"""
Body interpretation tiles for Layer 2b (Verlauf UI).

Logic aligned with frontend/src/utils/interpret.js (Körper-Kontext).
Uses the same thresholds; outputs structured tiles + related_placeholder_keys
for alignment with Layer 2a registry keys.

No formatting for KI — structured dicts only.
"""

from __future__ import annotations

from datetime import date, datetime
from typing import Any, Dict, List, Optional


def _safe_float(v: Any) -> Optional[float]:
    if v is None:
        return None
    try:
        return round(float(v), 4)
    except (TypeError, ValueError):
        return None


def _calc_derived(m: Dict, height_cm: float) -> Dict[str, float]:
    out: Dict[str, float] = {}
    w = _safe_float(m.get("c_waist"))
    h = _safe_float(m.get("c_hip"))
    lean = _safe_float(m.get("lean_mass"))
    if w and h:
        out["whr"] = round(w / h, 2)
    if w and height_cm:
        out["whtr"] = round(w / height_cm, 2)
    if lean and height_cm:
        hm = height_cm / 100.0
        out["ffmi"] = round(lean / (hm ** 2), 1)
    return out


def _bf_status_ranges(sex: str) -> Dict[str, float]:
    if sex == "f":
        return {"essential": 14, "athletic": 21, "fit": 25, "avg": 32}
    return {"essential": 6, "athletic": 14, "fit": 18, "avg": 25}


def get_body_interpretation_tiles(
    measurement: Dict[str, Any],
    profile: Dict[str, Any],
    prev_measurement: Optional[Dict[str, Any]] = None,
) -> List[Dict[str, Any]]:
    """
    Returns interpretation tiles. Each tile includes related_placeholder_keys
    pointing to Layer 2a registry keys fed by the same Layer-1 metrics.
    """
    results: List[Dict[str, Any]] = []
    sex = profile.get("sex") or "m"
    height = _safe_float(profile.get("height")) or 178.0

    m = measurement
    derived = _calc_derived(m, height)

    # ── Körperfett ──────────────────────────────────────────────────────────
    bf = _safe_float(m.get("body_fat_pct"))
    if bf is not None:
        ranges = _bf_status_ranges(sex)
        if bf <= ranges["essential"]:
            msg = "Sehr niedriger Körperfettanteil"
            detail = (
                "Essenzielle Fettwerte – nur für Leistungssportler geeignet, "
                "auf Dauer nicht empfehlenswert."
            )
            status = "warn"
        elif bf <= ranges["athletic"]:
            msg = "Athletischer Körperfettanteil"
            detail = "Ausgezeichnet. Typisch für aktive Sportler mit hohem Trainingsvolumen."
            status = "good"
        elif bf <= ranges["fit"]:
            msg = "Guter Körperfettanteil"
            detail = "Sehr gute Fitness-Kategorie. Gesund und gut in Form."
            status = "good"
        elif bf <= ranges["avg"]:
            msg = "Durchschnittlicher Körperfettanteil"
            detail = (
                "Im normalen Bereich. Verbesserung durch Kombination aus Kraft- "
                "und Ausdauertraining möglich."
            )
            status = "warn"
        else:
            msg = "Erhöhter Körperfettanteil"
            detail = (
                "Über dem empfohlenen Bereich. Ernährungsumstellung und "
                "regelmäßiges Training empfohlen."
            )
            status = "bad"

        results.append(
            {
                "category": "Körperfett",
                "icon": "🫧",
                "status": status,
                "title": msg,
                "detail": detail,
                "value": f"{bf}%",
                "related_placeholder_keys": ["caliper_summary", "fm_28d_change"],
            }
        )

    # ── WHR ─────────────────────────────────────────────────────────────────
    whr = derived.get("whr")
    if whr is not None:
        limit = 0.90 if sex == "m" else 0.85
        limit_high = 1.0 if sex == "m" else 0.95
        if whr < limit:
            status = "good"
            title = "Günstige Fettverteilung"
            detail = (
                f"Dein WHR von {whr} liegt unter dem Grenzwert ({limit}). "
                "Birnenförmige Fettverteilung – metabolisch günstig."
            )
        elif whr < limit_high:
            status = "warn"
            title = "Grenzwertiger WHR"
            detail = (
                f"Dein WHR von {whr} liegt leicht über dem Zielwert ({limit}). "
                "Apfelförmige Tendenz – Bauchfett reduzieren empfohlen."
            )
        else:
            status = "bad"
            title = "Erhöhtes Risiko durch Fettverteilung"
            detail = (
                f"WHR von {whr} deutlich über dem Grenzwert. Erhöhtes "
                "kardiovaskuläres Risiko durch viszerales Fett."
            )
        results.append(
            {
                "category": "Fettverteilung",
                "icon": "📐",
                "status": status,
                "title": title,
                "detail": detail,
                "value": str(whr),
                "related_placeholder_keys": ["waist_hip_ratio", "circ_summary"],
            }
        )

    # ── WHtR ────────────────────────────────────────────────────────────────
    whtr = derived.get("whtr")
    if whtr is not None:
        if whtr < 0.40:
            status = "warn"
            title = "Sehr schlanke Taille"
            detail = f"WHtR {whtr} – möglicherweise zu wenig Körpermasse."
        elif whtr < 0.50:
            status = "good"
            title = "Optimale Taillen-Größen-Relation"
            detail = (
                f"WHtR {whtr} – im optimalen Bereich. Geringstes kardiovaskuläres Risiko."
            )
        elif whtr < 0.60:
            status = "warn"
            title = "Leicht erhöhter WHtR"
            detail = f"WHtR {whtr} – Ziel ist unter 0,50. Moderat erhöhtes Risiko."
        else:
            status = "bad"
            title = "Stark erhöhter WHtR"
            detail = (
                f"WHtR {whtr} – deutlich erhöhtes Risiko. Taille sollte weniger "
                "als die Hälfte der Körpergröße betragen."
            )
        results.append(
            {
                "category": "Taille/Größe",
                "icon": "📏",
                "status": status,
                "title": title,
                "detail": detail,
                "value": str(whtr),
                "related_placeholder_keys": ["circ_summary", "waist_28d_delta"],
            }
        )

    # ── FFMI ─────────────────────────────────────────────────────────────────
    ffmi = derived.get("ffmi")
    if ffmi is not None:
        natural_limit = 25.0 if sex == "m" else 22.0
        if ffmi < (18.0 if sex == "m" else 15.0):
            status = "warn"
            title = "Unterdurchschnittliche Muskelmasse"
            detail = (
                f"FFMI {ffmi} – Krafttraining kann die Muskelmasse und den "
                "Grundumsatz deutlich verbessern."
            )
        elif ffmi < (22.0 if sex == "m" else 19.0):
            status = "good"
            title = "Durchschnittliche Muskelmasse"
            detail = f"FFMI {ffmi} – gute Basis. Mit regelmäßigem Krafttraining weiter ausbaubar."
        elif ffmi <= natural_limit:
            status = "good"
            title = "Überdurchschnittliche Muskelmasse"
            detail = f"FFMI {ffmi} – sehr gut. Oberes natürliches Spektrum für Kraftsportler."
        else:
            status = "warn"
            title = "Außergewöhnlich hohe Muskelmasse"
            detail = (
                f"FFMI {ffmi} – oberhalb der natürlichen Grenze (~{natural_limit}). "
                "Selten ohne unterstützende Mittel erreichbar."
            )
        results.append(
            {
                "category": "Muskelmasse",
                "icon": "💪",
                "status": status,
                "title": title,
                "detail": detail,
                "value": str(ffmi),
                "related_placeholder_keys": ["lbm_28d_change", "caliper_summary"],
            }
        )

    # ── BMI ───────────────────────────────────────────────────────────────────
    w_kg = _safe_float(m.get("weight"))
    if w_kg is not None and height > 0:
        bmi = round(w_kg / ((height / 100.0) ** 2), 1)
        if bmi < 18.5:
            status = "warn"
            title = "Untergewicht (BMI)"
            detail = f"BMI {bmi} – unter 18,5. Auf ausreichende Kalorienzufuhr und Nährstoffversorgung achten."
        elif bmi < 25:
            status = "good"
            title = "Normalgewicht (BMI)"
            detail = f"BMI {bmi} – im optimalen Bereich (18,5–24,9)."
        elif bmi < 30:
            status = "warn"
            title = "Übergewicht (BMI)"
            detail = (
                f"BMI {bmi} – leichtes Übergewicht. BMI allein ist wenig aussagekräftig "
                "bei Muskelmasse – Körperfett-% beachten."
            )
        else:
            status = "bad"
            title = "Adipositas (BMI)"
            detail = f"BMI {bmi} – deutliches Übergewicht. Ärztliche Beratung empfohlen."
        results.append(
            {
                "category": "BMI",
                "icon": "⚖️",
                "status": status,
                "title": title,
                "detail": detail,
                "value": str(bmi),
                "related_placeholder_keys": ["bmi", "weight_aktuell"],
            }
        )

    # ── Vergleich zur letzten Messung (Caliper) ───────────────────────────────
    if prev_measurement:
        p = prev_measurement
        m_date = m.get("date")
        p_date = p.get("date")
        days = 0
        if m_date and p_date:
            if isinstance(m_date, str):
                m_date = datetime.fromisoformat(m_date[:10]).date()
            if isinstance(p_date, str):
                p_date = datetime.fromisoformat(p_date[:10]).date()
            if isinstance(m_date, date) and isinstance(p_date, date):
                days = (m_date - p_date).days

        changes: List[Dict[str, Any]] = []
        if m.get("body_fat_pct") is not None and p.get("body_fat_pct") is not None:
            diff = round(float(m["body_fat_pct"]) - float(p["body_fat_pct"]), 1)
            if abs(diff) >= 0.3:
                changes.append({"label": "Körperfett", "diff": diff, "unit": "%", "invert": True})
        if m.get("weight") is not None and p.get("weight") is not None:
            diff = round(float(m["weight"]) - float(p["weight"]), 1)
            if abs(diff) >= 0.2:
                changes.append({"label": "Gewicht", "diff": diff, "unit": "kg", "invert": True})
        if m.get("lean_mass") is not None and p.get("lean_mass") is not None:
            diff = round(float(m["lean_mass"]) - float(p["lean_mass"]), 1)
            if abs(diff) >= 0.2:
                changes.append({"label": "Magermasse", "diff": diff, "unit": "kg", "invert": False})
        if m.get("c_waist") is not None and p.get("c_waist") is not None:
            diff = round(float(m["c_waist"]) - float(p["c_waist"]), 1)
            if abs(diff) >= 0.5:
                changes.append({"label": "Taille", "diff": diff, "unit": "cm", "invert": True})
        if m.get("c_belly") is not None and p.get("c_belly") is not None:
            diff = round(float(m["c_belly"]) - float(p["c_belly"]), 1)
            if abs(diff) >= 0.5:
                changes.append({"label": "Bauch", "diff": diff, "unit": "cm", "invert": True})

        if changes:
            positive = [c for c in changes if (c["diff"] < 0 if c["invert"] else c["diff"] > 0)]
            negative = [c for c in changes if (c["diff"] > 0 if c["invert"] else c["diff"] < 0)]
            detail_parts = []
            for c in changes:
                sign = "+" if c["diff"] > 0 else ""
                good = (c["diff"] < 0) if c["invert"] else (c["diff"] > 0)
                detail_parts.append(
                    f"{c['label']}: {sign}{c['diff']} {c['unit']} {'✓' if good else '↑'}"
                )
            detail = " · ".join(detail_parts)
            if len(positive) > len(negative):
                st = "good"
                title = "Positive Entwicklung seit letzter Messung"
            elif len(negative) > len(positive):
                st = "warn"
                title = "Verschlechterung seit letzter Messung"
            else:
                st = "warn"
                title = "Gemischte Entwicklung seit letzter Messung"

            results.append(
                {
                    "category": f"Seit letzter Messung ({days} Tage)",
                    "icon": "📊",
                    "status": st,
                    "title": title,
                    "detail": detail,
                    "value": f"{days}d",
                    "related_placeholder_keys": [
                        "caliper_summary",
                        "weight_trend",
                        "lbm_28d_change",
                        "waist_28d_delta",
                    ],
                }
            )

    return results