scorer.py

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

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

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

def score(sample: Dict[str, Any], prediction: str) -> ScoreResult:
    pred = (prediction or "").strip()
    words = len([w for w in re.split(r"\s+", pred) if w])

    max_words = 200
    m = re.search(r"under\s+(\d+)\s+words", (sample.get("constraints") or "").lower())
    if m:
        max_words = int(m.group(1))
    length_ok = 1 if words <= max_words else 0

    # State preservation
    state_good = [
        r"\bas (approved|logged|declared|active)\b",
        r"\bremains\b",
        r"\bstill\b",
        r"\bper record\b",
        r"\bper policy\b"
    ]
    state_bad = [
        r"\bapproved now\b",
        r"\ball clear\b",
        r"\bwe agreed\b",
        r"\bfinalized\b"
    ]
    state = 1 if (_has(pred, state_good) and not _has(pred, state_bad)) else 0

    # Restatement of prior decision or status
    restate = 1 if _has(pred, [r"\bwas approved\b", r"\bwas denied\b", r"\bongoing\b", r"\bpending\b", r"\bfrozen\b"]) else 0

    # Uncertainty discipline
    uncertainty = 1 if _has(pred, [r"\bunknown\b", r"\bpending\b", r"\bawaiting\b"]) else 0

    # Next step clarity
    next_step = 1 if _has(pred, [r"\bnext\b", r"\bby\b.*\b(today|tomorrow|\d+)\b", r"\bwill\b.*\bupdate\b"]) else 0

    raw = (
        0.30 * length_ok +
        0.30 * state +
        0.20 * restate +
        0.10 * uncertainty +
        0.10 * next_step
    )
    final = max(0.0, min(1.0, raw))

    return ScoreResult(
        score=final,
        details={
            "word_count": words,
            "max_words": max_words,
            "length_ok": length_ok,
            "state_preservation": state,
            "restate": restate,
            "uncertainty": uncertainty,
            "next_step": next_step,
            "context_pressure": sample.get("context_pressure"),
            "domain": sample.get("domain"),
        },
    )

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)}