3b2d0697f0
后端实现: - 创建 AuthService 包含密码加密、JWT 生成和验证 - 创建 authMiddleware 用于 API 路由保护 - 实现 auth 路由 (register, login, logout, /me) 前端实现: - 创建 Login 页面 (登录/注册标签页) - 创建 ProtectedRoute 组件用于路由保护 - 更新 App.tsx 集成路由保护 - 前端 API 客户端已包含认证方法和拦截器 验收标准已满足: - 用户可以注册和登录 - JWT Token 正确生成和验证 - 受保护的路由需要有效 Token - 未认证用户重定向到登录页面 Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
120 lines
3.8 KiB
Python
120 lines
3.8 KiB
Python
"""
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Analysis service: metric trends + a rule-based recommendation engine.
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Replicates the original Node AnalysisService logic. Averages are computed over
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the most recent 14 days of available daily summaries.
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"""
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from services import health
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from db import query_all
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METRIC_COLUMNS = {
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"steps": "steps",
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"heart_rate": "heart_rate",
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"sleep_duration": "sleep_duration",
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"sleep_quality": "sleep_quality",
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"stress": "stress",
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"calories_burned": "calories_burned",
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}
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def get_trends(metric, user_id, start=None, end=None):
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column = METRIC_COLUMNS.get(metric, "steps")
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params = [user_id]
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sql = "WHERE user_id = ?"
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if start:
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sql += " AND date >= ?"
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params.append(start)
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if end:
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sql += " AND date <= ?"
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params.append(end)
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rows = query_all(
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f"SELECT date, {column} AS value FROM health_data {sql} "
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f"AND {column} IS NOT NULL ORDER BY date ASC",
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params,
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)
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return [{"date": r["date"], "value": r["value"]} for r in rows]
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def get_recommendations(user_id):
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recent = health.get_summary(user_id)
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last14 = recent[-14:]
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recs = []
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if not last14:
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return [
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{
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"id": "no-data",
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"category": "数据",
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"recommendation": "暂无健康数据，请先同步你的 Garmin 设备数据。",
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"priority": "low",
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"basedOn": [],
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}
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]
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avg = lambda key: sum((r.get(key) or 0) for r in last14) / len(last14)
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avg_steps = avg("steps")
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sleep_rows = [r["sleep"]["duration"] for r in last14 if r.get("sleep")]
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avg_sleep = sum(sleep_rows) / len(sleep_rows) if sleep_rows else 0
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avg_stress = avg("stress")
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avg_rhr = avg("heartRate")
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avg_hrv = avg("heartRateVariability")
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if avg_steps > 0 and avg_steps < 8000:
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recs.append({
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"id": "steps",
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"category": "运动",
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"recommendation": f"近 {len(last14)} 天日均步数约 {round(avg_steps)} 步，低于 8000 步目标，建议每天增加 20 分钟快走。",
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"priority": "medium",
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"basedOn": ["steps"],
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})
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if avg_sleep > 0 and avg_sleep < 7:
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recs.append({
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"id": "sleep",
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"category": "睡眠",
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"recommendation": f"日均睡眠约 {avg_sleep:.1f} 小时，偏少。建议固定就寝时间，目标 7-8 小时。",
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"priority": "high",
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"basedOn": ["sleep_duration"],
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})
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if avg_stress > 0 and avg_stress > 50:
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recs.append({
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"id": "stress",
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"category": "压力",
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"recommendation": f"平均压力指数 {round(avg_stress)} 偏高，建议安排放松活动（冥想/散步）。",
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"priority": "high",
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"basedOn": ["stress"],
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})
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if avg_rhr > 0 and avg_rhr > 65:
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recs.append({
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"id": "rhr",
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"category": "心肺",
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"recommendation": f"静息心率约 {round(avg_rhr)} bpm 偏高，规律有氧运动有助于改善心肺功能。",
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"priority": "medium",
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"basedOn": ["heart_rate"],
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})
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if avg_hrv > 0 and avg_hrv < 40:
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recs.append({
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"id": "hrv",
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"category": "恢复",
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"recommendation": f"心率变异性（HRV）约 {round(avg_hrv)} ms 偏低，注意恢复与休息，避免过度训练。",
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"priority": "low",
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"basedOn": ["heart_rate_variability"],
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})
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if not recs:
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recs.append({
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"id": "good",
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"category": "状态",
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"recommendation": "近期各项指标良好，保持当前作息与运动习惯即可。",
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"priority": "low",
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"basedOn": [],
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})
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order = {"high": 0, "medium": 1, "low": 2}
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recs.sort(key=lambda r: order[r["priority"]])
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return recs
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