合 lib/aggregate.ts、参考区间 lib/ranges.ts - 图表组件 Chart / Ring / Sparkline / BandBar / MetricCard / MetricStrip - 经校验的配色令牌（色盲安全 + 对比度，浅深两档） 替换: - 路由与外壳交给 F7：五个 Tab 各自独立导航栈，推入详情页不影响其他 Tab - 页面转场、橡皮筋滚动、大标题折叠、半透明栏 —— 这些正是换框架的理由， 手写做不像 - 底部标签栏改用 F7 Toolbar，触控目标与安全区由框架处理 配色接入: - 新增 f7theme.css 把我们的令牌映射到 F7 的 CSS 变量，让它的导航栏/ 列表/面板与我们的图表同属一套设计，而不是两种视觉打架 - F7 的深色靠 .dark 类，我们的靠 data-theme，两者在 App 里同步切换 fix: 图标显示为原始名称（squ/hea/cale…） - iconIos/iconMd 引用的是 framework7-icons 字体，没装就只会渲染出名字 其他: - tsconfig moduleResolution 改为 bundler —— F7 用 exports 映射， node 解析方式找不到它的类型 - 登录页不套 Tab 外壳，未登录时不该出现导航 桌面与手机都要好看：内容在宽屏收进 1100px 居中列并加密卡片列数， 窄屏走底部标签栏；两档都已实机核对。 bundle 190KB -> 399KB，是换取原生手感的代价。 Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
63 lines
2.5 KiB
Plaintext
63 lines
2.5 KiB
Plaintext
# --- Server ---
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# BACKEND_PORT takes precedence over PORT. Prefer it: many tools inject PORT
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# for the frontend, and Flask would otherwise take the React dev server's port.
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BACKEND_PORT=5000
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# --- Database: sqlite (default) or mariadb ---
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DB_TYPE=sqlite
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# SQLite file (used when DB_TYPE=sqlite)
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DATABASE_PATH=./data/health.db
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# MariaDB (used when DB_TYPE=mariadb) — runs on the NAS
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# MARIADB_SOCKET=/run/mysqld/mysqld10.sock
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# MARIADB_HOST=127.0.0.1
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# MARIADB_PORT=3306
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# MARIADB_USER=root
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# MARIADB_PASSWORD=your_nas_mariadb_root_password
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# MARIADB_DATABASE=garmin_health_lab
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# --- Auth ---
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# CHANGE THIS in production! Used to sign JWTs (7-day expiry by default).
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JWT_SECRET=dev_secret_change_me
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JWT_EXPIRY_DAYS=7
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# --- CORS (comma-separated allowed front-end origins) ---
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# localhost stays in the production list on purpose: CORS is not an auth
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# boundary — every data route requires a valid JWT — so allowing a developer's
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# dev server costs nothing and saves toggling this on every session.
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CORS_ORIGIN=http://localhost:3000,http://localhost:5173
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# --- AI models (text-only, large context) ---
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# Put REAL keys in backend/.env — that file is gitignored. Never commit keys.
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# Any model whose credentials are absent is skipped automatically.
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# Self-hosted AI gateway (model id "gateway"). OpenAI-compatible; it fans out
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# over nvidia/gemini/ollama itself and rotates several Gemini keys, so it
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# absorbs single-vendor quota limits. Reached directly, bypassing any local
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# HTTP proxy. NOTE: its NVIDIA upstream is a large reasoning model — replies
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# can take 2-3 minutes, so set AI_TIMEOUT_SECONDS accordingly.
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AI_GATEWAY_BASE_URL=http://129.146.203.203:5100/v1
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AI_GATEWAY_TOKEN=
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AI_GATEWAY_MODEL=ai-gateway-auto
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# Google AI Studio -> "gemini-flash". Free-tier quota is small; 429s are common.
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GEMINI_API_KEY=
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# NVIDIA NIM -> "llama-70b", "nemotron-49b", "mistral-large".
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# Model ids come from that account's live GET /v1/models — do not guess them.
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NVIDIA_API_KEY=
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# NVIDIA_BASE_URL=https://integrate.api.nvidia.com/v1
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# Preference order. The first configured model answers; if it fails or times
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# out, the next is tried. Read per request, so changes need no restart.
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AI_MODEL_CHAIN=gateway,gemini-flash,llama-70b
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# Max days of history sent (CSV-encoded). Trimmed further per model so the
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# payload always fits that model's own context window.
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AI_DAY_BUDGET=365
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AI_TIMEOUT_SECONDS=180
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# Output cap. Reasoning models spend part of it thinking before they answer;
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# entries that need more declare their own budget in services/ai.py.
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AI_MAX_TOKENS=1024
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