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

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

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    p = (prediction or "").lower()
    words_ok = len(p.split()) <= 750

    has_recipe = "recipe" in p or "protocol" in p or "package" in p
    has_alignment = "constraint" in p or "alignment" in p or "break" in p
    has_softening = "soften" in p or "basin" in p or "trajectory" in p
    has_stability = "stability" in p or "perturb" in p or "fails if" in p
    has_tox = "tox" in p or "safety" in p or "risk" in p
    has_validation = "trial" in p or "endpoint" in p or "validate" in p

    raw = (
        0.15 * int(words_ok) +
        0.20 * int(has_recipe) +
        0.20 * int(has_alignment) +
        0.15 * int(has_softening) +
        0.15 * int(has_stability) +
        0.05 * int(has_tox) +
        0.10 * int(has_validation)
    )
    return ScoreResult(score=min(1.0, raw), details={"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)}