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

BASINS = {
    "basin_a_inflammatory_autonomic",
    "basin_b_mito_metabolic_fatigue",
    "basin_c_neuroimmune_cognitive",
    "basin_d_mast_cell_histamine_like",
    "basin_e_autoimmune_multisystem",
}

@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 _extract_ints(text: str) -> List[int]:
    return [int(x) for x in re.findall(r"\b\d{1,3}\b", text) if 0 <= int(x) <= 100]

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower().strip()
    words_ok = len(p.split()) <= 360

    basin_ok = any(b in p for b in BASINS)
    nums = _extract_ints(p)
    has_score = any(0 <= n <= 100 for n in nums)

    features_ref = _has(p, [r"top5", r"features", r"pem", r"pots", r"il6", r"ana", r"tryptase", r"esr"])
    evidence_ref = _has(p, [r"because", r"domin", r"pattern", r"defines", r"stable"])

    raw = (
        0.30 * int(basin_ok) +
        0.20 * int(has_score) +
        0.20 * int(features_ref) +
        0.20 * int(evidence_ref) +
        0.10 * int(words_ok)
    )
    return ScoreResult(score=min(1.0, raw), details={"basin_ok": basin_ok, "id": sample.get("id")})

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)}