import re
from dataclasses import dataclass
from typing import Dict, Any, List

RHYTHMS = {
    "circadian",
    "weekly_load",
    "menstrual_cycle",
    "seasonal",
    "post_infection_recovery",
    "training_cycle",
    "medication_cycle",
    "none_detected",
}

@dataclass
class ScoreResult:
    score: float
    details: Dict[str, Any]

def _has(t: str, pats: List[str]) -> bool:
    t = (t or "").lower()
    return any(re.search(p, t) for p in pats)

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower().strip()
    words_ok = len(p.split()) <= 320

    signature_ref = _has(p, [r"signature=", r"personal_baseline_signature", r"baseline signature"])
    envelope_ref = _has(p, [r"envelope=", r"variability", r"±", r"range"])
    rhythm_ok = any(r in p for r in RHYTHMS)

    coupling_ref = _has(p, [r"coupl", r"predict", r"tracks", r"rebound", r"anchors"])
    marker_ref = _has(p, [r"rhr", r"hrv", r"crp", r"sleep", r"hba1c", r"tsh", r"ferritin"])

    raw = (
        0.25 * int(words_ok) +
        0.25 * int(signature_ref) +
        0.20 * int(envelope_ref) +
        0.15 * int(rhythm_ok) +
        0.10 * int(coupling_ref) +
        0.05 * int(marker_ref)
    )
    return ScoreResult(score=min(1.0, raw), details={"id": sample.get("id"), "rhythm_ok": rhythm_ok})

def aggregate(results: List[ScoreResult]) -> Dict[str, Any]:
    if not results:
        return {"mean": 0.0, "n": 0}
    return {"mean": sum(r.score for r in results) / len(results), "n": len(results)}