Fix 20+ defects found by real Docker/PostgreSQL deployment
Première exécution réelle de la stack (build des images, PostgreSQL 16, parcours fonctionnels en HTTP) : 28 tables, 105 index, extension pg_trgm, et 181 assertions rejouées après correction. Santé : filtres enum invalides renvoyaient 500 au lieu d'une erreur française ; plan_line s'arrêtait à la fin de la fenêtre du graphique au lieu de la date d'atteinte de l'objectif ; deficit_target_kcal était recalculé après le plancher calorique ; « dernière pesée » affichait deux valeurs différentes selon l'endpoint ; objectif protéines absent. Vape : durée de vie moyenne des résistances incluait la résistance en cours ; archiver la recette active la laissait active ; coût théorique inventé avant la date d'arrêt ; économies projetées dans le futur ; €/ml arrondi à 2 décimales écrasait le modèle de coût DIY. Finances : le sankey compensait crédits et débits non catégorisés ; rows_total excluait les lignes filtrées, faussant l'arithmétique du rapport d'import. Socle : les erreurs HTTP du framework fuitaient en anglais dans l'enveloppe française ; nginx renvoyait sa page 413 HTML au lieu du JSON français ; fins de ligne normalisées en LF. 348 tests pytest (+7), ruff, tsc et vite build au vert. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
This commit is contained in:
Claude Opus 5
@@ -1,3 +1,4 @@
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import http
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from typing import Any
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from fastapi import FastAPI, Request
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@@ -54,6 +55,38 @@ _HTTP_CODES = {
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}
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# Starlette fills `HTTPException.detail` with the ENGLISH reason phrase of the
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# status code ("Not Found", "Method Not Allowed"…). C1 forbids shipping those to
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# the user, so framework-raised errors get a French message instead.
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_FRENCH_MESSAGES = {
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400: "Requête invalide.",
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401: "Authentification requise.",
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403: "Accès refusé.",
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404: "Ressource introuvable.",
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405: "Méthode non autorisée pour cette ressource.",
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409: "Conflit avec l'état actuel de la ressource.",
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413: "Le contenu envoyé est trop volumineux.",
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422: "Les données envoyées sont invalides.",
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429: "Trop de requêtes. Réessayez dans un instant.",
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500: "Une erreur interne est survenue.",
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}
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_FALLBACK_MESSAGE = "Une erreur est survenue."
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def _english_default(status_code: int) -> str | None:
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try:
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return http.HTTPStatus(status_code).phrase
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except ValueError:
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return None
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def _french_message(exc: StarletteHTTPException) -> str:
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detail = exc.detail if isinstance(exc.detail, str) else None
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if detail and detail != _english_default(exc.status_code):
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return detail # explicit message set by the application: keep it
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return _FRENCH_MESSAGES.get(exc.status_code, _FALLBACK_MESSAGE)
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def _payload(code: str, message: str, details: Any = None) -> dict[str, Any]:
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return {"error": {"code": code, "message": message, "details": details or {}}}
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@@ -86,9 +119,7 @@ def register_error_handlers(app: FastAPI) -> None:
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# Normalize framework-raised HTTP errors (404 route, 405…) to the
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# single error shape of §8.3.
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code = _HTTP_CODES.get(exc.status_code, "error")
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message = (
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exc.detail if isinstance(exc.detail, str) else "Une erreur est survenue."
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)
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return JSONResponse(
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status_code=exc.status_code, content=_payload(code, message)
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status_code=exc.status_code,
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content=_payload(code, _french_message(exc)),
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)
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@@ -34,6 +34,7 @@ WORKOUT_OVERLAP_THRESHOLD = 0.8 # cross-source workout dedup (§3.6)
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ADAPTIVE_TDEE_MIN_DAYS = 21 # minimal tracked window for §5.7
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TDEE_SMOOTHING_DAYS = 7 # moving average window for the budget
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MEASURED_TOTAL_MIN_RATIO = 0.8 # total_kcal plausibility guard vs BMR
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PROTEIN_G_PER_KG_DEFAULT = 1.6 # default protein target (ux-pages.md §15.3)
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# Field-by-field merge priority for activity_daily (§3.5). Unknown sources
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# rank after every listed one.
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@@ -216,6 +217,15 @@ def trend_at_day(trend: list[tuple[date, float]], day: date) -> float | None:
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return value
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def protein_goal_g(
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weight_kg: float | None, g_per_kg: float = PROTEIN_G_PER_KG_DEFAULT
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) -> float | None:
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"""Daily protein target in grams (ux-pages.md §15.3: g/kg of body weight)."""
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if weight_kg is None or weight_kg <= 0:
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return None
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return round(weight_kg * g_per_kg, 1)
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def regression_slope(points: list[tuple[date, float]]) -> float | None:
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"""OLS slope in kg/day over (day, value) points; None if < 3 points (§5.5)."""
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if len(points) < 3:
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@@ -26,6 +26,7 @@ OFF_PRODUCT_URL = "https://world.openfoodfacts.org/api/v2/product/{barcode}.json
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OFF_USER_AGENT = "LifeTrack/1.0 (meejayproduction@gmail.com)"
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OFF_TIMEOUT_S = 5.0
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OFF_SOURCE = "off"
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OFF_MAX_PAGE_SIZE = 100
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OFF_FIELDS = (
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"code,product_name,product_name_fr,brands,quantity,serving_size,"
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@@ -182,8 +183,12 @@ def search_foods(db: Session, query: str, limit: int = 20) -> FoodSearchResponse
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return FoodSearchResponse(query=query, items=items, origin="cache")
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known = {(row.source, row.source_id) for row in local}
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# The OFF results overlap the local cache: products already cached are skipped
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# below, so asking only for the missing count under-delivers as soon as the
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# cache is partially warm. Ask for `limit` new products plus the overlap.
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page_size = min(limit + len(known), OFF_MAX_PAGE_SIZE)
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try:
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hits = fetch_off_search(query, limit - len(items))
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hits = fetch_off_search(query, page_size)
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except (httpx.HTTPError, ValueError) as exc:
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if items:
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return FoodSearchResponse(query=query, items=items, origin="cache")
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@@ -463,6 +463,7 @@ class NutritionDayDetail(BaseModel):
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totals: NutritionDayRead
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water_ml: int = 0
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water_goal_ml: int | None = None
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protein_goal_g: float | None = None
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meals: list[MealTotals] = Field(default_factory=list)
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@@ -203,6 +203,17 @@ def weights_query(
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return _apply_sort(stmt, sort, WEIGHT_SORTS, "-measured_at")
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def latest_weight(
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db: Session, user_id: int, tz: ZoneInfo, until: dt.date | None = None
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) -> WeightEntry | None:
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"""Most recent weigh-in (<= end of the local day `until`), or None."""
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stmt = select(WeightEntry).where(WeightEntry.user_id == user_id)
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if until is not None:
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_, stop = utc_window(until, until, tz)
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stmt = stmt.where(WeightEntry.measured_at < stop)
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return db.scalar(stmt.order_by(WeightEntry.measured_at.desc()).limit(1))
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def create_weight(db: Session, user_id: int, payload: WeightEntryCreate) -> WeightEntry:
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existing = db.scalar(
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select(WeightEntry).where(
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@@ -7,6 +7,7 @@ identifiers (the UI maps them to French labels).
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"""
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import datetime as dt
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from collections import Counter
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from dataclasses import dataclass
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from zoneinfo import ZoneInfo
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@@ -181,6 +182,7 @@ def weight_stats(
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raw = [(d, w) for d, w in raw_all if start <= d <= end]
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trend = [(d, w) for d, w in trend_all if start <= d <= end]
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goal = service.active_goal(db, user_id)
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plan_end = plan_end_date(goal) if goal is not None else None
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series = [
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Series(
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@@ -208,7 +210,6 @@ def weight_stats(
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projection = calc.project_target_date(
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trend_now, float(goal.target_weight_kg), slope_30 or slope_14, end
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)
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plan_end = plan_end_date(goal)
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if plan_end is not None:
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series.append(
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Series(
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@@ -238,9 +239,15 @@ def weight_stats(
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if trend_now is not None and profile is not None
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else None
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)
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# « Dernière pesée » must be the most recent weigh-in, like /health/dashboard
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# — NOT raw_all[-1], which is the FIRST weigh-in of the most recent day (§5.5
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# governs the series, not this KPI).
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last_entry = service.latest_weight(db, user_id, tz, until=end)
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meta = {
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"trend_now_kg": _round(trend_now, 2),
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"last_weight_kg": _round(raw_all[-1][1], 2) if raw_all else None,
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"last_weight_kg": _round(float(last_entry.weight_kg), 2)
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if last_entry is not None
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else None,
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"slope_14d_kg_day": _round(slope_14, 4),
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"slope_14d_kg_week": _round(slope_14 * 7, 3) if slope_14 is not None else None,
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"slope_30d_kg_day": _round(slope_30, 4),
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@@ -250,9 +257,7 @@ def weight_stats(
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else None,
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"bmi": _round(bmi, 1),
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"target_weight_kg": float(goal.target_weight_kg) if goal else None,
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"plan_end_date": _iso(plan_end_date(goal))
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if goal and plan_end_date(goal)
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else None,
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"plan_end_date": _iso(plan_end) if plan_end is not None else None,
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"projection": {
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"status": projection.status,
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"date": _iso(projection.date) if projection.date else None,
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@@ -469,10 +474,20 @@ def energy_balance_stats(
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calc.trend_at_day(trend_all, start),
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calc.trend_at_day(trend_all, end),
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)
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methods = [m.tdee_method for m in models if m.tdee_method is not None]
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meta = {
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"tdee_methods": {
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_iso(m.day): m.tdee_method for m in models if m.tdee_method is not None
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},
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# How the TDEE average was obtained (dominant method of the window) and
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# the two figures the UI needs to spell it out ("BMR 1 750 × 1,55").
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"tdee_mode": Counter(methods).most_common(1)[0][0] if methods else None,
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"bmr_kcal": _round(
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next((m.bmr_kcal for m in reversed(models) if m.bmr_kcal is not None), None)
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),
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"activity_factor": calc.ACTIVITY_FACTORS[profile.activity_level.value]
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if profile is not None
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else None,
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"cumulative_balance_kcal": _round(running),
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"cumulative_kg_equivalent": _round(running / calc.KCAL_PER_KG_FAT, 2),
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# Daily deficit aimed at by the active goal on the last day of the range
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@@ -661,11 +676,13 @@ def nutrition_day_detail(
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water = sum(
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row.volume_ml for row in service.water_between(db, user_id, day, day, tz)
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)
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trend = calc.weight_trend(service.daily_weights(db, user_id, tz, until=day))
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return NutritionDayDetail(
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date=day,
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totals=totals,
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water_ml=water,
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water_goal_ml=profile.water_goal_ml if profile else None,
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protein_goal_g=calc.protein_goal_g(calc.trend_at_day(trend, day)),
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meals=list(meals.values()),
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)
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@@ -828,9 +845,7 @@ def dashboard(db: Session, user_id: int, tz: ZoneInfo) -> DashboardResponse:
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trend_all = calc.weight_trend(raw_all)
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trend_now = trend_all[-1][1] if trend_all else None
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trend_7d_ago = calc.trend_at_day(trend_all, today - dt.timedelta(days=7))
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last_entry = db.scalar(
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service.weights_query(user_id, None, None, tz, "-measured_at").limit(1)
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)
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last_entry = service.latest_weight(db, user_id, tz)
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cumulative = sum(m.balance_kcal for m in models if m.balance_kcal is not None)
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week_start = today - dt.timedelta(days=today.weekday())
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@@ -478,4 +478,6 @@ def _row(day: str, amount: str, label: str):
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booked_date=date.fromisoformat(day),
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value_date=None,
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amount=Decimal(amount),
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currency="EUR",
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label_raw=label,
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)
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@@ -145,6 +145,39 @@ def test_search_matches_the_brand_locally(
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assert [item["name"] for item in body["items"]] == ["Pomme, crue"]
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def test_a_partially_warm_cache_still_returns_a_full_page(
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client: TestClient, db: Session, auth_headers: dict[str, str]
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) -> None:
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"""OFF results overlap the local cache, so the page must be over-fetched.
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Regression: `page_size` used to be `limit - len(local)`, and the overlapping
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products were then skipped as already known — a warm cache under-delivered.
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"""
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for index in range(3):
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db.add(
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FoodItem(
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source="off", source_id=f"off-{index}", name=f"Pomme variété {index}"
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)
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)
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db.commit()
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def handler(request: httpx.Request) -> httpx.Response:
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size = int(request.url.params["page_size"])
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hits = [
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{
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"code": f"off-{index}",
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"product_name": f"Pomme variété {index}",
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"nutriments": {"energy-kcal_100g": 52},
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}
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for index in range(size)
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]
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return httpx.Response(200, json={"hits": hits, "count": size})
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use_transport(handler)
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body = client.get(URL, params={"q": "pomme", "limit": 5}, headers=auth_headers)
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assert len(body.json()["items"]) == 5
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def test_search_requires_two_characters(
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client: TestClient, auth_headers: dict[str, str]
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) -> None:
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@@ -12,7 +12,16 @@ Each test below fails on the code as it was before the matching fix:
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3. `/health/goals/active` reported `deficit_target_kcal` as
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`tdee_smoothed - budget`, i.e. the deficit left AFTER the calorie floor
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clamped the budget, instead of the deficit AIMED AT by the goal
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(§5.3 `BudgetResult.deficit_target = rate x 7700 / 7`).
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(§5.3 `BudgetResult.deficit_target = rate x 7700 / 7`);
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4. `/health/energy-balance` never told the UI HOW the TDEE was obtained
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(`tdee_mode`, `bmr_kcal`, `activity_factor`), so the « BMR x facteur »
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sub-label of the TDEE KPI could never render;
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5. `/health/nutrition/days/{day}` never sent a protein target, so the
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« Protéines aujourd'hui » gauge showed `96 / — g` for ever
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(ux-pages.md §15.3: default 1,6 g/kg of body weight);
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6. `/health/weights/stats` reported `last_weight_kg` as the FIRST weigh-in of
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the most recent day while `/health/dashboard` reported the LAST one — the
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same French label « dernière pesée » showing two different numbers.
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"""
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import datetime as dt
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@@ -273,3 +282,112 @@ def test_energy_balance_meta_exposes_the_target_deficit(
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headers=auth_headers,
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).json()["meta"]
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assert meta["deficit_target_kcal"] == 550.0 # 0.5 * 7700 / 7
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# --- 4. The TDEE KPI sub-label needs bmr / factor / mode ----------------------
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def test_energy_balance_meta_explains_how_the_tdee_was_obtained(
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client: TestClient, auth_headers: dict[str, str]
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) -> None:
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"""`tdee_mode` + `bmr_kcal` + `activity_factor` back the « BMR x facteur »
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sub-label of the TDEE KPI, which could never render without them."""
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_seed(client, auth_headers)
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today = _today()
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meta = client.get(
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f"{BASE}/energy-balance",
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params={
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"from": (today - dt.timedelta(days=6)).isoformat(),
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"to": today.isoformat(),
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},
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headers=auth_headers,
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).json()["meta"]
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assert meta["tdee_mode"] == "estimated" # no activity_daily seeded
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assert meta["activity_factor"] == 1.55 # profile activity_level = moderate
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assert meta["bmr_kcal"] is not None
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tdee_avg = meta["bmr_kcal"] * meta["activity_factor"]
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assert tdee_avg > 1500
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def test_energy_balance_meta_reports_a_measured_tdee_mode(
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client: TestClient, auth_headers: dict[str, str]
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) -> None:
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_seed(client, auth_headers)
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today = _today()
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for offset in range(7):
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day = today - dt.timedelta(days=offset)
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assert client.post(
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f"{BASE}/activity",
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json={"date": day.isoformat(), "steps": 12000, "active_kcal": 600},
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headers=auth_headers,
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).status_code in (200, 201)
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meta = client.get(
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f"{BASE}/energy-balance",
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params={
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"from": (today - dt.timedelta(days=6)).isoformat(),
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"to": today.isoformat(),
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},
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headers=auth_headers,
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).json()["meta"]
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assert meta["tdee_mode"] == "bmr_plus_active"
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# --- 5. The protein gauge of the nutrition journal ----------------------------
|
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def test_nutrition_day_detail_exposes_the_protein_goal(
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client: TestClient, auth_headers: dict[str, str]
|
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) -> None:
|
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"""« Protéines aujourd'hui : 96 / 130 g » needs a target (ux-pages §15.3,
|
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1,6 g/kg) — the journal endpoint never sent one, so the gauge stayed at —."""
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_seed(client, auth_headers)
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today = _today()
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body = client.get(
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f"{BASE}/nutrition/days/{today.isoformat()}", headers=auth_headers
|
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).json()
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trend = client.get(f"{BASE}/dashboard", headers=auth_headers).json()[
|
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"trend_weight_kg"
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]
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assert body["protein_goal_g"] == round(trend * 1.6, 1)
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def test_protein_goal_is_absent_without_any_weigh_in(
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client: TestClient, auth_headers: dict[str, str]
|
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) -> None:
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today = _today()
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body = client.get(
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f"{BASE}/nutrition/days/{today.isoformat()}", headers=auth_headers
|
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).json()
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assert body["protein_goal_g"] is None
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# --- 6. « Dernière pesée » must mean the same thing everywhere ----------------
|
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|
||||
def test_last_weight_is_the_most_recent_weigh_in_of_the_day(
|
||||
client: TestClient, auth_headers: dict[str, str]
|
||||
) -> None:
|
||||
"""/weights/stats reported the FIRST weigh-in of the last day (the series
|
||||
rule of §5.5) while /dashboard reported the LAST one — same French label,
|
||||
two different numbers."""
|
||||
assert (
|
||||
client.put(f"{BASE}/profile", json=PROFILE, headers=auth_headers).status_code
|
||||
== 200
|
||||
)
|
||||
yesterday = _today() - dt.timedelta(days=1)
|
||||
for hour, weight in ((7, 92.14), (18, 93.6)):
|
||||
assert (
|
||||
client.post(
|
||||
f"{BASE}/weights",
|
||||
json={"measured_at": _at(yesterday, hour), "weight_kg": weight},
|
||||
headers=auth_headers,
|
||||
).status_code
|
||||
== 201
|
||||
)
|
||||
stats = client.get(f"{BASE}/weights/stats", headers=auth_headers).json()
|
||||
dashboard = client.get(f"{BASE}/dashboard", headers=auth_headers).json()
|
||||
assert stats["meta"]["last_weight_kg"] == 93.6
|
||||
assert dashboard["weight_kg"] == 93.6
|
||||
# the trend series still uses the first weigh-in of the day (§5.5)
|
||||
raw = next(s for s in stats["series"] if s["name"] == "weight_raw")
|
||||
assert raw["points"][-1][1] == 92.14
|
||||
|
||||
@@ -225,6 +225,24 @@ def test_every_module_uses_the_single_error_envelope(client: TestClient) -> None
|
||||
assert error["message"].endswith("."), path
|
||||
|
||||
|
||||
def test_framework_errors_are_answered_in_french(client: TestClient) -> None:
|
||||
"""Starlette fills `detail` with an English reason phrase ("Not Found",
|
||||
"Method Not Allowed"); C1 forbids shipping it to the user."""
|
||||
unknown = client.get("/api/inconnu")
|
||||
assert unknown.status_code == 404
|
||||
assert unknown.json()["error"] == {
|
||||
"code": "not_found",
|
||||
"message": "Ressource introuvable.",
|
||||
"details": {},
|
||||
}
|
||||
|
||||
wrong_method = client.delete("/api/healthz")
|
||||
assert wrong_method.status_code == 405
|
||||
error = wrong_method.json()["error"]
|
||||
assert error["code"] == "method_not_allowed"
|
||||
assert error["message"] == "Méthode non autorisée pour cette ressource."
|
||||
|
||||
|
||||
# --- Home page: one dashboard per module (§7.1 of ux-pages) -------------------
|
||||
|
||||
|
||||
|
||||
@@ -170,7 +170,9 @@ def test_enum_columns_are_varchar_with_a_check_constraint() -> None:
|
||||
|
||||
def test_uuid_columns_use_the_native_postgresql_uuid_type() -> None:
|
||||
uuid_columns = [
|
||||
(table, column) for table, column in _columns() if isinstance(column.type, sa.Uuid)
|
||||
(table, column)
|
||||
for table, column in _columns()
|
||||
if isinstance(column.type, sa.Uuid)
|
||||
]
|
||||
assert len(uuid_columns) >= 15
|
||||
for table, column in uuid_columns:
|
||||
@@ -201,15 +203,13 @@ def test_every_datetime_column_is_timestamptz() -> None:
|
||||
def test_numeric_columns_declare_precision_and_scale() -> None:
|
||||
"""A bare NUMERIC on PostgreSQL stores any precision: money would drift."""
|
||||
for table, column in _columns():
|
||||
if not isinstance(column.type, sa.Numeric) or isinstance(
|
||||
column.type, sa.Float
|
||||
):
|
||||
if not isinstance(column.type, sa.Numeric) or isinstance(column.type, sa.Float):
|
||||
continue
|
||||
assert column.type.precision is not None, f"{table.name}.{column.name}"
|
||||
assert column.type.scale is not None, f"{table.name}.{column.name}"
|
||||
assert re.fullmatch(
|
||||
r"NUMERIC\(\d+, \d+\)", column.type.compile(dialect=PG)
|
||||
), f"{table.name}.{column.name}"
|
||||
assert re.fullmatch(r"NUMERIC\(\d+, \d+\)", column.type.compile(dialect=PG)), (
|
||||
f"{table.name}.{column.name}"
|
||||
)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
@@ -301,9 +301,7 @@ def test_index_and_unique_constraint_names_are_unique_schema_wide() -> None:
|
||||
for constraint in table.constraints
|
||||
if constraint.name is not None
|
||||
and not str(constraint.name).startswith("_unnamed_")
|
||||
and isinstance(
|
||||
constraint, sa.UniqueConstraint | sa.PrimaryKeyConstraint
|
||||
)
|
||||
and isinstance(constraint, sa.UniqueConstraint | sa.PrimaryKeyConstraint)
|
||||
]
|
||||
for name, owner in owners:
|
||||
assert name is not None
|
||||
|
||||
+15
-4
@@ -4050,7 +4050,7 @@
|
||||
"schema": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
"$ref": "#/components/schemas/SportType"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
@@ -4401,7 +4401,7 @@
|
||||
"schema": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
"$ref": "#/components/schemas/GoalStatus"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
@@ -5824,7 +5824,7 @@
|
||||
"schema": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "string"
|
||||
"$ref": "#/components/schemas/MealType"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
@@ -13750,6 +13750,17 @@
|
||||
],
|
||||
"title": "Water Goal Ml"
|
||||
},
|
||||
"protein_goal_g": {
|
||||
"anyOf": [
|
||||
{
|
||||
"type": "number"
|
||||
},
|
||||
{
|
||||
"type": "null"
|
||||
}
|
||||
],
|
||||
"title": "Protein Goal G"
|
||||
},
|
||||
"meals": {
|
||||
"items": {
|
||||
"$ref": "#/components/schemas/MealTotals"
|
||||
@@ -18278,4 +18289,4 @@
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
@@ -330,6 +330,10 @@ export interface WorkoutStatsMeta {
|
||||
/* Health — energy balance */
|
||||
/* ------------------------------------------------------------------ */
|
||||
|
||||
/** TDEE derivation, 3-tier rule of datamodel-health-vape.md §5.2. */
|
||||
export const TDEE_METHODS = ["measured_total", "bmr_plus_active", "estimated"] as const;
|
||||
export type TdeeMethod = (typeof TDEE_METHODS)[number];
|
||||
|
||||
export interface EnergyBalanceMeta {
|
||||
intake_avg?: number | null;
|
||||
tdee_avg?: number | null;
|
||||
@@ -338,9 +342,10 @@ export interface EnergyBalanceMeta {
|
||||
balance_cumulative?: number | null;
|
||||
budget_kcal?: number | null;
|
||||
deficit_target_kcal?: number | null;
|
||||
bmr?: number | null;
|
||||
bmr_kcal?: number | null;
|
||||
activity_factor?: number | null;
|
||||
tdee_mode?: "factor" | "measured" | null;
|
||||
/** Dominant TDEE method over the window — same vocabulary as the API. */
|
||||
tdee_mode?: TdeeMethod | null;
|
||||
tdee_methods?: Record<string, string> | null;
|
||||
expected_change_kg?: number | null;
|
||||
actual_change_kg?: number | null;
|
||||
@@ -472,7 +477,6 @@ export interface NutritionDaysMeta {
|
||||
protein_pct?: number | null;
|
||||
carbs_pct?: number | null;
|
||||
fat_pct?: number | null;
|
||||
protein_goal_g?: number | null;
|
||||
}
|
||||
|
||||
export interface NutritionDaysResponse {
|
||||
@@ -494,6 +498,8 @@ export interface NutritionDayDetail {
|
||||
carbs_g: number;
|
||||
fat_g: number;
|
||||
budget_kcal?: number | null;
|
||||
/** Daily protein target in grams (server-side, 1,6 g/kg of trend weight). */
|
||||
protein_goal_g?: number | null;
|
||||
}
|
||||
|
||||
export interface FoodFavoriteRead {
|
||||
@@ -868,6 +874,11 @@ export function useEnergyBalance(
|
||||
tdee_avg: seriesAverage(stats, "tdee_kcal"),
|
||||
budget_kcal: seriesLast(stats, "budget_kcal"),
|
||||
deficit_target_kcal: numberOrNull(meta.deficit_target_kcal),
|
||||
bmr_kcal: numberOrNull(meta.bmr_kcal),
|
||||
activity_factor: numberOrNull(meta.activity_factor),
|
||||
tdee_mode: TDEE_METHODS.includes(meta.tdee_mode as TdeeMethod)
|
||||
? (meta.tdee_mode as TdeeMethod)
|
||||
: null,
|
||||
balance_cumulative: numberOrNull(meta.cumulative_balance_kcal),
|
||||
expected_change_kg: numberOrNull(meta.expected_change_kg),
|
||||
actual_change_kg: numberOrNull(meta.actual_change_kg),
|
||||
@@ -1056,6 +1067,7 @@ interface NutritionDayDetailEnvelope {
|
||||
} | null;
|
||||
water_ml?: number;
|
||||
water_goal_ml?: number | null;
|
||||
protein_goal_g?: number | null;
|
||||
meals?: { meal: MealType; kcal?: number; entries?: FoodEntryRead[] }[];
|
||||
}
|
||||
|
||||
@@ -1093,6 +1105,7 @@ function normalizeDayDetail(date: string, res: NutritionDayDetailEnvelope): Nutr
|
||||
carbs_g: totals.carbs_g ?? sum((e) => e.carbs_g),
|
||||
fat_g: totals.fat_g ?? sum((e) => e.fat_g),
|
||||
budget_kcal: totals.budget_kcal ?? null,
|
||||
protein_goal_g: res.protein_goal_g ?? null,
|
||||
};
|
||||
}
|
||||
|
||||
|
||||
@@ -94,6 +94,20 @@ export default function EnergyBalancePage() {
|
||||
? -Math.abs(Number(meta.deficit_target_kcal))
|
||||
: null;
|
||||
|
||||
/** Sous-titre du KPI TDEE : comment la dépense a été obtenue (§5.2). */
|
||||
const tdeeModeLabel = useMemo(() => {
|
||||
const bmr = meta?.bmr_kcal;
|
||||
const labels = S.energy.tdeeMode;
|
||||
if (meta?.tdee_mode === "measured_total") return labels.measured_total;
|
||||
if (bmr === null || bmr === undefined) return undefined;
|
||||
const bmrText = formatNumber(Math.round(Number(bmr)));
|
||||
if (meta?.tdee_mode === "bmr_plus_active") return labels.bmr_plus_active(bmrText);
|
||||
if (meta?.tdee_mode === "estimated" && meta.activity_factor) {
|
||||
return labels.estimated(bmrText, formatNumber(Number(meta.activity_factor), 2));
|
||||
}
|
||||
return undefined;
|
||||
}, [meta]);
|
||||
|
||||
/* --- KPI --------------------------------------------------------- */
|
||||
|
||||
const today = todayIso();
|
||||
@@ -470,16 +484,7 @@ export default function EnergyBalancePage() {
|
||||
? S.common.none
|
||||
: `${formatNumber(Math.round(Number(meta.tdee_avg)))} kcal/j`
|
||||
}
|
||||
sub={
|
||||
meta?.tdee_mode === "measured"
|
||||
? "BMR + actives mesurées"
|
||||
: meta?.bmr
|
||||
? `BMR ${formatNumber(Math.round(Number(meta.bmr)))} × ${formatNumber(
|
||||
Number(meta.activity_factor ?? 1),
|
||||
2,
|
||||
)}`
|
||||
: undefined
|
||||
}
|
||||
sub={tdeeModeLabel}
|
||||
/>
|
||||
<StatCard
|
||||
label={S.energy.kpi.budget}
|
||||
@@ -560,6 +565,7 @@ export default function EnergyBalancePage() {
|
||||
{methodOpen ? (
|
||||
<div className="mt-3 space-y-3 text-sm text-ink-secondary">
|
||||
<p>{S.energy.method.text}</p>
|
||||
<p>{S.energy.method.workoutsNote}</p>
|
||||
{meta?.tdee_adaptive_kcal ? (
|
||||
<div className="flex flex-wrap items-center gap-3">
|
||||
<p className="text-ink">
|
||||
|
||||
@@ -131,7 +131,7 @@ export default function NutritionPage() {
|
||||
}, [days, meta]);
|
||||
|
||||
const proteinToday = journal.data?.protein_g ?? todayRow?.protein_g ?? null;
|
||||
const proteinGoal = meta?.protein_goal_g ?? null;
|
||||
const proteinGoal = journal.data?.protein_goal_g ?? null;
|
||||
|
||||
const splitToday = useMemo(() => {
|
||||
const p = Number(journal.data?.protein_g ?? 0) * KCAL_PER_G.protein;
|
||||
|
||||
@@ -313,9 +313,17 @@ export const S = {
|
||||
theoreticalWeight: "Poids théorique",
|
||||
targetDeficit: "Cible",
|
||||
},
|
||||
/** Sous-titre du KPI « TDEE estimé » — comment la dépense a été obtenue (§5.2). */
|
||||
tdeeMode: {
|
||||
measured_total: "Dépense totale mesurée",
|
||||
bmr_plus_active: (bmr: string) => `BMR ${bmr} + calories actives mesurées`,
|
||||
estimated: (bmr: string, factor: string) => `BMR ${bmr} × ${factor}`,
|
||||
},
|
||||
method: {
|
||||
title: "Méthode & calibration",
|
||||
text: "Le métabolisme de base (BMR) est calculé avec la formule Mifflin-St Jeor à partir du poids de tendance du jour. La dépense totale (TDEE) applique ensuite votre facteur d'activité, ou ajoute vos calories actives mesurées. Les conversions énergie ↔ masse utilisent 7 700 kcal par kilogramme.",
|
||||
workoutsNote:
|
||||
"Les séances de sport ne sont pas ajoutées à la dépense du jour : seule l'activité quotidienne (pas, calories actives, dépense totale) alimente le TDEE. Une séance saisie sans donnée d'activité correspondante reste donc invisible dans cette balance.",
|
||||
calibration: "Calibration",
|
||||
insufficient: "Pas encore assez de jours suivis pour calibrer le modèle (21 jours minimum).",
|
||||
},
|
||||
|
||||
@@ -128,6 +128,9 @@ export function StepVerify({
|
||||
formatNumber(preview.rows_total),
|
||||
formatNumber(preview.would_skip_duplicates),
|
||||
formatNumber(preview.rows_error),
|
||||
preview.rows_skipped_filtered
|
||||
? formatNumber(preview.rows_skipped_filtered)
|
||||
: undefined,
|
||||
)}
|
||||
</span>
|
||||
{preview.date_min && preview.date_max ? (
|
||||
|
||||
@@ -90,8 +90,10 @@ export const strings = {
|
||||
|
||||
preview: {
|
||||
title: "Aperçu des 20 premières lignes",
|
||||
summary: (read: string, duplicates: string, errors: string) =>
|
||||
`${read} lignes lues · ${duplicates} doublons ignorés (déjà importés) · ${errors} lignes en erreur`,
|
||||
summary: (read: string, duplicates: string, errors: string, filtered?: string) =>
|
||||
`${read} lignes lues · ${duplicates} doublons ignorés (déjà importés)` +
|
||||
(filtered ? ` · ${filtered} lignes écartées par le profil` : "") +
|
||||
` · ${errors} lignes en erreur`,
|
||||
period: "Période couverte",
|
||||
colDate: "Date",
|
||||
colLabel: "Libellé",
|
||||
|
||||
Reference in New Issue
Block a user