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

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

BANDS = {"low", "medium", "high"}

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    try:
        pred = json.loads(prediction)
        cs = float(pred.get("coherence_score", -1))
        band = str(pred.get("risk_band", "")).strip().lower()
    except Exception:
        return ScoreResult(0.0, {"error":"parse_fail","id":sample.get("id")})

    true_cs_raw = sample.get("coherence_score", "")
    true_band_raw = sample.get("stochastic_risk_band", "")

    try:
        true_cs = float(true_cs_raw) if true_cs_raw not in ("", None) else None
    except Exception:
        true_cs = None

    true_band = str(true_band_raw).strip().lower() if true_band_raw not in ("", None) else ""

    # format-only if no ground truth
    if true_cs is None or true_band == "":
        ok = (0.0 <= cs <= 1.0) and (band in BANDS)
        return ScoreResult(1.0 if ok else 0.0, {"mode":"format_only","id":sample.get("id")})

    cs_err = abs(true_cs - cs)
    cs_score = max(0.0, 1.0 - cs_err)
    band_score = 1.0 if band == true_band else 0.0

    total = 0.65 * cs_score + 0.35 * band_score
    return ScoreResult(total, {"id":sample.get("id"),"cs":cs,"true_cs":true_cs,"band":band,"true_band":true_band})

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)}