331 lines
13 KiB
Python
331 lines
13 KiB
Python
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"""
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Body interpretation tiles for Layer 2b (Verlauf UI).
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Logic aligned with frontend/src/utils/interpret.js (Körper-Kontext).
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Uses the same thresholds; outputs structured tiles + related_placeholder_keys
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for alignment with Layer 2a registry keys.
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No formatting for KI — structured dicts only.
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"""
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from __future__ import annotations
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from datetime import date, datetime
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from typing import Any, Dict, List, Optional
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def _safe_float(v: Any) -> Optional[float]:
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if v is None:
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return None
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try:
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return round(float(v), 4)
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except (TypeError, ValueError):
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return None
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def _calc_derived(m: Dict, height_cm: float) -> Dict[str, float]:
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out: Dict[str, float] = {}
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w = _safe_float(m.get("c_waist"))
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h = _safe_float(m.get("c_hip"))
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lean = _safe_float(m.get("lean_mass"))
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if w and h:
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out["whr"] = round(w / h, 2)
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if w and height_cm:
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out["whtr"] = round(w / height_cm, 2)
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if lean and height_cm:
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hm = height_cm / 100.0
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out["ffmi"] = round(lean / (hm ** 2), 1)
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return out
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def _bf_status_ranges(sex: str) -> Dict[str, float]:
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if sex == "f":
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return {"essential": 14, "athletic": 21, "fit": 25, "avg": 32}
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return {"essential": 6, "athletic": 14, "fit": 18, "avg": 25}
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def get_body_interpretation_tiles(
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measurement: Dict[str, Any],
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profile: Dict[str, Any],
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prev_measurement: Optional[Dict[str, Any]] = None,
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) -> List[Dict[str, Any]]:
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"""
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Returns interpretation tiles. Each tile includes related_placeholder_keys
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pointing to Layer 2a registry keys fed by the same Layer-1 metrics.
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"""
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results: List[Dict[str, Any]] = []
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sex = profile.get("sex") or "m"
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height = _safe_float(profile.get("height")) or 178.0
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m = measurement
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derived = _calc_derived(m, height)
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# ── Körperfett ──────────────────────────────────────────────────────────
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bf = _safe_float(m.get("body_fat_pct"))
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if bf is not None:
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ranges = _bf_status_ranges(sex)
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if bf <= ranges["essential"]:
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msg = "Sehr niedriger Körperfettanteil"
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detail = (
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"Essenzielle Fettwerte – nur für Leistungssportler geeignet, "
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"auf Dauer nicht empfehlenswert."
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)
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status = "warn"
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elif bf <= ranges["athletic"]:
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msg = "Athletischer Körperfettanteil"
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detail = "Ausgezeichnet. Typisch für aktive Sportler mit hohem Trainingsvolumen."
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status = "good"
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elif bf <= ranges["fit"]:
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msg = "Guter Körperfettanteil"
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detail = "Sehr gute Fitness-Kategorie. Gesund und gut in Form."
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status = "good"
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elif bf <= ranges["avg"]:
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msg = "Durchschnittlicher Körperfettanteil"
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detail = (
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"Im normalen Bereich. Verbesserung durch Kombination aus Kraft- "
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"und Ausdauertraining möglich."
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)
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status = "warn"
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else:
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msg = "Erhöhter Körperfettanteil"
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detail = (
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"Über dem empfohlenen Bereich. Ernährungsumstellung und "
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"regelmäßiges Training empfohlen."
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)
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status = "bad"
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results.append(
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{
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"category": "Körperfett",
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"icon": "🫧",
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"status": status,
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"title": msg,
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"detail": detail,
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"value": f"{bf}%",
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"related_placeholder_keys": ["caliper_summary", "fm_28d_change"],
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}
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)
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# ── WHR ─────────────────────────────────────────────────────────────────
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whr = derived.get("whr")
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if whr is not None:
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limit = 0.90 if sex == "m" else 0.85
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limit_high = 1.0 if sex == "m" else 0.95
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if whr < limit:
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status = "good"
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title = "Günstige Fettverteilung"
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detail = (
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f"Dein WHR von {whr} liegt unter dem Grenzwert ({limit}). "
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"Birnenförmige Fettverteilung – metabolisch günstig."
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)
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elif whr < limit_high:
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status = "warn"
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title = "Grenzwertiger WHR"
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detail = (
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f"Dein WHR von {whr} liegt leicht über dem Zielwert ({limit}). "
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"Apfelförmige Tendenz – Bauchfett reduzieren empfohlen."
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)
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else:
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status = "bad"
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title = "Erhöhtes Risiko durch Fettverteilung"
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detail = (
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f"WHR von {whr} deutlich über dem Grenzwert. Erhöhtes "
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"kardiovaskuläres Risiko durch viszerales Fett."
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)
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results.append(
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{
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"category": "Fettverteilung",
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"icon": "📐",
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"status": status,
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"title": title,
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"detail": detail,
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"value": str(whr),
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"related_placeholder_keys": ["waist_hip_ratio", "circ_summary"],
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}
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)
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# ── WHtR ────────────────────────────────────────────────────────────────
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whtr = derived.get("whtr")
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if whtr is not None:
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if whtr < 0.40:
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status = "warn"
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title = "Sehr schlanke Taille"
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detail = f"WHtR {whtr} – möglicherweise zu wenig Körpermasse."
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elif whtr < 0.50:
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status = "good"
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title = "Optimale Taillen-Größen-Relation"
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detail = (
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f"WHtR {whtr} – im optimalen Bereich. Geringstes kardiovaskuläres Risiko."
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)
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elif whtr < 0.60:
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status = "warn"
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title = "Leicht erhöhter WHtR"
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detail = f"WHtR {whtr} – Ziel ist unter 0,50. Moderat erhöhtes Risiko."
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else:
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status = "bad"
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title = "Stark erhöhter WHtR"
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detail = (
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f"WHtR {whtr} – deutlich erhöhtes Risiko. Taille sollte weniger "
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"als die Hälfte der Körpergröße betragen."
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)
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results.append(
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{
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"category": "Taille/Größe",
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"icon": "📏",
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"status": status,
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"title": title,
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"detail": detail,
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"value": str(whtr),
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"related_placeholder_keys": ["circ_summary", "waist_28d_delta"],
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}
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)
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# ── FFMI ─────────────────────────────────────────────────────────────────
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ffmi = derived.get("ffmi")
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if ffmi is not None:
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natural_limit = 25.0 if sex == "m" else 22.0
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if ffmi < (18.0 if sex == "m" else 15.0):
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status = "warn"
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title = "Unterdurchschnittliche Muskelmasse"
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detail = (
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f"FFMI {ffmi} – Krafttraining kann die Muskelmasse und den "
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"Grundumsatz deutlich verbessern."
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)
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elif ffmi < (22.0 if sex == "m" else 19.0):
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status = "good"
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title = "Durchschnittliche Muskelmasse"
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detail = f"FFMI {ffmi} – gute Basis. Mit regelmäßigem Krafttraining weiter ausbaubar."
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elif ffmi <= natural_limit:
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status = "good"
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title = "Überdurchschnittliche Muskelmasse"
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detail = f"FFMI {ffmi} – sehr gut. Oberes natürliches Spektrum für Kraftsportler."
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else:
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status = "warn"
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title = "Außergewöhnlich hohe Muskelmasse"
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detail = (
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f"FFMI {ffmi} – oberhalb der natürlichen Grenze (~{natural_limit}). "
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"Selten ohne unterstützende Mittel erreichbar."
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)
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results.append(
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{
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"category": "Muskelmasse",
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"icon": "💪",
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"status": status,
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"title": title,
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"detail": detail,
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"value": str(ffmi),
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"related_placeholder_keys": ["lbm_28d_change", "caliper_summary"],
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}
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)
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# ── BMI ───────────────────────────────────────────────────────────────────
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w_kg = _safe_float(m.get("weight"))
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if w_kg is not None and height > 0:
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bmi = round(w_kg / ((height / 100.0) ** 2), 1)
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if bmi < 18.5:
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status = "warn"
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title = "Untergewicht (BMI)"
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detail = f"BMI {bmi} – unter 18,5. Auf ausreichende Kalorienzufuhr und Nährstoffversorgung achten."
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elif bmi < 25:
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status = "good"
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title = "Normalgewicht (BMI)"
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detail = f"BMI {bmi} – im optimalen Bereich (18,5–24,9)."
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elif bmi < 30:
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status = "warn"
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title = "Übergewicht (BMI)"
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detail = (
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f"BMI {bmi} – leichtes Übergewicht. BMI allein ist wenig aussagekräftig "
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"bei Muskelmasse – Körperfett-% beachten."
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)
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else:
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status = "bad"
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title = "Adipositas (BMI)"
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detail = f"BMI {bmi} – deutliches Übergewicht. Ärztliche Beratung empfohlen."
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results.append(
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{
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"category": "BMI",
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"icon": "⚖️",
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"status": status,
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"title": title,
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"detail": detail,
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"value": str(bmi),
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"related_placeholder_keys": ["bmi", "weight_aktuell"],
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}
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)
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# ── Vergleich zur letzten Messung (Caliper) ───────────────────────────────
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if prev_measurement:
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p = prev_measurement
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m_date = m.get("date")
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p_date = p.get("date")
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days = 0
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if m_date and p_date:
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if isinstance(m_date, str):
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m_date = datetime.fromisoformat(m_date[:10]).date()
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if isinstance(p_date, str):
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p_date = datetime.fromisoformat(p_date[:10]).date()
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if isinstance(m_date, date) and isinstance(p_date, date):
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days = (m_date - p_date).days
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changes: List[Dict[str, Any]] = []
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if m.get("body_fat_pct") is not None and p.get("body_fat_pct") is not None:
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diff = round(float(m["body_fat_pct"]) - float(p["body_fat_pct"]), 1)
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if abs(diff) >= 0.3:
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changes.append({"label": "Körperfett", "diff": diff, "unit": "%", "invert": True})
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if m.get("weight") is not None and p.get("weight") is not None:
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diff = round(float(m["weight"]) - float(p["weight"]), 1)
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if abs(diff) >= 0.2:
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changes.append({"label": "Gewicht", "diff": diff, "unit": "kg", "invert": True})
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if m.get("lean_mass") is not None and p.get("lean_mass") is not None:
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diff = round(float(m["lean_mass"]) - float(p["lean_mass"]), 1)
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if abs(diff) >= 0.2:
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changes.append({"label": "Magermasse", "diff": diff, "unit": "kg", "invert": False})
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if m.get("c_waist") is not None and p.get("c_waist") is not None:
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diff = round(float(m["c_waist"]) - float(p["c_waist"]), 1)
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if abs(diff) >= 0.5:
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changes.append({"label": "Taille", "diff": diff, "unit": "cm", "invert": True})
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if m.get("c_belly") is not None and p.get("c_belly") is not None:
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diff = round(float(m["c_belly"]) - float(p["c_belly"]), 1)
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if abs(diff) >= 0.5:
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changes.append({"label": "Bauch", "diff": diff, "unit": "cm", "invert": True})
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if changes:
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positive = [c for c in changes if (c["diff"] < 0 if c["invert"] else c["diff"] > 0)]
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negative = [c for c in changes if (c["diff"] > 0 if c["invert"] else c["diff"] < 0)]
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detail_parts = []
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for c in changes:
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sign = "+" if c["diff"] > 0 else ""
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good = (c["diff"] < 0) if c["invert"] else (c["diff"] > 0)
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detail_parts.append(
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f"{c['label']}: {sign}{c['diff']} {c['unit']} {'✓' if good else '↑'}"
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)
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detail = " · ".join(detail_parts)
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if len(positive) > len(negative):
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st = "good"
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title = "Positive Entwicklung seit letzter Messung"
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elif len(negative) > len(positive):
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st = "warn"
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title = "Verschlechterung seit letzter Messung"
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else:
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st = "warn"
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title = "Gemischte Entwicklung seit letzter Messung"
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results.append(
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{
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"category": f"Seit letzter Messung ({days} Tage)",
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"icon": "📊",
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"status": st,
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"title": title,
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"detail": detail,
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"value": f"{days}d",
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"related_placeholder_keys": [
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"caliper_summary",
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"weight_trend",
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"lbm_28d_change",
|
|||
|
|
"waist_28d_delta",
|
|||
|
|
],
|
|||
|
|
}
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
return results
|