feat(ai): 每个数据页面都有 AI 解读，靠一条带优先级的生产者/消费者队列
原来只有今日页有晨报、指标详情页有归因，其余页面一片空白。现在除设置外 的 10 个页面都有：健康、睡眠、运动、趋势、每日、身体成分、成绩预测、 身体年龄、挑战赛、运动详情。 不是给每个页面写一套，而是一个通用管线： - services/scopes.py：一个页面一个 context builder，返回同一个信封。 context["highlights"] 是已经算好的白话事实——模型负责解读它们，模型不 可用时规则引擎原样渲染。两者引用同一批数字，所以降级读起来不像换了个 App。 没数据的页面返回 None，宁可不出卡片，也不让模型对着空表格发挥。 - coach.scope_messages / parse_scope_insight：一套提示词吃所有页面，页面 的差异全在 context 里，加页面 = 加一个 builder。 - 前端 <AiPanel scope="…">：一个组件渲染所有页面，轮询逻辑抽成 lib/insight.ts 的 usePolledInsight，晨报卡也改用它。 ## 队列 一次生成 40 秒到 4.5 分钟，所以什么都不能在请求里生成。页面只负责入队， worker 负责消费（services/jobs.py）。 优先级才是用队列而不是后台线程的理由：同步完成后 prefetch 把所有页面按 背景优先级排进去，可能要跑半小时；而用户一打开某个页面，那个页面的任务 立刻提到队首、下一个就跑。你在看什么，队列就在算什么。 队列放在数据库而不是内存里，因为 gunicorn 有两个 worker：任务带 holder 声明后回读确认，和 scheduler.py 抢 tick 是同一套做法。id 由 user+kind+subject 推导，所以每几秒一次的轮询是幂等的入队，不会每几秒堆一 个任务。 ## 网关中断时踩到的两个坑（当场修了） 写完正好赶上 oracle 那台机器不通，于是看到： - 三次失败后任务被永久标 failed，网关恢复了也不会重试——一次瞬时中断就把 那个页面的解读判了死刑，直到它的数据碰巧变化。加了冷却期，过期后重置 尝试次数再排一次。 - 队列已经放弃了，页面还在 pending 转圈，要转满 8 分钟才停。meta.pending 现在跟着队列状态走，并把失败原因带给卡片。 顺带把 BAND_SOURCES 从 routes/settings.py 下沉到 services/insights.py： 教练要拿它做参照，而 services 不该反向依赖 routes。 Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
This commit is contained in:
ericwyuan
@@ -13,6 +13,7 @@ import db
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from config import CORS_ORIGINS, PORT, STATIC_DIR
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from routes import auth, garmin, health, analysis, settings
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from services import scheduler
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from services import jobs as ai_jobs
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from services import garmin as garmin_svc
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@@ -32,6 +33,14 @@ def create_app():
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# one of them actually runs a given tick.
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scheduler.start()
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# The AI coach's consumer. Same story: the queue is in the database, so
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# every worker can run one and a given job is still generated once.
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# Claims held by the previous process are released first — otherwise they
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# sit in `running` until they time out, which to the screen waiting on one
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# is indistinguishable from a generation that never finishes.
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ai_jobs.reset_stale_claims()
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ai_jobs.start()
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# In production the built React app is served by this same process, so the
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# deployment is a single port with no reverse proxy to configure. In
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# development STATIC_DIR does not exist and the CRA dev server serves the
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@@ -302,6 +302,31 @@ CREATE TABLE IF NOT EXISTS ai_recommendations (
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-- attribution, and `subject` is the day (briefing) or metric+range (trend).
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-- Same reasoning as ai_recommendations above — a generation costs minutes, so
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-- it can never sit inside a page load.
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-- The coach's work queue. Generating one insight costs minutes against the
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-- gateway, so nothing is produced inside a request: screens enqueue, a worker
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-- consumes. `priority` is what makes the screen the user is actually looking
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-- at jump ahead of the backfill queued after a sync (lower runs first).
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--
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-- `id` is derived from user+kind+subject, so enqueueing the same work twice
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-- updates one row rather than piling up duplicates — which is what keeps a
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-- poll every few seconds from queueing a job every few seconds.
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CREATE TABLE IF NOT EXISTS ai_jobs (
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id VARCHAR(160) PRIMARY KEY,
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user_id VARCHAR(64) NOT NULL,
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kind VARCHAR(32) NOT NULL,
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subject VARCHAR(96) NOT NULL,
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fingerprint VARCHAR(64),
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priority INT NOT NULL DEFAULT 10,
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status VARCHAR(16) NOT NULL DEFAULT 'pending',
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attempts INT NOT NULL DEFAULT 0,
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error TEXT,
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holder VARCHAR(64),
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claimed_at DATETIME,
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created_at DATETIME,
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updated_at DATETIME,
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FOREIGN KEY (user_id) REFERENCES users(id)
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);
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CREATE TABLE IF NOT EXISTS ai_insights (
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id VARCHAR(160) PRIMARY KEY,
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user_id VARCHAR(64) NOT NULL,
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@@ -7,6 +7,8 @@ from auth import require_auth
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from services import analysis as analysis_svc
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from services import ai as ai_svc
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from services import insights
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from services import jobs as ai_jobs
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from services import scopes
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bp = Blueprint("analysis", __name__)
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@@ -135,3 +137,39 @@ def copilot():
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# until it completes, which would undo the point of streaming it.
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headers={"Cache-Control": "no-cache", "X-Accel-Buffering": "no"},
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)
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@bp.route("/insight", methods=["GET"])
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@require_auth
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def insight():
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"""One screen's AI reading. `?scope=` names the screen.
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Answers immediately, like the briefing: the computed highlights come back
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with `meta.pending` while the model's version is generated. Opening a
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screen queues it at interactive priority, ahead of any backfill still
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running, so what you are looking at is what the queue works on next.
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`?subject=` identifies the item for per-item screens (`activity` needs an
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activity id, `daily` takes a date).
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"""
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scope = request.args.get("scope")
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if scope not in scopes.SCOPES:
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return jsonify({
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"error": f"不支持的页面: {scope}",
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"supported": sorted(scopes.SCOPES),
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}), 400
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return jsonify(analysis_svc.get_scope_insight(
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g.user_id, scope,
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subject=request.args.get("subject") or None,
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refresh=_flag("refresh"),
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))
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@bp.route("/insight/queue", methods=["GET"])
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@require_auth
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def insight_queue():
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"""What the coach still has to generate — for a progress indicator."""
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return jsonify({
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"pending": ai_jobs.pending_count(g.user_id),
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"enabled": ai_jobs.ENABLED,
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})
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@@ -3,6 +3,7 @@ from flask import Blueprint, request, g, jsonify
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from auth import require_auth
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from services import settings as settings_svc
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from services.insights import BAND_SOURCES
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from services import fitness_age
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bp = Blueprint("settings", __name__)
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@@ -54,32 +55,3 @@ def rating_basis():
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"AI 只负责解读这些结果，不参与设定任何阈值。"
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),
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})
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# Kept beside the UI's RANGES table (client/src/lib/ranges.ts). Each entry says
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# where that metric's band edges came from — the honest answer for several of
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# them is "general-population orientation figures", and it says so.
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BAND_SOURCES = [
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{"metric": "步数", "bands": "<5k 偏低 · 5–8k 一般 · 8–12k 达标 · >12k 优秀",
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"source": "步数与死亡率的队列研究（约 8000 步起获益明显，12000 步后趋平）"},
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{"metric": "静息心率", "bands": "<50 很低 · 50–65 正常 · 65–75 偏高 · >75 较高",
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"source": "健康成人静息心率 60–100 bpm 为正常范围，规律运动者常低于 60"},
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{"metric": "心率变异性", "bands": "<25 偏低 · 25–40 一般 · 40–70 良好 · >70 很好",
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"source": "夜间 RMSSD 的一般人群分布；个体差异极大，趋势比绝对值更有意义"},
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{"metric": "睡眠时长", "bands": "<6h 不足 · 6–7h 偏少 · 7–9h 充足 · >9h 偏多",
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"source": "美国睡眠医学会 / 睡眠研究会成人 7–9 小时建议"},
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{"metric": "压力", "bands": "0–25 休息 · 26–50 偏低 · 51–75 中等 · >75 偏高",
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"source": "Garmin 官方压力分级，与手表显示一致"},
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{"metric": "身体电量", "bands": "0–25 很低 · 26–50 偏低 · 51–75 良好 · >75 充足",
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"source": "Garmin 官方 Body Battery 分级"},
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{"metric": "血氧", "bands": "<90 偏低 · 90–94 略低 · ≥95 正常",
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"source": "静息血氧饱和度常用临床参考；腕表光学测量误差较大，仅供趋势参考"},
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{"metric": "呼吸频率", "bands": "<12 偏低 · 12–20 正常 · >20 偏高",
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"source": "成人静息呼吸频率 12–20 次/分"},
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{"metric": "强度分钟", "bands": "<10 偏少 · 10–21 一般 · ≥21 达标",
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"source": "WHO 每周 150 分钟中等强度活动，折合每天约 21 分钟"},
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{"metric": "训练准备度", "bands": "0–25 很低 · 26–50 偏低 · 51–75 就绪 · >75 很好",
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"source": "Garmin 官方 Training Readiness 分级"},
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{"metric": "爬楼", "bands": "<5 偏少 · 5–10 达标 · >10 优秀",
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"source": "一般性活动量参考，无权威标准"},
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]
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@@ -8,12 +8,13 @@ import datetime
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import hashlib
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import json
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import os
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import threading
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from services import health
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from services import ai as ai_svc
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from services import coach
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from services import insights
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from services import jobs
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from services import scopes
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from db import query_all, query_one, execute
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from config import DB_TYPE
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@@ -261,19 +262,11 @@ def clear_ai_cache(user_id):
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execute("DELETE FROM ai_recommendations WHERE user_id = ?", [user_id])
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# --- AI coach: briefing, trend attribution, Copilot -------------------------
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# Same caching rationale as the recommendations above, with one addition: a
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# briefing is the first thing on the 今日 screen, so it can never wait on a
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# generation. The endpoint answers immediately from the rule engine and the
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# model's version replaces it on a later poll.
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_JOB_LOCK = threading.Lock()
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_JOBS = set()
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def _insight_key(kind, subject):
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return f"{kind}:{subject}"
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# --- AI coach: briefing, per-screen insights, attribution, Copilot ----------
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# Same caching rationale as the recommendations above, with one addition: no
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# screen can wait on a generation, so every one of them answers immediately
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# from the rule engine and queues the model's version, which replaces it on a
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# later poll. The queue lives in services/jobs.py.
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def _read_insight(user_id, kind, subject, fingerprint):
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row = query_one(
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"SELECT * FROM ai_insights WHERE user_id = ? AND kind = ? AND subject = ?",
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@@ -322,52 +315,14 @@ def _write_insight(user_id, kind, subject, fingerprint, payload, meta):
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def _context_fingerprint(context):
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"""Digest of everything the prompt will contain.
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The whole context rather than a chosen subset: a briefing is derived from
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The whole context rather than a chosen subset: an insight is derived from
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all of it, so any change to any field — a corrected sleep stage, a newly
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synced activity — should expire the cached answer.
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synced activity — should expire the stored answer.
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"""
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blob = json.dumps(context, ensure_ascii=False, sort_keys=True)
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return hashlib.sha256(blob.encode("utf-8")).hexdigest()[:64]
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def _run_in_background(key, target):
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"""Start `target` once per key; a second caller joins the first one's run.
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Guards against the obvious failure mode of a poll-until-ready endpoint:
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the client polls every few seconds while a generation takes minutes, and
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without this every poll would start another one.
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The claim is per-process, not per-deployment: with Gunicorn's two workers
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each can start one generation for the same key. That is deliberate rather
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than overlooked — the `job_locks` table would make it exclusive, but the
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cost here is a duplicate call, not a duplicate row (the cache id is derived
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from user+kind+subject, so the second write lands on the first one's row).
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A cross-process lock is worth adding only if the gateway's rate limits
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start to bite.
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"""
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with _JOB_LOCK:
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if key in _JOBS:
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return False
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_JOBS.add(key)
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def runner():
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try:
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target()
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except Exception as e: # noqa: BLE001 - a background job must not die silently
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print(f"[analysis] background job {key} failed: {e}")
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finally:
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with _JOB_LOCK:
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_JOBS.discard(key)
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threading.Thread(target=runner, name=f"ai-{key}", daemon=True).start()
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return True
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def _generating(key):
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with _JOB_LOCK:
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return key in _JOBS
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def generate_briefing(user_id, context, model=None):
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"""Ask a model for the briefing and store it. Returns (briefing, meta)."""
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completion, meta = ai_svc.complete(coach.briefing_messages(context), model)
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@@ -398,7 +353,6 @@ def get_briefing(user_id, date=None, model=None, refresh=False, wait=False):
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fingerprint = _context_fingerprint(context)
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subject = context["snapshotDate"]
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key = _insight_key("briefing", subject)
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if not refresh:
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cached = _read_insight(user_id, "briefing", subject, fingerprint)
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@@ -425,19 +379,14 @@ def get_briefing(user_id, date=None, model=None, refresh=False, wait=False):
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"meta": {"source": "rules", "reason": str(e)},
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}
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started = _run_in_background(
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key, lambda: generate_briefing(user_id, context, model)
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)
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# `pending` in the meta is what tells the client to poll again: the card it
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# is showing is the placeholder, not the final answer.
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state = jobs.enqueue(user_id, "briefing", subject, fingerprint,
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jobs.PRIORITY_INTERACTIVE)
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return {
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"briefing": coach.rule_briefing(context),
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"context": context,
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"meta": {
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"source": "rules",
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# `pending` is what tells the client to poll again: the card it is
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# showing is the placeholder, not the final answer.
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"pending": True,
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"generating": started or _generating(key),
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},
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"meta": _queued_meta(state, user_id, "briefing", subject),
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}
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@@ -510,6 +459,113 @@ def _delete_insight(user_id, kind, subject):
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)
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def generate_scope_insight(user_id, scope, subject=None):
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"""Generate and store one screen's insight. Raises on failure."""
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built = scopes.build(user_id, scope, subject)
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if not built:
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return None
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resolved, context = built
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completion, meta = ai_svc.complete(coach.scope_messages(context))
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insight = coach.parse_scope_insight(completion.text)
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_write_insight(user_id, scope, resolved, _context_fingerprint(context),
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insight, meta)
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return insight, meta
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def get_scope_insight(user_id, scope, subject=None, refresh=False):
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"""One screen's AI insight, answered immediately.
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Returns the stored model answer when it matches the current data.
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Otherwise the computed highlights are returned right away and the
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generation is queued **at interactive priority** — so opening a screen
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puts it ahead of whatever backfill is still working through.
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"""
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if scope not in scopes.SCOPES:
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raise KeyError(scope)
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built = scopes.build(user_id, scope, subject)
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if not built:
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return {"insight": None, "context": None,
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"meta": {"source": "none", "reason": "这个页面还没有可分析的数据"}}
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resolved, context = built
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fingerprint = _context_fingerprint(context)
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if refresh:
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_delete_insight(user_id, scope, resolved)
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else:
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cached = _read_insight(user_id, scope, resolved, fingerprint)
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if cached:
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insight, meta = cached
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return {"insight": insight, "context": context, "meta": meta}
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state = jobs.enqueue(user_id, scope, resolved, fingerprint,
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jobs.PRIORITY_INTERACTIVE)
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return {
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"insight": coach.rule_scope_insight(context),
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"context": context,
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"meta": _queued_meta(state, user_id, scope, resolved),
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}
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def _queued_meta(state, user_id, kind, subject, **extra):
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"""Meta for an answer that is standing in for a queued generation.
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`pending` drives the client's poll loop, so it must be False once the
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queue has given up — otherwise the screen keeps polling for minutes for an
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answer that is not coming, and shows a spinner the whole time.
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"""
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meta = {"source": "rules", "pending": state in ("pending", "running"),
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"queue": state, "subject": subject, **extra}
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if state == "failed":
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info = jobs.status_of(user_id, kind, subject) or {}
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meta["reason"] = info.get("error") or "生成失败，稍后会自动重试"
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return meta
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def prefetch_insights(user_id):
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"""Queue every screen's insight at background priority.
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|
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Called after a sync: the data has changed, so every stored answer is stale.
|
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These run for as long as they run — anything the user actually opens jumps
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the queue ahead of them.
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"""
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queued = []
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||||
for scope in scopes.PREFETCH_SCOPES:
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try:
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built = scopes.build(user_id, scope)
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except Exception as e: # noqa: BLE001 - one screen must not stop the rest
|
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print(f"[analysis] prefetch {scope} failed to build: {e}")
|
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continue
|
||||
if not built:
|
||||
continue
|
||||
resolved, context = built
|
||||
jobs.enqueue(user_id, scope, resolved, _context_fingerprint(context),
|
||||
jobs.PRIORITY_PREFETCH)
|
||||
queued.append(scope)
|
||||
|
||||
context = insights.build_context(user_id)
|
||||
if context:
|
||||
jobs.enqueue(user_id, "briefing", context["snapshotDate"],
|
||||
_context_fingerprint(context), jobs.PRIORITY_PREFETCH)
|
||||
queued.append("briefing")
|
||||
return queued
|
||||
|
||||
|
||||
def _run_job(user_id, kind, subject):
|
||||
"""What the queue worker calls. Dispatches on the job's kind."""
|
||||
if kind == "briefing":
|
||||
context = insights.build_context(user_id, subject)
|
||||
if not context:
|
||||
return
|
||||
generate_briefing(user_id, context)
|
||||
return
|
||||
generate_scope_insight(user_id, kind, subject)
|
||||
|
||||
|
||||
jobs.set_runner(_run_job)
|
||||
|
||||
|
||||
def clear_insight_cache(user_id, kind=None):
|
||||
if kind:
|
||||
execute(
|
||||
|
||||
@@ -407,3 +407,97 @@ def rule_trend_insight(window):
|
||||
"caution": "该结论由规则计算得出，未经模型归因，仅描述相关性而非因果。",
|
||||
"confidence": "low",
|
||||
}
|
||||
|
||||
|
||||
# --- per-screen insights ----------------------------------------------------
|
||||
# One prompt for every screen. The screen-specific part is entirely in the
|
||||
# context `scopes.py` builds, so a new screen needs a builder and nothing here.
|
||||
SCOPE_SCHEMA = """{
|
||||
"headline": "针对这个页面的一句话结论，不超过 45 字",
|
||||
"points": [
|
||||
{"title": "维度名，不超过 8 字", "detail": "该维度的判断与依据，引用具体数值，不超过 70 字"}
|
||||
],
|
||||
"actions": ["可执行的建议，1~3 条，每条不超过 30 字；没有值得建议的就给空数组"],
|
||||
"caution": "需要留意的风险或容易误读之处；没有就填 null",
|
||||
"confidence": "high|medium|low —— 取决于样本量与证据强度"
|
||||
}"""
|
||||
|
||||
|
||||
def scope_messages(context):
|
||||
"""Prompt for one screen's insight.
|
||||
|
||||
`highlights` goes in ahead of the raw data on purpose: they are the facts
|
||||
already computed from it, and leading with them is what stops the model
|
||||
re-deriving (and mis-deriving) numbers that are sitting right there.
|
||||
"""
|
||||
highlights = "\n".join(
|
||||
f"- {h['title']}：{h['detail']}" for h in context.get("highlights") or []
|
||||
)
|
||||
return [
|
||||
{"role": "system", "content": SYSTEM},
|
||||
{
|
||||
"role": "user",
|
||||
"content": (
|
||||
f"我正在看「{context.get('label')}」这个页面。以下是已经算好的要点：\n\n"
|
||||
f"{highlights}\n\n"
|
||||
"完整数据如下（deviations 的 z 值是相对我自身近 28 天基线的偏离，"
|
||||
"trends 是长周期走势，两者都已算好，直接引用即可）：\n\n"
|
||||
f"```json\n{_payload(context)}\n```\n\n"
|
||||
"请针对这个页面给出解读与建议，只谈这个页面涉及的内容，"
|
||||
f"严格按以下 JSON 结构输出：\n\n{SCOPE_SCHEMA}"
|
||||
),
|
||||
},
|
||||
]
|
||||
|
||||
|
||||
def parse_scope_insight(reply):
|
||||
data = ai_svc.extract_json(reply)
|
||||
if not isinstance(data, dict):
|
||||
raise ai_svc.AIError("模型未返回 JSON 对象")
|
||||
|
||||
points = []
|
||||
for item in data.get("points") or []:
|
||||
if isinstance(item, dict):
|
||||
title = _text(item.get("title"), 20)
|
||||
detail = _text(item.get("detail"), 220)
|
||||
else:
|
||||
title, detail = None, _text(item, 220)
|
||||
if detail:
|
||||
points.append({"title": title or "要点", "detail": detail})
|
||||
|
||||
actions = [_text(a, 60) for a in (data.get("actions") or []) if _text(a, 60)]
|
||||
confidence = str(data.get("confidence", "medium")).lower()
|
||||
if confidence not in ("high", "medium", "low"):
|
||||
confidence = "medium"
|
||||
|
||||
headline = _text(data.get("headline"), 140)
|
||||
# A card with no headline and no points is blank space; rejecting it lets
|
||||
# the caller fall back to the rule engine rather than render nothing.
|
||||
if not headline and not points:
|
||||
raise ai_svc.AIError("模型返回的解读没有可用内容")
|
||||
|
||||
return {
|
||||
"headline": headline,
|
||||
"points": points[:5],
|
||||
"actions": actions[:3],
|
||||
"caution": _text(data.get("caution"), 200),
|
||||
"confidence": confidence,
|
||||
}
|
||||
|
||||
|
||||
def rule_scope_insight(context):
|
||||
"""The computed highlights, rendered as-is.
|
||||
|
||||
No interpretation, and it says so: the honest fallback is the facts without
|
||||
the reading of them, not a guess at what a model would have said.
|
||||
"""
|
||||
highlights = context.get("highlights") or []
|
||||
return {
|
||||
# The first highlight leads and is then dropped from the list: showing
|
||||
# it in both places printed the same sentence twice.
|
||||
"headline": highlights[0]["detail"] if highlights else None,
|
||||
"points": [dict(h) for h in highlights[1:6]],
|
||||
"actions": [],
|
||||
"caution": "以下为直接计算结果，尚未经过模型解读。",
|
||||
"confidence": "low",
|
||||
}
|
||||
|
||||
@@ -69,6 +69,15 @@ def _flatten(day):
|
||||
return flat
|
||||
|
||||
|
||||
def flatten_days(rows):
|
||||
"""`get_summary` output as flat metric maps, with derived shares filled in.
|
||||
|
||||
Public counterpart of `_flatten` for the scope builders, which all need the
|
||||
same normalisation before they can compute anything.
|
||||
"""
|
||||
return [_flatten(r) for r in rows]
|
||||
|
||||
|
||||
# Metrics the briefing reasons about. `higher_better` drives the plain-language
|
||||
# verdict; None means the direction is not meaningful on its own (steps on a
|
||||
# rest day are not a failure).
|
||||
@@ -459,3 +468,34 @@ def window_context(user_id, metric, start, end, rows=None):
|
||||
for a in health.get_activities(user_id, start, end)[:40]
|
||||
],
|
||||
}
|
||||
|
||||
|
||||
# Reference bands, kept beside the UI's RANGES table (client/src/lib/ranges.ts).
|
||||
# Each entry says where that metric's band edges came from — the honest answer
|
||||
# for several of them is "general-population orientation figures", and it says
|
||||
# so. Lives here rather than in routes/: the 评分依据 screen serves it, but the
|
||||
# coach also reasons against it, and services must not import from routes.
|
||||
BAND_SOURCES = [
|
||||
{"metric": "步数", "bands": "<5k 偏低 · 5–8k 一般 · 8–12k 达标 · >12k 优秀",
|
||||
"source": "步数与死亡率的队列研究（约 8000 步起获益明显，12000 步后趋平）"},
|
||||
{"metric": "静息心率", "bands": "<50 很低 · 50–65 正常 · 65–75 偏高 · >75 较高",
|
||||
"source": "健康成人静息心率 60–100 bpm 为正常范围，规律运动者常低于 60"},
|
||||
{"metric": "心率变异性", "bands": "<25 偏低 · 25–40 一般 · 40–70 良好 · >70 很好",
|
||||
"source": "夜间 RMSSD 的一般人群分布；个体差异极大，趋势比绝对值更有意义"},
|
||||
{"metric": "睡眠时长", "bands": "<6h 不足 · 6–7h 偏少 · 7–9h 充足 · >9h 偏多",
|
||||
"source": "美国睡眠医学会 / 睡眠研究会成人 7–9 小时建议"},
|
||||
{"metric": "压力", "bands": "0–25 休息 · 26–50 偏低 · 51–75 中等 · >75 偏高",
|
||||
"source": "Garmin 官方压力分级，与手表显示一致"},
|
||||
{"metric": "身体电量", "bands": "0–25 很低 · 26–50 偏低 · 51–75 良好 · >75 充足",
|
||||
"source": "Garmin 官方 Body Battery 分级"},
|
||||
{"metric": "血氧", "bands": "<90 偏低 · 90–94 略低 · ≥95 正常",
|
||||
"source": "静息血氧饱和度常用临床参考；腕表光学测量误差较大，仅供趋势参考"},
|
||||
{"metric": "呼吸频率", "bands": "<12 偏低 · 12–20 正常 · >20 偏高",
|
||||
"source": "成人静息呼吸频率 12–20 次/分"},
|
||||
{"metric": "强度分钟", "bands": "<10 偏少 · 10–21 一般 · ≥21 达标",
|
||||
"source": "WHO 每周 150 分钟中等强度活动，折合每天约 21 分钟"},
|
||||
{"metric": "训练准备度", "bands": "0–25 很低 · 26–50 偏低 · 51–75 就绪 · >75 很好",
|
||||
"source": "Garmin 官方 Training Readiness 分级"},
|
||||
{"metric": "爬楼", "bands": "<5 偏少 · 5–10 达标 · >10 优秀",
|
||||
"source": "一般性活动量参考，无权威标准"},
|
||||
]
|
||||
|
||||
271
backend/services/jobs.py
Normal file
271
backend/services/jobs.py
Normal file
@@ -0,0 +1,271 @@
|
||||
"""
|
||||
The coach's producer/consumer queue.
|
||||
|
||||
Producing one insight costs 40s to several minutes against the gateway, so it
|
||||
can never happen inside a request. Screens *enqueue*; a worker thread consumes.
|
||||
|
||||
Priority is the whole point of the queue rather than a plain background thread:
|
||||
after a sync the backfill enqueues every scope at low priority, and those jobs
|
||||
may take half an hour to work through — but the moment the user opens a screen,
|
||||
that screen's job is promoted to the front and runs next. What they are looking
|
||||
at is always what the queue is working on.
|
||||
|
||||
The queue lives in the database, not in memory, because Gunicorn runs several
|
||||
workers: a job is claimed with a holder id and re-read to confirm, the same way
|
||||
`scheduler.py` claims its tick, so exactly one worker runs a given job.
|
||||
"""
|
||||
import datetime
|
||||
import hashlib
|
||||
import os
|
||||
import threading
|
||||
import time
|
||||
|
||||
from config import DB_TYPE
|
||||
from db import execute, query_one, query_all
|
||||
|
||||
# Lower runs first.
|
||||
PRIORITY_INTERACTIVE = 0 # a screen the user has open right now
|
||||
PRIORITY_PREFETCH = 10 # backfill after a sync
|
||||
|
||||
# A claim older than this is treated as abandoned: the worker holding it died
|
||||
# mid-generation, and without expiry that job would never run again.
|
||||
CLAIM_TIMEOUT_SECONDS = int(os.environ.get("AI_JOB_CLAIM_TIMEOUT") or 1800)
|
||||
|
||||
# Generations are slow, not frequent; polling this often costs nothing and
|
||||
# keeps an interactive job's wait to a couple of seconds.
|
||||
POLL_SECONDS = float(os.environ.get("AI_JOB_POLL_SECONDS") or 2)
|
||||
|
||||
MAX_ATTEMPTS = int(os.environ.get("AI_JOB_MAX_ATTEMPTS") or 3)
|
||||
|
||||
# How long a job that exhausted its attempts stays given up on before it is
|
||||
# tried again. Without this a transient upstream outage is permanent: three
|
||||
# quick failures while the gateway is unreachable would retire that screen's
|
||||
# insight until its underlying data happened to change, which for a screen the
|
||||
# user is not syncing could be days.
|
||||
FAILED_RETRY_SECONDS = int(os.environ.get("AI_JOB_RETRY_AFTER") or 1800)
|
||||
|
||||
ENABLED = (os.environ.get("AI_JOBS") or "true").lower() not in ("0", "false", "no")
|
||||
|
||||
_started = False
|
||||
_start_lock = threading.Lock()
|
||||
|
||||
# Set by analysis.py at import time. Injected rather than imported so this
|
||||
# module stays free of the feature logic it schedules — and so the two do not
|
||||
# import each other in a cycle.
|
||||
_runner = None
|
||||
|
||||
|
||||
def set_runner(fn):
|
||||
"""Register `fn(user_id, kind, subject) -> None`, called for each job."""
|
||||
global _runner
|
||||
_runner = fn
|
||||
|
||||
|
||||
def _now():
|
||||
return datetime.datetime.utcnow()
|
||||
|
||||
|
||||
def _iso(dt):
|
||||
return dt.isoformat(timespec="seconds")
|
||||
|
||||
|
||||
def _parse(value):
|
||||
if not value:
|
||||
return None
|
||||
try:
|
||||
return datetime.datetime.fromisoformat(str(value).replace(" ", "T"))
|
||||
except ValueError:
|
||||
return None
|
||||
|
||||
|
||||
def job_id(user_id, kind, subject):
|
||||
return hashlib.sha256(
|
||||
f"{user_id}|{kind}|{subject}".encode("utf-8")
|
||||
).hexdigest()[:64]
|
||||
|
||||
|
||||
def enqueue(user_id, kind, subject, fingerprint=None,
|
||||
priority=PRIORITY_PREFETCH):
|
||||
"""Queue one generation, or promote it if it is already queued.
|
||||
|
||||
Returns the job's current status. Idempotent by design: the screen polls
|
||||
every few seconds while it waits, and every one of those polls calls this.
|
||||
|
||||
A finished job is re-queued only when the data it was derived from has
|
||||
changed — that is what `fingerprint` is for, and it is why a poll on
|
||||
unchanged data does not restart the work that just completed.
|
||||
"""
|
||||
jid = job_id(user_id, kind, subject)
|
||||
now = _iso(_now())
|
||||
row = query_one("SELECT * FROM ai_jobs WHERE id = ?", [jid])
|
||||
|
||||
if row:
|
||||
if row["status"] == "running":
|
||||
claimed = _parse(row.get("claimed_at"))
|
||||
if claimed and (_now() - claimed).total_seconds() < CLAIM_TIMEOUT_SECONDS:
|
||||
# Already being generated. Promoting it now would not make the
|
||||
# in-flight call any faster.
|
||||
return "running"
|
||||
|
||||
stale = fingerprint and row.get("fingerprint") != fingerprint
|
||||
if row["status"] == "done" and not stale:
|
||||
return "done"
|
||||
if row["status"] == "failed" and row["attempts"] >= MAX_ATTEMPTS and not stale:
|
||||
gave_up = _parse(row.get("updated_at"))
|
||||
if gave_up and (_now() - gave_up).total_seconds() < FAILED_RETRY_SECONDS:
|
||||
return "failed"
|
||||
# Past the cooldown: reset the attempt count so the outage that
|
||||
# exhausted it does not count against the retry.
|
||||
execute(
|
||||
"UPDATE ai_jobs SET status = 'pending', attempts = 0, error = NULL, "
|
||||
"holder = NULL, claimed_at = NULL, priority = ?, updated_at = ? "
|
||||
"WHERE id = ?",
|
||||
[min(priority, row["priority"]), now, jid],
|
||||
)
|
||||
return "pending"
|
||||
|
||||
# Promote (never demote): a screen the user just opened must not be
|
||||
# pushed back by the prefetch entry that was already sitting there.
|
||||
execute(
|
||||
"UPDATE ai_jobs SET status = 'pending', priority = ?, "
|
||||
"fingerprint = ?, holder = NULL, claimed_at = NULL, "
|
||||
"attempts = ?, updated_at = ? WHERE id = ?",
|
||||
[
|
||||
min(priority, row["priority"]),
|
||||
fingerprint or row.get("fingerprint"),
|
||||
0 if stale else row["attempts"],
|
||||
now, jid,
|
||||
],
|
||||
)
|
||||
return "pending"
|
||||
|
||||
cols = ["id", "user_id", "kind", "subject", "fingerprint", "priority",
|
||||
"status", "attempts", "created_at", "updated_at"]
|
||||
execute(
|
||||
f"INSERT INTO ai_jobs ({', '.join(cols)}) "
|
||||
f"VALUES ({', '.join(['?'] * len(cols))})",
|
||||
[jid, user_id, kind, subject, fingerprint, priority, "pending", 0,
|
||||
now, now],
|
||||
)
|
||||
return "pending"
|
||||
|
||||
|
||||
def status_of(user_id, kind, subject):
|
||||
row = query_one("SELECT * FROM ai_jobs WHERE id = ?",
|
||||
[job_id(user_id, kind, subject)])
|
||||
if not row:
|
||||
return None
|
||||
return {
|
||||
"status": row["status"],
|
||||
"priority": row["priority"],
|
||||
"attempts": row["attempts"],
|
||||
"error": row.get("error"),
|
||||
"updatedAt": row.get("updated_at"),
|
||||
}
|
||||
|
||||
|
||||
def pending_count(user_id=None):
|
||||
sql = "SELECT COUNT(*) AS n FROM ai_jobs WHERE status IN ('pending', 'running')"
|
||||
params = []
|
||||
if user_id:
|
||||
sql += " AND user_id = ?"
|
||||
params.append(user_id)
|
||||
row = query_one(sql, params)
|
||||
return (row or {}).get("n") or 0
|
||||
|
||||
|
||||
def _claim_next():
|
||||
"""Take the highest-priority runnable job, or None.
|
||||
|
||||
Ordered by priority then age so the interactive job wins and, among equals,
|
||||
the one that has waited longest goes first.
|
||||
"""
|
||||
cutoff = _iso(_now() - datetime.timedelta(seconds=CLAIM_TIMEOUT_SECONDS))
|
||||
rows = query_all(
|
||||
"SELECT * FROM ai_jobs WHERE status = 'pending' "
|
||||
"OR (status = 'running' AND (claimed_at IS NULL OR claimed_at < ?)) "
|
||||
"ORDER BY priority ASC, created_at ASC",
|
||||
[cutoff],
|
||||
)
|
||||
holder = f"{os.getpid()}-{threading.get_ident()}"
|
||||
|
||||
for row in rows:
|
||||
if row["attempts"] >= MAX_ATTEMPTS:
|
||||
continue
|
||||
execute(
|
||||
"UPDATE ai_jobs SET status = 'running', holder = ?, claimed_at = ?, "
|
||||
"attempts = ?, updated_at = ? WHERE id = ? AND status = ?",
|
||||
[holder, _iso(_now()), row["attempts"] + 1, _iso(_now()),
|
||||
row["id"], row["status"]],
|
||||
)
|
||||
# Re-read: another worker may have claimed it between the SELECT and
|
||||
# the UPDATE, in which case its holder is the one now recorded.
|
||||
check = query_one("SELECT holder FROM ai_jobs WHERE id = ?", [row["id"]])
|
||||
if check and check.get("holder") == holder:
|
||||
return row
|
||||
return None
|
||||
|
||||
|
||||
def _finish(jid, error=None):
|
||||
execute(
|
||||
"UPDATE ai_jobs SET status = ?, error = ?, holder = NULL, "
|
||||
"claimed_at = NULL, updated_at = ? WHERE id = ?",
|
||||
["failed" if error else "done", (error or "")[:500] if error else None,
|
||||
_iso(_now()), jid],
|
||||
)
|
||||
|
||||
|
||||
def run_once():
|
||||
"""Claim and run one job. Returns True when something was run."""
|
||||
if _runner is None:
|
||||
return False
|
||||
row = _claim_next()
|
||||
if not row:
|
||||
return False
|
||||
try:
|
||||
_runner(row["user_id"], row["kind"], row["subject"])
|
||||
except Exception as e: # noqa: BLE001 - one bad job must not stop the queue
|
||||
_finish(row["id"], f"{type(e).__name__}: {e}")
|
||||
print(f"[ai-jobs] {row['kind']}:{row['subject']} failed: {e}")
|
||||
return True
|
||||
_finish(row["id"])
|
||||
return True
|
||||
|
||||
|
||||
def _loop():
|
||||
while True:
|
||||
try:
|
||||
# Straight on to the next job when one was just run: after a
|
||||
# sync there is a whole backfill waiting, and sleeping between
|
||||
# each would add hours to it for no reason.
|
||||
if not run_once():
|
||||
time.sleep(POLL_SECONDS)
|
||||
except Exception as e: # noqa: BLE001 - the loop must outlive any failure
|
||||
print(f"[ai-jobs] worker error: {e}")
|
||||
time.sleep(POLL_SECONDS)
|
||||
|
||||
|
||||
def start():
|
||||
"""Start one consumer per process."""
|
||||
global _started
|
||||
if not ENABLED:
|
||||
print("[ai-jobs] disabled by AI_JOBS")
|
||||
return
|
||||
with _start_lock:
|
||||
if _started:
|
||||
return
|
||||
_started = True
|
||||
threading.Thread(target=_loop, daemon=True, name="ai-jobs").start()
|
||||
print("[ai-jobs] worker started")
|
||||
|
||||
|
||||
def reset_stale_claims():
|
||||
"""Release jobs a previous process was running when it stopped.
|
||||
|
||||
Without this they sit in `running` until the claim expires, which for the
|
||||
screen waiting on one looks exactly like a generation that never finishes.
|
||||
"""
|
||||
execute(
|
||||
"UPDATE ai_jobs SET status = 'pending', holder = NULL, claimed_at = NULL "
|
||||
"WHERE status = 'running'"
|
||||
)
|
||||
@@ -22,6 +22,7 @@ import time
|
||||
|
||||
from config import DB_TYPE
|
||||
from db import execute, query_one, query_all
|
||||
from services import analysis as analysis_svc
|
||||
from services import garmin as garmin_svc
|
||||
from services import settings as settings_svc
|
||||
|
||||
@@ -163,6 +164,14 @@ def sync_all_accounts(days=None, respect_schedule=False):
|
||||
out = garmin_svc.sync_data(uid, {}, days=d)
|
||||
results.append({"user": uid, "status": out.get("status"),
|
||||
"records": out.get("recordsSynced")})
|
||||
# New data invalidates every stored insight, so queue them all at
|
||||
# background priority. They may take a while; anything the user
|
||||
# opens meanwhile jumps ahead of them.
|
||||
if out.get("recordsSynced"):
|
||||
try:
|
||||
analysis_svc.prefetch_insights(uid)
|
||||
except Exception as e: # noqa: BLE001 - never fail a sync over this
|
||||
print(f"[scheduler] prefetch failed for {uid}: {e}")
|
||||
except Exception as e: # noqa: BLE001 - one account must not stop the rest
|
||||
results.append({"user": uid, "status": "error", "error": str(e)[:200]})
|
||||
return results
|
||||
|
||||
601
backend/services/scopes.py
Normal file
601
backend/services/scopes.py
Normal file
@@ -0,0 +1,601 @@
|
||||
"""
|
||||
Per-screen contexts for the AI coach.
|
||||
|
||||
One builder per screen, all returning the same envelope, so the queue, the
|
||||
prompt, the cache and the UI component stay generic — adding a screen means
|
||||
adding an entry here, not another endpoint and another card.
|
||||
|
||||
Every builder returns `(subject, context)`:
|
||||
|
||||
* `subject` identifies *what* the insight is about (a date, an activity id, a
|
||||
window). Together with the scope name it keys the cache and the job queue,
|
||||
so 睡眠 for last week and 睡眠 for today are separate entries rather than one
|
||||
overwriting the other.
|
||||
* `context["highlights"]` is the plain-language facts, already computed. The
|
||||
model interprets them; the rule engine renders them verbatim when no model
|
||||
answers. Both versions therefore quote the same numbers, which is what keeps
|
||||
a fallback from reading like a different app.
|
||||
|
||||
Builders return None when the screen has nothing to say about — no nights
|
||||
recorded, no scale readings — so the caller can stay quiet instead of asking a
|
||||
model to comment on an empty table.
|
||||
"""
|
||||
import datetime
|
||||
import statistics
|
||||
|
||||
from services import garmin as garmin_svc
|
||||
from services import garmin_extras as extras
|
||||
from services import fitness_age
|
||||
from services import health
|
||||
from services import insights
|
||||
from services import settings as settings_svc
|
||||
|
||||
# The same reference bands the 评分依据 screen shows. Passed to the model so it
|
||||
# cannot call a number 偏高 that the card beside it labels 正常.
|
||||
from services.insights import BAND_SOURCES
|
||||
|
||||
|
||||
def _round(value, digits=2):
|
||||
return None if value is None else round(float(value), digits)
|
||||
|
||||
|
||||
def _mean(values):
|
||||
values = [v for v in values if v is not None]
|
||||
return round(statistics.fmean(values), 2) if values else None
|
||||
|
||||
|
||||
def _recent(rows, days):
|
||||
"""The last `days` calendar days of flattened rows, relative to the newest."""
|
||||
if not rows:
|
||||
return []
|
||||
end = datetime.date.fromisoformat(rows[-1]["date"])
|
||||
cutoff = (end - datetime.timedelta(days=days - 1)).isoformat()
|
||||
return [r for r in rows if r["date"] >= cutoff]
|
||||
|
||||
|
||||
def _summary(user_id):
|
||||
return insights.flatten_days(health.get_summary(user_id))
|
||||
|
||||
|
||||
def _deviations_for(rows, metrics):
|
||||
"""Today's departures, narrowed to the metrics this screen is about."""
|
||||
if not rows:
|
||||
return []
|
||||
picked = set(metrics)
|
||||
return [
|
||||
d for d in insights.deviations(rows, rows[-1])
|
||||
if d["metric"] in picked
|
||||
]
|
||||
|
||||
|
||||
def _trends_for(rows, metrics):
|
||||
picked = set(metrics)
|
||||
return [t for t in insights.trends(rows) if t["metric"] in picked]
|
||||
|
||||
|
||||
# --- 健康 --------------------------------------------------------------------
|
||||
def build_health(user_id, subject=None):
|
||||
rows = _summary(user_id)
|
||||
if not rows:
|
||||
return None
|
||||
today = rows[-1]
|
||||
|
||||
deviations = insights.deviations(rows, today)
|
||||
notable = [d for d in deviations
|
||||
if d["z"] is not None and abs(d["z"]) >= insights.Z_NOTABLE]
|
||||
|
||||
highlights = [{
|
||||
"title": d["label"],
|
||||
# The label is repeated inside the sentence because the first
|
||||
# highlight is also used as a standalone headline, where the title
|
||||
# beside it is not shown.
|
||||
"detail": (
|
||||
f"{d['label']} {d['value']}{d['unit']}，近 {d['baselineDays']} 天基线 "
|
||||
f"{d['baselineMean']}{d['unit']}，偏离 {abs(d['z']):.1f} 个标准差。"
|
||||
),
|
||||
} for d in notable[:5]]
|
||||
if not highlights:
|
||||
highlights = [{"title": "整体", "detail": "今日各项指标均在个人基线的正常波动范围内。"}]
|
||||
|
||||
return today["date"], {
|
||||
"scope": "health",
|
||||
"label": "健康总览",
|
||||
"snapshotDate": today["date"],
|
||||
"highlights": highlights,
|
||||
"deviations": deviations,
|
||||
"trends": insights.trends(rows)[:8],
|
||||
"referenceBands": BAND_SOURCES,
|
||||
"profile": _profile(user_id, rows),
|
||||
}
|
||||
|
||||
|
||||
def _profile(user_id, rows):
|
||||
raw = settings_svc.get_raw(user_id)
|
||||
age = settings_svc.age_from(raw["birth_date"])
|
||||
return {
|
||||
"age": age,
|
||||
"sex": raw["sex"],
|
||||
"bmi": settings_svc.bmi_from(raw["height_cm"], raw["weight_kg"]),
|
||||
"vo2max": insights.latest_of(rows, "vo2max"),
|
||||
"enduranceScore": insights.latest_of(rows, "enduranceScore"),
|
||||
}
|
||||
|
||||
|
||||
# --- 睡眠 --------------------------------------------------------------------
|
||||
SLEEP_METRICS = ("sleepDuration", "sleepQuality", "sleepDeepPct", "sleepRemPct")
|
||||
NIGHTS = 14
|
||||
|
||||
|
||||
def build_sleep(user_id, subject=None):
|
||||
rows = _summary(user_id)
|
||||
nights = [r for r in rows if r.get("sleepDuration") is not None]
|
||||
if not nights:
|
||||
return None
|
||||
|
||||
window = _recent(nights, NIGHTS)
|
||||
durations = [n["sleepDuration"] for n in window]
|
||||
avg = _mean(durations)
|
||||
debt = round(sum(insights.SLEEP_TARGET_HOURS - d for d in durations
|
||||
if d < insights.SLEEP_TARGET_HOURS), 1)
|
||||
|
||||
deep = _mean([n.get("sleepDeepPct") for n in window])
|
||||
rem = _mean([n.get("sleepRemPct") for n in window])
|
||||
rem_low, rem_high = insights.REM_REFERENCE_PCT
|
||||
deep_low, _ = insights.DEEP_REFERENCE_PCT
|
||||
|
||||
highlights = [{
|
||||
"title": f"近 {len(window)} 晚",
|
||||
"detail": (
|
||||
f"平均 {avg} 小时（目标 {insights.SLEEP_TARGET_HOURS:g}），"
|
||||
f"累计缺口 {debt} 小时，平均评分 "
|
||||
f"{_mean([n.get('sleepQuality') for n in window])}。"
|
||||
),
|
||||
}]
|
||||
if rem is not None:
|
||||
highlights.append({
|
||||
"title": "REM 占比",
|
||||
"detail": f"平均 {rem}%（参考 {rem_low:g}~{rem_high:g}%）"
|
||||
+ ("，偏低。" if rem < rem_low else "。"),
|
||||
})
|
||||
if deep is not None:
|
||||
highlights.append({
|
||||
"title": "深睡占比",
|
||||
"detail": f"平均 {deep}%（参考 ≥{deep_low:g}%）"
|
||||
+ ("，偏低。" if deep < deep_low else "，达标。"),
|
||||
})
|
||||
|
||||
return f"{window[-1]['date']}:{len(window)}", {
|
||||
"scope": "sleep",
|
||||
"label": "睡眠",
|
||||
"windowNights": len(window),
|
||||
"highlights": highlights,
|
||||
"nights": [{
|
||||
"date": n["date"],
|
||||
"hours": _round(n.get("sleepDuration")),
|
||||
"score": n.get("sleepQuality"),
|
||||
"deepPct": n.get("sleepDeepPct"),
|
||||
"remPct": n.get("sleepRemPct"),
|
||||
"awakePct": n.get("sleepAwakePct"),
|
||||
"sleepHr": n.get("sleepRespirationAvg"),
|
||||
"sleepStress": n.get("sleepStressAvg"),
|
||||
"spo2": n.get("sleepSpo2Avg"),
|
||||
} for n in window],
|
||||
"averages": {
|
||||
"hours": avg, "debtHours": debt, "deepPct": deep, "remPct": rem,
|
||||
"targetHours": insights.SLEEP_TARGET_HOURS,
|
||||
},
|
||||
"deviations": _deviations_for(rows, SLEEP_METRICS),
|
||||
"trends": _trends_for(rows, SLEEP_METRICS),
|
||||
# The night's autonomic readings are what tell recovery apart from
|
||||
# merely lying down for eight hours.
|
||||
"autonomic": {
|
||||
"restingHr": rows[-1].get("heartRate"),
|
||||
"hrv": rows[-1].get("heartRateVariability"),
|
||||
},
|
||||
}
|
||||
|
||||
|
||||
# --- 运动 --------------------------------------------------------------------
|
||||
EXERCISE_METRICS = ("intensityMinutes", "steps", "trainingReadiness",
|
||||
"enduranceScore", "vo2max")
|
||||
EXERCISE_DAYS = 30
|
||||
|
||||
|
||||
def build_exercise(user_id, subject=None):
|
||||
rows = _summary(user_id)
|
||||
if not rows:
|
||||
return None
|
||||
end = rows[-1]["date"]
|
||||
start = (datetime.date.fromisoformat(end)
|
||||
- datetime.timedelta(days=EXERCISE_DAYS - 1)).isoformat()
|
||||
activities = health.get_activities(user_id, start, end)
|
||||
|
||||
by_sport = {}
|
||||
for a in activities:
|
||||
sport = a.get("activity_type") or "其他"
|
||||
entry = by_sport.setdefault(sport, {"sessions": 0, "minutes": 0, "calories": 0})
|
||||
entry["sessions"] += 1
|
||||
entry["minutes"] += round((a.get("duration") or 0) / 60)
|
||||
entry["calories"] += a.get("calories") or 0
|
||||
|
||||
window = _recent(rows, EXERCISE_DAYS)
|
||||
intensity = [r.get("intensityMinutes") for r in window]
|
||||
weekly = round((sum(v for v in intensity if v) / max(len(window), 1)) * 7)
|
||||
|
||||
highlights = [{
|
||||
"title": f"近 {EXERCISE_DAYS} 天",
|
||||
"detail": (
|
||||
f"共 {len(activities)} 次运动，合计 "
|
||||
f"{sum(e['minutes'] for e in by_sport.values())} 分钟；"
|
||||
f"强度分钟周均约 {weekly}（WHO 建议 150）。"
|
||||
),
|
||||
}]
|
||||
if by_sport:
|
||||
top = max(by_sport.items(), key=lambda kv: kv[1]["minutes"])
|
||||
highlights.append({
|
||||
"title": "主要项目",
|
||||
"detail": f"{top[0]}，{top[1]['sessions']} 次 / {top[1]['minutes']} 分钟。",
|
||||
})
|
||||
readiness = insights.latest_of(rows, "trainingReadiness", within=7)
|
||||
if readiness is not None:
|
||||
highlights.append({"title": "训练准备度", "detail": f"最近一次 {readiness:g}/100。"})
|
||||
|
||||
return f"{end}:{EXERCISE_DAYS}", {
|
||||
"scope": "exercise",
|
||||
"label": "运动",
|
||||
"windowDays": EXERCISE_DAYS,
|
||||
"highlights": highlights,
|
||||
"bySport": by_sport,
|
||||
"weeklyIntensityMinutes": weekly,
|
||||
"sessions": [{
|
||||
"date": a.get("start_time"),
|
||||
"sport": a.get("activity_type"),
|
||||
"durationMin": round((a.get("duration") or 0) / 60) or None,
|
||||
"distanceKm": _round((a["distance"] / 1000) if a.get("distance") else None),
|
||||
"calories": a.get("calories"),
|
||||
"avgHr": a.get("heart_rate_average"),
|
||||
"maxHr": a.get("heart_rate_max"),
|
||||
} for a in activities[-30:]],
|
||||
"deviations": _deviations_for(rows, EXERCISE_METRICS),
|
||||
"trends": _trends_for(rows, EXERCISE_METRICS),
|
||||
"activityShift": insights.activity_shift(rows),
|
||||
"personalRecords": health.get_personal_records(user_id)[:10],
|
||||
"profile": _profile(user_id, rows),
|
||||
}
|
||||
|
||||
|
||||
# --- 趋势 --------------------------------------------------------------------
|
||||
def build_trends(user_id, subject=None):
|
||||
rows = _summary(user_id)
|
||||
if len(rows) < 20:
|
||||
return None
|
||||
|
||||
trends = insights.trends(rows)
|
||||
moved = sorted(
|
||||
[t for t in trends if t.get("direction")],
|
||||
key=lambda t: abs(t["slopePer30d"] or 0), reverse=True,
|
||||
)
|
||||
highlights = [{
|
||||
"title": t["label"],
|
||||
"detail": (
|
||||
f"{t['days']} 天内由 {t['firstMean']}{t['unit']} 到 "
|
||||
f"{t['lastMean']}{t['unit']}（{t['direction']}），"
|
||||
f"斜率约 {t['slopePer30d']}{t['unit']}/30 天。"
|
||||
),
|
||||
} for t in moved[:5]]
|
||||
if not highlights:
|
||||
highlights = [{"title": "整体", "detail": "各项指标长期走势平稳，无明显方向性变化。"}]
|
||||
|
||||
return f"{rows[-1]['date']}:{len(rows)}", {
|
||||
"scope": "trends",
|
||||
"label": "长期趋势",
|
||||
"highlights": highlights,
|
||||
"trends": trends,
|
||||
"activityShift": insights.activity_shift(rows),
|
||||
"coverage": {
|
||||
"days": len(rows), "first": rows[0]["date"], "last": rows[-1]["date"],
|
||||
},
|
||||
"profile": _profile(user_id, rows),
|
||||
}
|
||||
|
||||
|
||||
# --- 每日 --------------------------------------------------------------------
|
||||
def build_daily(user_id, subject=None):
|
||||
"""One calendar day, including the within-day curves the screen plots."""
|
||||
rows = _summary(user_id)
|
||||
if not rows:
|
||||
return None
|
||||
date = subject or rows[-1]["date"]
|
||||
match = [r for r in rows if r["date"] == date]
|
||||
if not match:
|
||||
return None
|
||||
day = match[0]
|
||||
history = [r for r in rows if r["date"] <= date]
|
||||
|
||||
series = extras.get_daily_series(user_id, date) or {}
|
||||
curves = {}
|
||||
for kind, points in series.items():
|
||||
values = [p[1] for p in points if isinstance(p, (list, tuple)) and p[1] is not None]
|
||||
if values:
|
||||
# The curve itself is thousands of points; its shape is what the
|
||||
# reader can see on the chart, so only the summary is sent.
|
||||
curves[kind] = {"min": min(values), "max": max(values),
|
||||
"mean": _mean(values), "samples": len(values)}
|
||||
|
||||
deviations = insights.deviations(history, day)
|
||||
notable = [d for d in deviations
|
||||
if d["z"] is not None and abs(d["z"]) >= insights.Z_NOTABLE]
|
||||
highlights = [{
|
||||
"title": d["label"],
|
||||
"detail": f"{d['label']} {d['value']}{d['unit']}，偏离基线 "
|
||||
f"{abs(d['z']):.1f} 个标准差。",
|
||||
} for d in notable[:4]] or [
|
||||
{"title": date, "detail": "这一天各项指标都在常态范围内。"}
|
||||
]
|
||||
|
||||
return date, {
|
||||
"scope": "daily",
|
||||
"label": f"{date} 当日",
|
||||
"date": date,
|
||||
"highlights": highlights,
|
||||
"metrics": {k: v for k, v in day.items() if k != "date"},
|
||||
"curves": curves,
|
||||
"activities": [{
|
||||
"sport": a.get("activity_type"),
|
||||
"durationMin": round((a.get("duration") or 0) / 60) or None,
|
||||
"calories": a.get("calories"),
|
||||
"avgHr": a.get("heart_rate_average"),
|
||||
} for a in health.get_activities(user_id, date, date)],
|
||||
"deviations": deviations,
|
||||
}
|
||||
|
||||
|
||||
# --- 身体成分 ----------------------------------------------------------------
|
||||
def build_body(user_id, subject=None):
|
||||
series = extras.get_body_composition(user_id)
|
||||
pressure = extras.get_blood_pressure(user_id)
|
||||
if not series and not pressure:
|
||||
return None
|
||||
|
||||
highlights = []
|
||||
if len(series) >= 2:
|
||||
first, last = series[0], series[-1]
|
||||
for key, label, unit in (("weightKg", "体重", "kg"),
|
||||
("bodyFatPct", "体脂率", "%"),
|
||||
("muscleMassKg", "肌肉量", "kg")):
|
||||
a, b = first.get(key), last.get(key)
|
||||
if a is None or b is None:
|
||||
continue
|
||||
highlights.append({
|
||||
"title": label,
|
||||
"detail": f"{first['date']} {a}{unit} → {last['date']} {b}{unit}"
|
||||
f"（{'+' if b >= a else ''}{round(b - a, 1)}{unit}）。",
|
||||
})
|
||||
elif series:
|
||||
last = series[-1]
|
||||
highlights.append({
|
||||
"title": "最近一次测量",
|
||||
"detail": f"{last['date']}：体重 {last.get('weightKg')} kg，"
|
||||
f"体脂 {last.get('bodyFatPct')}%。",
|
||||
})
|
||||
if pressure:
|
||||
p = pressure[0]
|
||||
highlights.append({
|
||||
"title": "血压",
|
||||
"detail": f"最近一次 {p['systolic']}/{p['diastolic']} mmHg"
|
||||
f"（{str(p['measuredAt'])[:16]}）。",
|
||||
})
|
||||
|
||||
subject_key = (series[-1]["date"] if series
|
||||
else str(pressure[0]["measuredAt"])[:10])
|
||||
return subject_key, {
|
||||
"scope": "body",
|
||||
"label": "身体成分",
|
||||
"highlights": highlights,
|
||||
"composition": series[-60:],
|
||||
"bloodPressure": pressure[:20],
|
||||
"profile": _profile(user_id, _summary(user_id)),
|
||||
}
|
||||
|
||||
|
||||
# --- 成绩预测 ----------------------------------------------------------------
|
||||
def build_race(user_id, subject=None):
|
||||
predictions = extras.get_race_predictions(user_id)
|
||||
if not predictions:
|
||||
return None
|
||||
rows = _summary(user_id)
|
||||
latest = predictions[-1]
|
||||
|
||||
def mmss(seconds):
|
||||
if not seconds:
|
||||
return None
|
||||
return f"{int(seconds) // 60}:{int(seconds) % 60:02d}"
|
||||
|
||||
highlights = [{
|
||||
"title": "当前预测",
|
||||
"detail": " · ".join(
|
||||
f"{label} {mmss(latest.get(key))}"
|
||||
for key, label in (("time5k", "5K"), ("time10k", "10K"),
|
||||
("timeHalf", "半马"), ("timeMarathon", "全马"))
|
||||
if latest.get(key)
|
||||
) + f"（{latest['date']}）。",
|
||||
}]
|
||||
if len(predictions) >= 2:
|
||||
first = predictions[0]
|
||||
a, b = first.get("time5k"), latest.get("time5k")
|
||||
if a and b:
|
||||
delta = int(a - b)
|
||||
highlights.append({
|
||||
"title": "5K 变化",
|
||||
"detail": f"自 {first['date']} 起{'快了' if delta > 0 else '慢了'} "
|
||||
f"{abs(delta)} 秒。",
|
||||
})
|
||||
|
||||
return latest["date"], {
|
||||
"scope": "race",
|
||||
"label": "成绩预测",
|
||||
"highlights": highlights,
|
||||
"predictions": predictions[-40:],
|
||||
"trends": _trends_for(rows, ("vo2max", "enduranceScore", "heartRate")),
|
||||
"profile": _profile(user_id, rows),
|
||||
}
|
||||
|
||||
|
||||
# --- 身体年龄 ----------------------------------------------------------------
|
||||
def build_body_age(user_id, subject=None):
|
||||
rows = _summary(user_id)
|
||||
if not rows:
|
||||
return None
|
||||
raw = settings_svc.get_raw(user_id)
|
||||
age = settings_svc.age_from(raw["birth_date"])
|
||||
estimate = fitness_age.estimate(
|
||||
age=age, sex=raw["sex"], vo2max=insights.latest_of(rows, "vo2max"),
|
||||
resting_hr=insights.latest_of(rows, "heartRate", within=30),
|
||||
bmi=settings_svc.bmi_from(raw["height_cm"], raw["weight_kg"]),
|
||||
)
|
||||
if not estimate or estimate.get("value") is None:
|
||||
return None
|
||||
|
||||
highlights = [{
|
||||
"title": "身体年龄",
|
||||
"detail": f"{estimate['value']} 岁，实际 {estimate['chronologicalAge']} 岁"
|
||||
f"（{'+' if estimate['delta'] >= 0 else ''}{estimate['delta']} 年）。",
|
||||
}] + [{
|
||||
"title": step["label"],
|
||||
"detail": f"输入 {step['input']}，贡献 {step['years']} 年。",
|
||||
} for step in estimate.get("steps", [])]
|
||||
|
||||
return rows[-1]["date"], {
|
||||
"scope": "bodyAge",
|
||||
"label": "身体年龄",
|
||||
"highlights": highlights,
|
||||
"estimate": {k: v for k, v in estimate.items() if k != "basis"},
|
||||
"trends": _trends_for(rows, ("vo2max", "heartRate", "enduranceScore")),
|
||||
"profile": _profile(user_id, rows),
|
||||
}
|
||||
|
||||
|
||||
# --- 挑战赛 ------------------------------------------------------------------
|
||||
def build_challenges(user_id, subject=None):
|
||||
rows = extras.get_challenges(user_id)
|
||||
if not rows:
|
||||
return None
|
||||
active = [c for c in rows if (c.get("status") or "").lower() in
|
||||
("active", "in_progress", "inprogress")]
|
||||
highlights = [{
|
||||
"title": "进行中",
|
||||
"detail": f"共 {len(active)} 项进行中，历史累计 {len(rows)} 项。",
|
||||
}]
|
||||
for c in active[:3]:
|
||||
highlights.append({
|
||||
"title": c.get("name") or "挑战",
|
||||
"detail": f"{c.get('startDate')} ~ {c.get('endDate')}，状态 {c.get('status')}。",
|
||||
})
|
||||
|
||||
return f"{rows[0].get('startDate')}:{len(rows)}", {
|
||||
"scope": "challenges",
|
||||
"label": "挑战赛",
|
||||
"highlights": highlights,
|
||||
"challenges": [{k: v for k, v in c.items() if k != "payload"}
|
||||
for c in rows[:40]],
|
||||
"activityShift": insights.activity_shift(_summary(user_id)),
|
||||
}
|
||||
|
||||
|
||||
# --- 运动详情 ----------------------------------------------------------------
|
||||
def build_activity(user_id, subject=None):
|
||||
"""One session. `subject` is the activity id."""
|
||||
if not subject:
|
||||
return None
|
||||
detail = garmin_svc.read_activity_detail(user_id, subject)
|
||||
listed = [a for a in health.get_activities(user_id) if str(a.get("id")) == str(subject)]
|
||||
if not detail and not listed:
|
||||
return None
|
||||
|
||||
summary = (detail or {}).get("summary") or {}
|
||||
base = listed[0] if listed else {}
|
||||
zones = (detail or {}).get("hrZones") or []
|
||||
total_zone = sum(z.get("seconds") or 0 for z in zones)
|
||||
|
||||
minutes = round((base.get("duration") or summary.get("duration") or 0) / 60)
|
||||
highlights = [{
|
||||
"title": base.get("activity_type") or (detail or {}).get("activityType") or "运动",
|
||||
"detail": f"{minutes} 分钟"
|
||||
+ (f"，{round((base.get('distance') or 0) / 1000, 2)} km"
|
||||
if base.get("distance") else "")
|
||||
+ (f"，平均心率 {base.get('heart_rate_average')}"
|
||||
if base.get("heart_rate_average") else "")
|
||||
+ "。",
|
||||
}]
|
||||
if total_zone:
|
||||
share = " · ".join(
|
||||
f"Z{z['zone']} {round((z.get('seconds') or 0) / total_zone * 100)}%"
|
||||
for z in zones if z.get("seconds")
|
||||
)
|
||||
highlights.append({"title": "心率区间分布", "detail": share + "。"})
|
||||
|
||||
return str(subject), {
|
||||
"scope": "activity",
|
||||
"label": "运动详情",
|
||||
"activityId": str(subject),
|
||||
"highlights": highlights,
|
||||
"session": {
|
||||
"sport": base.get("activity_type"),
|
||||
"startTime": base.get("start_time"),
|
||||
"durationMin": minutes or None,
|
||||
"distanceKm": _round((base["distance"] / 1000) if base.get("distance") else None),
|
||||
"calories": base.get("calories"),
|
||||
"avgHr": base.get("heart_rate_average"),
|
||||
"maxHr": base.get("heart_rate_max"),
|
||||
},
|
||||
"hrZones": zones,
|
||||
# The full summary carries dozens of sport-specific fields; only the
|
||||
# numeric ones are useful to reason over and they are already small.
|
||||
"detailSummary": {k: v for k, v in summary.items()
|
||||
if isinstance(v, (int, float))},
|
||||
"laps": ((detail or {}).get("laps") or [])[:20],
|
||||
}
|
||||
|
||||
|
||||
# --- registry ----------------------------------------------------------------
|
||||
class Scope:
|
||||
__slots__ = ("name", "label", "build", "needs_subject")
|
||||
|
||||
def __init__(self, name, label, build, needs_subject=False):
|
||||
self.name = name
|
||||
self.label = label
|
||||
self.build = build
|
||||
self.needs_subject = needs_subject
|
||||
|
||||
|
||||
SCOPES = {
|
||||
s.name: s for s in (
|
||||
Scope("health", "健康总览", build_health),
|
||||
Scope("sleep", "睡眠", build_sleep),
|
||||
Scope("exercise", "运动", build_exercise),
|
||||
Scope("trends", "长期趋势", build_trends),
|
||||
Scope("daily", "每日数据", build_daily),
|
||||
Scope("body", "身体成分", build_body),
|
||||
Scope("race", "成绩预测", build_race),
|
||||
Scope("bodyAge", "身体年龄", build_body_age),
|
||||
Scope("challenges", "挑战赛", build_challenges),
|
||||
Scope("activity", "运动详情", build_activity, needs_subject=True),
|
||||
)
|
||||
}
|
||||
|
||||
# Scopes worth generating ahead of the user asking, after a sync brings new
|
||||
# data in. `activity` and `daily` are excluded on purpose: they are per-item,
|
||||
# so pre-warming them would queue one job per session or per day rather than
|
||||
# one job.
|
||||
PREFETCH_SCOPES = ("health", "sleep", "exercise", "trends", "body", "race",
|
||||
"bodyAge", "challenges")
|
||||
|
||||
|
||||
def build(user_id, scope, subject=None):
|
||||
"""`(subject, context)` for one screen, or None when there is nothing to say."""
|
||||
entry = SCOPES.get(scope)
|
||||
if not entry:
|
||||
raise KeyError(scope)
|
||||
if entry.needs_subject and not subject:
|
||||
return None
|
||||
return entry.build(user_id, subject)
|
||||
@@ -13,6 +13,8 @@ from services import ai as ai_svc
|
||||
from services import analysis as analysis_svc
|
||||
from services import coach
|
||||
from services import insights
|
||||
from services import jobs
|
||||
from services import scopes
|
||||
|
||||
|
||||
def day(date, **metrics):
|
||||
@@ -487,27 +489,20 @@ class TestGetBriefing:
|
||||
def test_the_non_blocking_path_answers_without_calling_a_model(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
"""It enqueues instead. No worker runs in the suite, so a model call
|
||||
here would mean the request generated inline."""
|
||||
calls = answer(monkeypatch, json.dumps(GOOD_BRIEFING, ensure_ascii=False))
|
||||
monkeypatch.setattr(analysis_svc, "_run_in_background", lambda key, fn: True)
|
||||
out = analysis_svc.get_briefing(month["id"])
|
||||
assert out["meta"]["pending"] is True
|
||||
assert out["briefing"]["status"]
|
||||
assert calls == []
|
||||
assert jobs.pending_count(month["id"]) == 1
|
||||
|
||||
def test_one_generation_per_key_no_matter_how_often_it_is_polled(self):
|
||||
"""The poll runs every few seconds; a generation takes minutes."""
|
||||
started = []
|
||||
# A job that never finishes, so the key stays claimed across polls.
|
||||
blocked = analysis_svc.threading.Event()
|
||||
analysis_svc._run_in_background("test-key", lambda: (
|
||||
started.append(1), blocked.wait(5)
|
||||
))
|
||||
try:
|
||||
for _ in range(5):
|
||||
analysis_svc._run_in_background("test-key", lambda: started.append(1))
|
||||
assert len(started) == 1
|
||||
finally:
|
||||
blocked.set()
|
||||
def test_polling_does_not_queue_a_job_per_poll(self, month, gateway):
|
||||
"""The screen polls every few seconds; a generation takes minutes."""
|
||||
for _ in range(5):
|
||||
analysis_svc.get_briefing(month["id"])
|
||||
assert jobs.pending_count(month["id"]) == 1
|
||||
|
||||
|
||||
class TestGetTrendInsight:
|
||||
@@ -654,10 +649,8 @@ class TestEndpoints:
|
||||
assert "可以，注意强度。" in body
|
||||
|
||||
def test_briefing_never_leaks_the_gateway_token(
|
||||
self, client, auth, month, gateway, monkeypatch
|
||||
self, client, auth, month, gateway
|
||||
):
|
||||
# No real background generation: this suite must not reach the network.
|
||||
monkeypatch.setattr(analysis_svc, "_run_in_background", lambda key, fn: True)
|
||||
body = client.get("/api/analysis/briefing", headers=auth).get_data(as_text=True)
|
||||
assert "test-token" not in body
|
||||
|
||||
@@ -698,9 +691,293 @@ class TestRegenerate:
|
||||
analysis_svc.get_briefing(month["id"], wait=True)
|
||||
assert analysis_svc.get_briefing(month["id"])["meta"]["cached"] is True
|
||||
|
||||
monkeypatch.setattr(analysis_svc, "_run_in_background", lambda key, fn: True)
|
||||
analysis_svc.get_briefing(month["id"], refresh=True)
|
||||
|
||||
after = analysis_svc.get_briefing(month["id"])
|
||||
assert after["meta"].get("cached") is not True
|
||||
assert after["meta"]["pending"] is True
|
||||
|
||||
|
||||
# --- the producer/consumer queue --------------------------------------------
|
||||
class TestJobQueue:
|
||||
def test_enqueue_then_claim(self, db, user):
|
||||
jobs.enqueue(user["id"], "sleep", "2026-08-30:14")
|
||||
claimed = jobs._claim_next()
|
||||
assert claimed["kind"] == "sleep"
|
||||
assert claimed["status"] == "pending", "the row read is the pre-claim one"
|
||||
assert jobs.status_of(user["id"], "sleep", "2026-08-30:14")["status"] == "running"
|
||||
|
||||
def test_the_same_work_queued_twice_is_one_row(self, db, user):
|
||||
for _ in range(6):
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
assert jobs.pending_count(user["id"]) == 1
|
||||
|
||||
def test_opening_a_screen_promotes_it_ahead_of_the_backfill(self, db, user):
|
||||
for scope in ("health", "exercise", "trends"):
|
||||
jobs.enqueue(user["id"], scope, "s", priority=jobs.PRIORITY_PREFETCH)
|
||||
jobs.enqueue(user["id"], "sleep", "s", priority=jobs.PRIORITY_PREFETCH)
|
||||
# The user opens 睡眠 while the backfill is still queued.
|
||||
jobs.enqueue(user["id"], "sleep", "s", priority=jobs.PRIORITY_INTERACTIVE)
|
||||
assert jobs._claim_next()["kind"] == "sleep"
|
||||
|
||||
def test_priority_is_never_demoted(self, db, user):
|
||||
jobs.enqueue(user["id"], "sleep", "s", priority=jobs.PRIORITY_INTERACTIVE)
|
||||
jobs.enqueue(user["id"], "sleep", "s", priority=jobs.PRIORITY_PREFETCH)
|
||||
assert jobs.status_of(user["id"], "sleep", "s")["priority"] == \
|
||||
jobs.PRIORITY_INTERACTIVE
|
||||
|
||||
def test_equal_priority_runs_oldest_first(self, db, user):
|
||||
jobs.enqueue(user["id"], "health", "s")
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
assert jobs._claim_next()["kind"] == "health"
|
||||
|
||||
def test_a_finished_job_is_not_run_again(self, db, user):
|
||||
jobs.enqueue(user["id"], "sleep", "s", fingerprint="abc")
|
||||
jobs._finish(jobs.job_id(user["id"], "sleep", "s"))
|
||||
assert jobs.enqueue(user["id"], "sleep", "s", fingerprint="abc") == "done"
|
||||
assert jobs._claim_next() is None
|
||||
|
||||
def test_changed_data_requeues_a_finished_job(self, db, user):
|
||||
jobs.enqueue(user["id"], "sleep", "s", fingerprint="abc")
|
||||
jobs._finish(jobs.job_id(user["id"], "sleep", "s"))
|
||||
assert jobs.enqueue(user["id"], "sleep", "s", fingerprint="xyz") == "pending"
|
||||
|
||||
def test_a_running_job_is_not_restarted_by_a_poll(self, db, user):
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
jobs._claim_next()
|
||||
assert jobs.enqueue(user["id"], "sleep", "s") == "running"
|
||||
|
||||
def test_a_claim_left_by_a_dead_worker_is_retried(self, db, user):
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
jobs._claim_next()
|
||||
assert jobs._claim_next() is None, "still within the claim window"
|
||||
# Backdate the claim past its timeout, as a killed worker would leave it.
|
||||
stale = (jobs._now() - jobs.datetime.timedelta(
|
||||
seconds=jobs.CLAIM_TIMEOUT_SECONDS + 60))
|
||||
db.execute("UPDATE ai_jobs SET claimed_at = ?", [jobs._iso(stale)])
|
||||
assert jobs._claim_next() is not None
|
||||
|
||||
def test_restart_releases_whatever_was_running(self, db, user):
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
jobs._claim_next()
|
||||
jobs.reset_stale_claims()
|
||||
assert jobs.status_of(user["id"], "sleep", "s")["status"] == "pending"
|
||||
|
||||
def test_a_job_that_keeps_failing_is_eventually_dropped(self, db, user):
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
for _ in range(jobs.MAX_ATTEMPTS):
|
||||
row = jobs._claim_next()
|
||||
assert row is not None
|
||||
jobs._finish(row["id"], "boom")
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
assert jobs._claim_next() is None
|
||||
|
||||
def test_the_runner_failing_does_not_stop_the_queue(self, db, user, monkeypatch):
|
||||
monkeypatch.setattr(jobs, "_runner", lambda *a: (_ for _ in ()).throw(RuntimeError("x")))
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
assert jobs.run_once() is True
|
||||
assert jobs.status_of(user["id"], "sleep", "s")["status"] == "failed"
|
||||
|
||||
def test_run_once_dispatches_to_the_registered_runner(self, db, user, monkeypatch):
|
||||
seen = []
|
||||
monkeypatch.setattr(jobs, "_runner", lambda u, k, s: seen.append((u, k, s)))
|
||||
jobs.enqueue(user["id"], "sleep", "2026-08-30:14")
|
||||
assert jobs.run_once() is True
|
||||
assert seen == [(user["id"], "sleep", "2026-08-30:14")]
|
||||
assert jobs.status_of(user["id"], "sleep", "2026-08-30:14")["status"] == "done"
|
||||
|
||||
def test_run_once_is_a_no_op_on_an_empty_queue(self, db, user, monkeypatch):
|
||||
monkeypatch.setattr(jobs, "_runner", lambda *a: None)
|
||||
assert jobs.run_once() is False
|
||||
|
||||
def test_jobs_are_per_account(self, db, user, make_user):
|
||||
other = make_user("other@example.com")
|
||||
jobs.enqueue(user["id"], "sleep", "s")
|
||||
assert jobs.pending_count(other["id"]) == 0
|
||||
|
||||
|
||||
# --- per-screen scopes ------------------------------------------------------
|
||||
class TestScopes:
|
||||
def test_every_registered_scope_builds_or_declines(self, month):
|
||||
"""No builder may raise on a real account — an empty screen returns
|
||||
None so the caller can stay quiet."""
|
||||
for name in scopes.SCOPES:
|
||||
out = scopes.build(month["id"], name, subject="1" if name == "activity" else None)
|
||||
assert out is None or (isinstance(out, tuple) and len(out) == 2)
|
||||
|
||||
def test_a_scope_with_data_carries_highlights_and_a_subject(self, month):
|
||||
subject, context = scopes.build(month["id"], "sleep")
|
||||
assert subject
|
||||
assert context["scope"] == "sleep"
|
||||
assert context["highlights"]
|
||||
|
||||
def test_contexts_stay_small_enough_to_prompt_with(self, month):
|
||||
for name in ("health", "sleep", "exercise", "trends", "daily"):
|
||||
built = scopes.build(month["id"], name)
|
||||
if not built:
|
||||
continue
|
||||
blob = json.dumps(built[1], ensure_ascii=False)
|
||||
assert len(blob) < 30_000, f"{name} context is {len(blob)} chars"
|
||||
|
||||
def test_an_empty_screen_declines_rather_than_inventing_one(self, db, user):
|
||||
assert scopes.build(user["id"], "body") is None
|
||||
assert scopes.build(user["id"], "race") is None
|
||||
|
||||
def test_per_item_scopes_need_a_subject(self, month):
|
||||
assert scopes.build(month["id"], "activity") is None
|
||||
|
||||
def test_unknown_scope_raises(self, month):
|
||||
with pytest.raises(KeyError):
|
||||
scopes.build(month["id"], "nope")
|
||||
|
||||
def test_prefetch_list_excludes_per_item_screens(self):
|
||||
"""Pre-warming `activity` would queue one job per session, not one."""
|
||||
assert "activity" not in scopes.PREFETCH_SCOPES
|
||||
assert "daily" not in scopes.PREFETCH_SCOPES
|
||||
assert set(scopes.PREFETCH_SCOPES) <= set(scopes.SCOPES)
|
||||
|
||||
def test_health_scope_carries_the_bands_the_ui_shows(self, month):
|
||||
_, context = scopes.build(month["id"], "health")
|
||||
assert context["referenceBands"], "the model must not contradict the card"
|
||||
|
||||
|
||||
class TestScopeInsight:
|
||||
def test_unknown_scope_raises(self, month):
|
||||
with pytest.raises(KeyError):
|
||||
analysis_svc.get_scope_insight(month["id"], "nope")
|
||||
|
||||
def test_an_empty_screen_reports_it(self, db, user, gateway):
|
||||
out = analysis_svc.get_scope_insight(user["id"], "race")
|
||||
assert out["meta"]["source"] == "none"
|
||||
|
||||
def test_first_visit_answers_from_rules_and_queues_the_model(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
calls = answer(monkeypatch, "{}")
|
||||
out = analysis_svc.get_scope_insight(month["id"], "sleep")
|
||||
assert out["meta"]["pending"] is True
|
||||
# The computed facts, with the first one leading; a screen with a
|
||||
# single fact has a headline and no remaining points.
|
||||
assert out["insight"]["headline"]
|
||||
assert calls == [], "nothing may be generated inside the request"
|
||||
assert jobs.pending_count(month["id"]) == 1
|
||||
|
||||
def test_the_stored_answer_is_served_once_generated(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
reply = json.dumps({
|
||||
"headline": "睡眠偏短。", "points": [{"title": "时长", "detail": "6.5 小时。"}],
|
||||
"actions": ["提前入睡"], "caution": None, "confidence": "medium",
|
||||
}, ensure_ascii=False)
|
||||
answer(monkeypatch, reply)
|
||||
analysis_svc.generate_scope_insight(month["id"], "sleep")
|
||||
out = analysis_svc.get_scope_insight(month["id"], "sleep")
|
||||
assert out["meta"]["source"] == "ai"
|
||||
assert out["meta"]["cached"] is True
|
||||
assert out["insight"]["headline"] == "睡眠偏短。"
|
||||
|
||||
def test_rules_fallback_says_it_has_not_been_interpreted(self, month, gateway):
|
||||
out = analysis_svc.get_scope_insight(month["id"], "sleep")
|
||||
assert "尚未经过模型解读" in out["insight"]["caution"]
|
||||
|
||||
def test_prefetch_queues_every_screen_that_has_data(self, month, gateway):
|
||||
queued = analysis_svc.prefetch_insights(month["id"])
|
||||
assert "briefing" in queued
|
||||
assert "sleep" in queued
|
||||
assert jobs.pending_count(month["id"]) == len(queued)
|
||||
|
||||
def test_the_queue_runner_reaches_a_scope(self, month, gateway, monkeypatch):
|
||||
reply = json.dumps({"headline": "ok", "points": [], "actions": [],
|
||||
"confidence": "low"}, ensure_ascii=False)
|
||||
answer(monkeypatch, reply)
|
||||
subject, _ = scopes.build(month["id"], "sleep")
|
||||
analysis_svc._run_job(month["id"], "sleep", subject)
|
||||
assert analysis_svc.get_scope_insight(month["id"], "sleep")["meta"]["source"] == "ai"
|
||||
|
||||
|
||||
class TestParseScopeInsight:
|
||||
def test_valid_reply(self):
|
||||
out = coach.parse_scope_insight(json.dumps({
|
||||
"headline": "睡眠不足。",
|
||||
"points": [{"title": "时长", "detail": "平均 6 小时。"}],
|
||||
"actions": ["提前入睡"], "caution": "样本偏少", "confidence": "high",
|
||||
}, ensure_ascii=False))
|
||||
assert out["confidence"] == "high"
|
||||
assert out["actions"] == ["提前入睡"]
|
||||
|
||||
def test_points_written_as_plain_strings(self):
|
||||
out = coach.parse_scope_insight(json.dumps(
|
||||
{"headline": "h", "points": ["纯文本要点"]}, ensure_ascii=False))
|
||||
assert out["points"][0]["detail"] == "纯文本要点"
|
||||
|
||||
def test_blank_reply_is_rejected(self):
|
||||
with pytest.raises(ai_svc.AIError):
|
||||
coach.parse_scope_insight(json.dumps({"confidence": "high"}))
|
||||
|
||||
def test_reasoning_preamble_is_skipped(self):
|
||||
reply = ("Let me think. Maybe {\"headline\": \"draft\"} ... final:\n"
|
||||
+ json.dumps({"headline": "定稿", "points": []}, ensure_ascii=False))
|
||||
assert coach.parse_scope_insight(reply)["headline"] == "定稿"
|
||||
|
||||
|
||||
class TestScopeEndpoint:
|
||||
def test_requires_auth(self, client):
|
||||
assert client.get("/api/analysis/insight?scope=sleep").status_code == 401
|
||||
|
||||
def test_unknown_scope_lists_the_supported_ones(self, client, auth, month):
|
||||
resp = client.get("/api/analysis/insight?scope=nope", headers=auth)
|
||||
assert resp.status_code == 400
|
||||
assert "sleep" in resp.get_json()["supported"]
|
||||
|
||||
def test_answers_immediately(self, client, auth, month):
|
||||
resp = client.get("/api/analysis/insight?scope=sleep", headers=auth)
|
||||
assert resp.status_code == 200
|
||||
assert resp.get_json()["insight"]["headline"]
|
||||
|
||||
def test_queue_endpoint_reports_outstanding_work(self, client, auth, month):
|
||||
client.get("/api/analysis/insight?scope=sleep", headers=auth)
|
||||
assert client.get("/api/analysis/insight/queue", headers=auth).get_json()["pending"] >= 1
|
||||
|
||||
|
||||
class TestOutageRecovery:
|
||||
"""A gateway outage must delay an insight, not retire it."""
|
||||
|
||||
def test_a_given_up_job_is_retried_after_the_cooldown(self, db, user):
|
||||
jid = jobs.job_id(user["id"], "sleep", "s")
|
||||
jobs.enqueue(user["id"], "sleep", "s", fingerprint="abc")
|
||||
for _ in range(jobs.MAX_ATTEMPTS):
|
||||
row = jobs._claim_next()
|
||||
jobs._finish(row["id"], "connection refused")
|
||||
jobs.enqueue(user["id"], "sleep", "s", fingerprint="abc")
|
||||
assert jobs.enqueue(user["id"], "sleep", "s", fingerprint="abc") == "failed"
|
||||
|
||||
# Past the cooldown, as it would be once the upstream is back.
|
||||
old = jobs._now() - jobs.datetime.timedelta(
|
||||
seconds=jobs.FAILED_RETRY_SECONDS + 60)
|
||||
db.execute("UPDATE ai_jobs SET updated_at = ? WHERE id = ?",
|
||||
[jobs._iso(old), jid])
|
||||
assert jobs.enqueue(user["id"], "sleep", "s", fingerprint="abc") == "pending"
|
||||
assert jobs.status_of(user["id"], "sleep", "s")["attempts"] == 0
|
||||
assert jobs._claim_next() is not None
|
||||
|
||||
def test_the_screen_stops_polling_once_the_queue_gives_up(
|
||||
self, month, gateway, monkeypatch
|
||||
):
|
||||
"""`pending` must go false, or the card spins for minutes waiting on an
|
||||
answer that is not coming."""
|
||||
def boom(self, messages, timeout=None, max_tokens=None):
|
||||
raise ai_svc.AIError("upstream unreachable")
|
||||
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "chat", boom)
|
||||
monkeypatch.setattr(ai_svc.OpenAICompatProvider, "stream", boom)
|
||||
|
||||
subject, _ = scopes.build(month["id"], "sleep")
|
||||
for _ in range(jobs.MAX_ATTEMPTS):
|
||||
analysis_svc.get_scope_insight(month["id"], "sleep")
|
||||
jobs.run_once()
|
||||
|
||||
out = analysis_svc.get_scope_insight(month["id"], "sleep")
|
||||
assert out["meta"]["queue"] == "failed"
|
||||
assert out["meta"]["pending"] is False
|
||||
assert out["meta"]["reason"], "the card should be able to say why"
|
||||
assert out["insight"]["headline"], "and still show the computed facts"
|
||||
|
||||
@@ -1,17 +1,10 @@
|
||||
import { useCallback, useEffect, useRef, useState } from 'react';
|
||||
import { useCallback, useState } from 'react';
|
||||
import {
|
||||
apiClient, errorMessage, Briefing, BriefingContext, InsightMeta,
|
||||
apiClient, Briefing, BriefingContext, InsightMeta,
|
||||
} from '../services/api';
|
||||
import { usePolledInsight } from '../lib/insight';
|
||||
import './AiBriefing.css';
|
||||
|
||||
/* A generation runs for minutes on the gateway's reasoning upstream, so the
|
||||
card shows the rule-based briefing immediately and polls for the model's
|
||||
version. The interval is a compromise: often enough that the swap feels
|
||||
like it belongs to this visit, rare enough that a five-minute generation
|
||||
costs a few dozen requests rather than a few hundred. */
|
||||
const POLL_MS = 8000;
|
||||
const POLL_LIMIT_MS = 6 * 60 * 1000;
|
||||
|
||||
interface Props {
|
||||
/** Day to brief on. Omit for the newest day on record. */
|
||||
date?: string;
|
||||
@@ -79,65 +72,17 @@ function Deviations({ context }: { context: BriefingContext }) {
|
||||
}
|
||||
|
||||
function AiBriefing({ date }: Props) {
|
||||
const [briefing, setBriefing] = useState<Briefing | null>(null);
|
||||
const [context, setContext] = useState<BriefingContext | null>(null);
|
||||
const [meta, setMeta] = useState<InsightMeta | null>(null);
|
||||
const [error, setError] = useState('');
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [open, setOpen] = useState(false);
|
||||
|
||||
/* The poll is cleared on unmount and whenever the day changes, so stepping
|
||||
back through dates cannot leave a timer writing into a stale card. */
|
||||
const timer = useRef<number>();
|
||||
const startedAt = useRef(0);
|
||||
|
||||
const load = useCallback(async (refresh?: boolean) => {
|
||||
try {
|
||||
const data = await apiClient.getBriefing({ date, refresh });
|
||||
setBriefing(data.briefing);
|
||||
setContext(data.context);
|
||||
setMeta(data.meta);
|
||||
setError('');
|
||||
return data.meta;
|
||||
} catch (err: any) {
|
||||
setError(errorMessage(err, '获取简报失败'));
|
||||
return null;
|
||||
} finally {
|
||||
setLoading(false);
|
||||
}
|
||||
const resp = await apiClient.getBriefing({ date, refresh });
|
||||
return { data: resp.briefing, meta: resp.meta, extra: resp.context };
|
||||
}, [date]);
|
||||
|
||||
const cancelled = useRef(false);
|
||||
|
||||
/* One poll loop, shared by the first load and by 重新生成. Each tick asks
|
||||
without `refresh` — only the first request may bypass the cache, or every
|
||||
tick would restart the generation it is waiting for. */
|
||||
const poll = useCallback(async (refresh?: boolean) => {
|
||||
window.clearTimeout(timer.current);
|
||||
startedAt.current = Date.now();
|
||||
setLoading(true);
|
||||
|
||||
const tick = async (first: boolean) => {
|
||||
const result = await load(first && refresh);
|
||||
if (cancelled.current) return;
|
||||
// Stop as soon as a model answer lands, and give up after the window a
|
||||
// generation realistically needs — an upstream that has gone quiet
|
||||
// should not leave the tab polling for the rest of the session.
|
||||
if (result?.pending && Date.now() - startedAt.current < POLL_LIMIT_MS) {
|
||||
timer.current = window.setTimeout(() => tick(false), POLL_MS);
|
||||
}
|
||||
};
|
||||
await tick(true);
|
||||
}, [load]);
|
||||
|
||||
useEffect(() => {
|
||||
cancelled.current = false;
|
||||
poll();
|
||||
return () => {
|
||||
cancelled.current = true;
|
||||
window.clearTimeout(timer.current);
|
||||
};
|
||||
}, [poll]);
|
||||
const {
|
||||
data: briefing, meta, extra, loading, error, refresh,
|
||||
} = usePolledInsight<Briefing>(load, '获取简报失败');
|
||||
const context = extra as BriefingContext | null;
|
||||
|
||||
if (loading && !briefing) {
|
||||
return <div className="brief-skeleton" aria-label="正在生成简报" />;
|
||||
@@ -197,11 +142,7 @@ function AiBriefing({ date }: Props) {
|
||||
{context && <Deviations context={context} />}
|
||||
|
||||
<div className="brief-foot">
|
||||
<button
|
||||
className="brief-link"
|
||||
onClick={() => poll(true)}
|
||||
disabled={loading}
|
||||
>
|
||||
<button className="brief-link" onClick={refresh} disabled={loading}>
|
||||
{loading ? '重新生成中…' : '重新生成'}
|
||||
</button>
|
||||
{meta.generatedAt && (
|
||||
|
||||
150
client/src/components/AiPanel.css
Normal file
150
client/src/components/AiPanel.css
Normal file
@@ -0,0 +1,150 @@
|
||||
/* The AI reading shown on every data screen. Deliberately quieter than the
|
||||
今日 briefing card: that one is the hero of its screen, these sit among the
|
||||
charts they comment on. */
|
||||
.aip {
|
||||
background: var(--surface-1);
|
||||
border: 1px solid var(--border);
|
||||
border-radius: 14px;
|
||||
padding: 0.85rem 0.9rem 0.6rem;
|
||||
margin-bottom: 1.25rem;
|
||||
animation: aip-in 0.32s var(--ease) both;
|
||||
}
|
||||
|
||||
@keyframes aip-in {
|
||||
from { opacity: 0; transform: translateY(6px); }
|
||||
to { opacity: 1; transform: none; }
|
||||
}
|
||||
|
||||
.aip-skeleton {
|
||||
height: 104px;
|
||||
border-radius: 14px;
|
||||
margin-bottom: 1.25rem;
|
||||
background: linear-gradient(
|
||||
100deg, var(--surface-1) 30%, var(--surface-2) 50%, var(--surface-1) 70%
|
||||
);
|
||||
background-size: 220% 100%;
|
||||
animation: aip-shimmer 1.4s linear infinite;
|
||||
}
|
||||
|
||||
@keyframes aip-shimmer {
|
||||
from { background-position: 180% 0; }
|
||||
to { background-position: -80% 0; }
|
||||
}
|
||||
|
||||
.aip-error { color: var(--status-critical); font-size: 0.82rem; }
|
||||
|
||||
.aip-head {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
gap: 0.5rem;
|
||||
margin-bottom: 0.15rem;
|
||||
}
|
||||
|
||||
.aip-head .sec-title { margin: 0; }
|
||||
|
||||
/* Always visible: a model reading and a plain calculation look alike on the
|
||||
page, and which one it is changes how much weight it deserves. */
|
||||
.aip-badge {
|
||||
display: inline-flex;
|
||||
align-items: center;
|
||||
gap: 0.3rem;
|
||||
font-size: 0.64rem;
|
||||
font-weight: 600;
|
||||
padding: 0.14rem 0.42rem;
|
||||
border-radius: 999px;
|
||||
color: var(--text-muted);
|
||||
background: var(--surface-0);
|
||||
border: 1px solid var(--border);
|
||||
white-space: nowrap;
|
||||
}
|
||||
|
||||
.aip-badge-ai {
|
||||
color: var(--accent);
|
||||
background: var(--accent-soft);
|
||||
border-color: transparent;
|
||||
}
|
||||
|
||||
.aip-badge-pending { color: var(--text-secondary); }
|
||||
|
||||
.aip-spinner {
|
||||
width: 0.52rem;
|
||||
height: 0.52rem;
|
||||
border-radius: 50%;
|
||||
border: 1.5px solid var(--border-strong);
|
||||
border-top-color: var(--accent);
|
||||
animation: aip-spin 0.8s linear infinite;
|
||||
}
|
||||
|
||||
@keyframes aip-spin { to { transform: rotate(360deg); } }
|
||||
|
||||
.aip-headline {
|
||||
margin: 0.4rem 0 0.7rem;
|
||||
font-size: 0.88rem;
|
||||
line-height: 1.55;
|
||||
color: var(--text-primary);
|
||||
}
|
||||
|
||||
.aip-point { margin-bottom: 0.55rem; }
|
||||
|
||||
.aip-tag {
|
||||
display: inline-block;
|
||||
margin-bottom: 0.14rem;
|
||||
font-size: 0.68rem;
|
||||
font-weight: 700;
|
||||
padding: 0.1rem 0.4rem;
|
||||
border-radius: 6px;
|
||||
color: var(--accent);
|
||||
background: var(--accent-soft);
|
||||
}
|
||||
|
||||
.aip-point p {
|
||||
margin: 0;
|
||||
font-size: 0.83rem;
|
||||
line-height: 1.5;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.aip-actions {
|
||||
margin: 0.5rem 0 0;
|
||||
padding-left: 1.05rem;
|
||||
}
|
||||
|
||||
.aip-actions li {
|
||||
font-size: 0.83rem;
|
||||
line-height: 1.55;
|
||||
color: var(--text-secondary);
|
||||
}
|
||||
|
||||
.aip-caution {
|
||||
margin: 0.55rem 0 0;
|
||||
padding-left: 0.55rem;
|
||||
border-left: 2px solid var(--status-warning);
|
||||
font-size: 0.76rem;
|
||||
line-height: 1.5;
|
||||
color: var(--text-muted);
|
||||
}
|
||||
|
||||
.aip-foot {
|
||||
display: flex;
|
||||
align-items: center;
|
||||
justify-content: space-between;
|
||||
gap: 0.5rem;
|
||||
margin-top: 0.65rem;
|
||||
padding-top: 0.5rem;
|
||||
border-top: 1px solid var(--grid);
|
||||
font-size: 0.68rem;
|
||||
color: var(--text-muted);
|
||||
}
|
||||
|
||||
.aip-link {
|
||||
background: none;
|
||||
border: none;
|
||||
padding: 0;
|
||||
font-size: 0.74rem;
|
||||
font-weight: 600;
|
||||
color: var(--accent);
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
.aip-link:disabled { color: var(--text-muted); cursor: default; }
|
||||
99
client/src/components/AiPanel.tsx
Normal file
99
client/src/components/AiPanel.tsx
Normal file
@@ -0,0 +1,99 @@
|
||||
import { useCallback } from 'react';
|
||||
import {
|
||||
apiClient, InsightScope, InsightMeta, ScopeInsight,
|
||||
} from '../services/api';
|
||||
import { usePolledInsight } from '../lib/insight';
|
||||
import './AiPanel.css';
|
||||
|
||||
const CONFIDENCE_LABEL: Record<string, string> = {
|
||||
high: '证据充分', medium: '证据一般', low: '证据薄弱',
|
||||
};
|
||||
|
||||
interface Props {
|
||||
scope: InsightScope;
|
||||
/** Identifies the item for per-item screens: an activity id, a date. */
|
||||
subject?: string;
|
||||
/** Heading. Defaults to the generic one. */
|
||||
title?: string;
|
||||
}
|
||||
|
||||
function Badge({ meta }: { meta: InsightMeta }) {
|
||||
if (meta.source === 'ai') {
|
||||
return (
|
||||
<span className="aip-badge aip-badge-ai" title={meta.model ?? undefined}>
|
||||
AI 生成{meta.upstream ? ` · ${meta.upstream}` : ''}
|
||||
</span>
|
||||
);
|
||||
}
|
||||
if (meta.pending) {
|
||||
return (
|
||||
<span className="aip-badge aip-badge-pending">
|
||||
<span className="aip-spinner" aria-hidden="true" />
|
||||
排队生成中
|
||||
</span>
|
||||
);
|
||||
}
|
||||
return <span className="aip-badge" title={meta.reason}>直接计算</span>;
|
||||
}
|
||||
|
||||
/**
|
||||
* One screen's AI reading.
|
||||
*
|
||||
* The same component on every screen: what differs between 睡眠 and 运动 is
|
||||
* entirely in the context the backend builds, so a new screen is one more
|
||||
* `<AiPanel scope="…" />` and a builder — not another card.
|
||||
*
|
||||
* It always renders something. The computed facts appear immediately, and the
|
||||
* model's interpretation replaces them when the coach's queue reaches this
|
||||
* screen — which opening the screen moves to the front of.
|
||||
*/
|
||||
function AiPanel({ scope, subject, title = 'AI 解读' }: Props) {
|
||||
const load = useCallback(async () => {
|
||||
const resp = await apiClient.getInsight(scope, { subject });
|
||||
return { data: resp.insight, meta: resp.meta };
|
||||
}, [scope, subject]);
|
||||
|
||||
const { data, meta, loading, error, refresh } =
|
||||
usePolledInsight<ScopeInsight>(load);
|
||||
|
||||
if (loading && !data) return <div className="aip-skeleton" aria-label="正在读取解读" />;
|
||||
if (error) return <section className="aip aip-error">{error}</section>;
|
||||
if (!data || !meta) return null;
|
||||
// Nothing to read yet on this screen — a card saying so is worse than none.
|
||||
if (meta.source === 'none') return null;
|
||||
|
||||
return (
|
||||
<section className="aip">
|
||||
<div className="aip-head">
|
||||
<h3 className="sec-title">{title}</h3>
|
||||
<Badge meta={meta} />
|
||||
</div>
|
||||
|
||||
{data.headline && <p className="aip-headline">{data.headline}</p>}
|
||||
|
||||
{data.points.map((p) => (
|
||||
<div className="aip-point" key={p.title + p.detail}>
|
||||
<span className="aip-tag">{p.title}</span>
|
||||
<p>{p.detail}</p>
|
||||
</div>
|
||||
))}
|
||||
|
||||
{!!data.actions.length && (
|
||||
<ul className="aip-actions">
|
||||
{data.actions.map((a) => <li key={a}>{a}</li>)}
|
||||
</ul>
|
||||
)}
|
||||
|
||||
{data.caution && <p className="aip-caution">{data.caution}</p>}
|
||||
|
||||
<div className="aip-foot">
|
||||
<button className="aip-link" onClick={refresh} disabled={loading}>
|
||||
{loading ? '重新生成中…' : '重新生成'}
|
||||
</button>
|
||||
<span>{CONFIDENCE_LABEL[data.confidence]}</span>
|
||||
</div>
|
||||
</section>
|
||||
);
|
||||
}
|
||||
|
||||
export default AiPanel;
|
||||
99
client/src/lib/insight.ts
Normal file
99
client/src/lib/insight.ts
Normal file
@@ -0,0 +1,99 @@
|
||||
import { useCallback, useEffect, useRef, useState } from 'react';
|
||||
import { errorMessage, InsightMeta } from '../services/api';
|
||||
|
||||
/* A generation runs for minutes on the gateway, so every AI card shows the
|
||||
rule-based version first and polls for the model's. The interval is a
|
||||
compromise: often enough that the swap belongs to this visit, rare enough
|
||||
that a five-minute wait costs a few dozen requests rather than a few
|
||||
hundred. */
|
||||
export const POLL_MS = 8000;
|
||||
export const POLL_LIMIT_MS = 8 * 60 * 1000;
|
||||
|
||||
export interface Loaded<T> {
|
||||
data: T | null;
|
||||
meta: InsightMeta;
|
||||
}
|
||||
|
||||
export interface Polled<T> {
|
||||
data: T | null;
|
||||
meta: InsightMeta | null;
|
||||
/** Extra payload the loader returned alongside the insight (the context). */
|
||||
extra: unknown;
|
||||
loading: boolean;
|
||||
error: string;
|
||||
/** Discard the stored answer and generate again. */
|
||||
refresh: () => void;
|
||||
}
|
||||
|
||||
/**
|
||||
* Load an AI insight, then keep polling while the model is still working.
|
||||
*
|
||||
* Shared by every AI card. `load` must be stable — wrap it in `useCallback`
|
||||
* keyed on whatever identifies the thing being read — because a new identity
|
||||
* restarts the poll, which is exactly right when the screen changes what it
|
||||
* is about and exactly wrong on an unrelated re-render.
|
||||
*/
|
||||
export function usePolledInsight<T>(
|
||||
load: (refresh?: boolean) => Promise<Loaded<T> & { extra?: unknown }>,
|
||||
fallbackMessage = '获取 AI 解读失败'
|
||||
): Polled<T> {
|
||||
const [data, setData] = useState<T | null>(null);
|
||||
const [meta, setMeta] = useState<InsightMeta | null>(null);
|
||||
const [extra, setExtra] = useState<unknown>(null);
|
||||
const [loading, setLoading] = useState(true);
|
||||
const [error, setError] = useState('');
|
||||
|
||||
const timer = useRef<number>();
|
||||
const startedAt = useRef(0);
|
||||
const cancelled = useRef(false);
|
||||
|
||||
const fetchOnce = useCallback(async (refresh?: boolean) => {
|
||||
try {
|
||||
const result = await load(refresh);
|
||||
if (cancelled.current) return null;
|
||||
setData(result.data);
|
||||
setMeta(result.meta);
|
||||
setExtra(result.extra ?? null);
|
||||
setError('');
|
||||
return result.meta;
|
||||
} catch (err: any) {
|
||||
if (!cancelled.current) setError(errorMessage(err, fallbackMessage));
|
||||
return null;
|
||||
} finally {
|
||||
if (!cancelled.current) setLoading(false);
|
||||
}
|
||||
}, [load, fallbackMessage]);
|
||||
|
||||
const poll = useCallback(async (refresh?: boolean) => {
|
||||
window.clearTimeout(timer.current);
|
||||
startedAt.current = Date.now();
|
||||
setLoading(true);
|
||||
|
||||
// Only the first request may bypass the cache; a `refresh` on every tick
|
||||
// would restart the generation the poll is waiting for.
|
||||
const tick = async (first: boolean) => {
|
||||
const result = await fetchOnce(first && refresh);
|
||||
if (cancelled.current) return;
|
||||
// Give up after the window a generation realistically needs — an
|
||||
// upstream that has gone quiet must not leave the tab polling forever.
|
||||
if (result?.pending && Date.now() - startedAt.current < POLL_LIMIT_MS) {
|
||||
timer.current = window.setTimeout(() => tick(false), POLL_MS);
|
||||
}
|
||||
};
|
||||
await tick(true);
|
||||
}, [fetchOnce]);
|
||||
|
||||
useEffect(() => {
|
||||
cancelled.current = false;
|
||||
poll();
|
||||
return () => {
|
||||
cancelled.current = true;
|
||||
window.clearTimeout(timer.current);
|
||||
};
|
||||
}, [poll]);
|
||||
|
||||
return {
|
||||
data, meta, extra, loading, error,
|
||||
refresh: () => { poll(true); },
|
||||
};
|
||||
}
|
||||
@@ -2,6 +2,8 @@ import { useEffect, useMemo, useState } from 'react';
|
||||
import { Link } from 'framework7-react';
|
||||
import { apiClient, ActivityDetail, errorMessage } from '../services/api';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import Chart from '../components/charts/Chart';
|
||||
import Skeleton from '../components/Skeleton';
|
||||
import './ActivityDetail.css';
|
||||
@@ -289,6 +291,7 @@ function ActivityDetailPage({ id, f7route }: Props) {
|
||||
|
||||
return (
|
||||
<Screen title={title} subtitle={s.startTimeLocal?.slice(0, 16).replace('T', ' ')} backLink>
|
||||
{FEATURES.ai && <AiPanel scope="activity" subject={activityId} title="AI 本次运动解读" />}
|
||||
<div className="metric-tabs">
|
||||
{TABS.map(([tabId, text]) => (
|
||||
<button
|
||||
|
||||
@@ -2,6 +2,8 @@ import { useEffect, useState } from 'react';
|
||||
import { Link } from 'framework7-react';
|
||||
import { apiClient, errorMessage, FitnessAge } from '../services/api';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import Skeleton from '../components/Skeleton';
|
||||
import './BodyAge.css';
|
||||
|
||||
@@ -42,6 +44,8 @@ function BodyAgePage() {
|
||||
</div>
|
||||
) : (
|
||||
<>
|
||||
{FEATURES.ai && <AiPanel scope="bodyAge" title="AI 身体年龄解读" />}
|
||||
|
||||
<section className="ba-hero">
|
||||
<div className="ba-value">
|
||||
{data.value}<span className="ba-unit">岁</span>
|
||||
|
||||
@@ -4,6 +4,8 @@ import {
|
||||
apiClient, BloodPressureReading, BodyCompositionDay, errorMessage,
|
||||
} from '../services/api';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import Chart from '../components/charts/Chart';
|
||||
import Skeleton from '../components/Skeleton';
|
||||
import { daysAgo, today as todayIso } from '../lib/day';
|
||||
@@ -71,6 +73,8 @@ function BodyPage() {
|
||||
</div>
|
||||
) : (
|
||||
<>
|
||||
{FEATURES.ai && <AiPanel scope="body" title="AI 身体成分解读" />}
|
||||
|
||||
<section className="md-hero">
|
||||
<div className="md-value">
|
||||
{latest?.weightKg != null ? latest.weightKg.toFixed(1) : '—'}
|
||||
|
||||
@@ -2,6 +2,8 @@ import { useEffect, useMemo, useState } from 'react';
|
||||
import { Link } from 'framework7-react';
|
||||
import { apiClient, Challenge, errorMessage } from '../services/api';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import Skeleton from '../components/Skeleton';
|
||||
import './Challenges.css';
|
||||
|
||||
@@ -54,6 +56,8 @@ function ChallengesPage() {
|
||||
</div>
|
||||
) : (
|
||||
<>
|
||||
{FEATURES.ai && <AiPanel scope="challenges" title="AI 挑战赛解读" />}
|
||||
|
||||
{kinds.length > 2 && (
|
||||
<div className="metric-tabs">
|
||||
{kinds.map((k) => (
|
||||
|
||||
@@ -6,6 +6,8 @@ import Chart from '../components/charts/Chart';
|
||||
import Skeleton from '../components/Skeleton';
|
||||
import './Daily.css';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import { iso, today as todayIso } from '../lib/day';
|
||||
|
||||
/** Every stored metric, grouped the way the device groups them. */
|
||||
@@ -189,6 +191,7 @@ function DailyPage() {
|
||||
|
||||
return (
|
||||
<Screen title="每日数据" backLink>
|
||||
{FEATURES.ai && <AiPanel scope="daily" subject={date} title="AI 当日解读" />}
|
||||
|
||||
<div className="day-nav">
|
||||
<button className="day-btn" onClick={() => shift(-1)} aria-label="前一天">‹</button>
|
||||
|
||||
@@ -4,6 +4,8 @@ import {
|
||||
apiClient, Activity, Badge, errorMessage, HealthDay, PersonalRecord,
|
||||
} from '../services/api';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import { daysAgo, today as todayIso } from '../lib/day';
|
||||
import MetricCard from '../components/charts/MetricCard';
|
||||
import Chart from '../components/charts/Chart';
|
||||
@@ -126,6 +128,8 @@ function ExercisePage() {
|
||||
<Screen title="运动" subtitle={`近 ${WINDOW_DAYS} 天`}>
|
||||
{error && <div className="screen-error">{error}</div>}
|
||||
|
||||
{FEATURES.ai && <AiPanel scope="exercise" title="AI 训练解读" />}
|
||||
|
||||
<section className="sec">
|
||||
<div className="mcard-grid">
|
||||
<MetricCard label="运动时长" value={totalMinutes} unit="分钟"
|
||||
|
||||
@@ -5,6 +5,8 @@ import { METRICS, metricHref } from '../lib/metrics';
|
||||
import MetricCard from '../components/charts/MetricCard';
|
||||
import Skeleton from '../components/Skeleton';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import { daysAgo, today as todayIso } from '../lib/day';
|
||||
import './Health.css';
|
||||
import './Settings.css';
|
||||
@@ -143,6 +145,8 @@ function HealthPage() {
|
||||
<Screen title="健康" subtitle="每项指标的最新值与参考区间">
|
||||
<BodyAge data={bodyAge} />
|
||||
|
||||
{FEATURES.ai && <AiPanel scope="health" title="AI 健康解读" />}
|
||||
|
||||
{SECTIONS.map((section) => (
|
||||
<section className="sec" key={section.title}>
|
||||
<h3 className="sec-title">{section.title}</h3>
|
||||
|
||||
@@ -2,6 +2,8 @@ import { useEffect, useState } from 'react';
|
||||
import { Link } from 'framework7-react';
|
||||
import { apiClient, errorMessage, RacePrediction } from '../services/api';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import Chart from '../components/charts/Chart';
|
||||
import Skeleton from '../components/Skeleton';
|
||||
import './MetricDetail.css';
|
||||
@@ -75,6 +77,8 @@ function RacePage() {
|
||||
<Screen title="成绩预测" backLink subtitle={latest.date}>
|
||||
{error && <div className="screen-error">{error}</div>}
|
||||
|
||||
{FEATURES.ai && <AiPanel scope="race" title="AI 成绩解读" />}
|
||||
|
||||
<div className="race-list">
|
||||
{DISTANCES.map(([key, label]) => (
|
||||
<div className="race-row" key={key}>
|
||||
|
||||
@@ -5,6 +5,8 @@ import Chart from '../components/charts/Chart';
|
||||
import StatTile from '../components/charts/StatTile';
|
||||
import Skeleton from '../components/Skeleton';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import { daysAgo, today as todayIso } from '../lib/day';
|
||||
|
||||
const RANGES = [7, 14, 30, 90];
|
||||
@@ -75,6 +77,7 @@ function SleepPage() {
|
||||
|
||||
return (
|
||||
<Screen title="睡眠" backLink subtitle="分期、评分与夜间生理指标">
|
||||
{FEATURES.ai && <AiPanel scope="sleep" title="AI 睡眠解读" />}
|
||||
|
||||
<div className="segmented-row">
|
||||
<span className="segmented-label">范围</span>
|
||||
|
||||
@@ -7,6 +7,8 @@ import {
|
||||
aggregate, Granularity, GRANULARITIES, isCumulative, suggestGranularity,
|
||||
} from '../lib/aggregate';
|
||||
import Screen from '../components/Screen';
|
||||
import AiPanel from '../components/AiPanel';
|
||||
import { FEATURES } from '../features';
|
||||
import { daysAgo, today as todayIso } from '../lib/day';
|
||||
|
||||
const RANGES = [
|
||||
@@ -221,6 +223,7 @@ function TrendsPage() {
|
||||
|
||||
return (
|
||||
<Screen title="趋势">
|
||||
{FEATURES.ai && <AiPanel scope="trends" title="AI 长期趋势归因" />}
|
||||
|
||||
<div className="segmented-row">
|
||||
<span className="segmented-label">范围</span>
|
||||
|
||||
@@ -427,6 +427,27 @@ export interface TrendInsightResponse {
|
||||
meta: InsightMeta;
|
||||
}
|
||||
|
||||
/** One screen's AI reading. The same shape for every screen, so one component
|
||||
* renders all of them. */
|
||||
export interface ScopeInsight {
|
||||
headline: string | null;
|
||||
points: Array<{ title: string; detail: string }>;
|
||||
actions: string[];
|
||||
caution: string | null;
|
||||
confidence: 'high' | 'medium' | 'low';
|
||||
}
|
||||
|
||||
export interface ScopeInsightResponse {
|
||||
insight: ScopeInsight | null;
|
||||
context: Record<string, any> | null;
|
||||
meta: InsightMeta;
|
||||
}
|
||||
|
||||
/** Screens the coach can read. Mirrors SCOPES in backend/services/scopes.py. */
|
||||
export type InsightScope =
|
||||
| 'health' | 'sleep' | 'exercise' | 'trends' | 'daily'
|
||||
| 'body' | 'race' | 'bodyAge' | 'challenges' | 'activity';
|
||||
|
||||
export interface CopilotTurn {
|
||||
role: 'user' | 'assistant';
|
||||
content: string;
|
||||
@@ -777,6 +798,34 @@ class ApiClient {
|
||||
}
|
||||
|
||||
// --- AI coach ---
|
||||
/**
|
||||
* One screen's AI reading.
|
||||
*
|
||||
* Answers immediately: while the model's version is being generated the
|
||||
* computed highlights come back with `meta.pending`, and opening the screen
|
||||
* puts its job at the front of the coach's queue. Poll to pick up the
|
||||
* finished version.
|
||||
*/
|
||||
async getInsight(
|
||||
scope: InsightScope,
|
||||
opts: { subject?: string; refresh?: boolean } = {}
|
||||
) {
|
||||
const { data } = await this.client.get<ScopeInsightResponse>('/analysis/insight', {
|
||||
params: {
|
||||
scope, subject: opts.subject, ...(opts.refresh ? { refresh: 1 } : {}),
|
||||
},
|
||||
});
|
||||
return data;
|
||||
}
|
||||
|
||||
/** How much the coach still has to generate — for a progress hint. */
|
||||
async getInsightQueue() {
|
||||
const { data } = await this.client.get<{ pending: number; enabled: boolean }>(
|
||||
'/analysis/insight/queue'
|
||||
);
|
||||
return data;
|
||||
}
|
||||
|
||||
/**
|
||||
* 晨间简报 for one day, plus the computed context behind it.
|
||||
*
|
||||
|
||||
Reference in New Issue
Block a user