import json
import sys
from pathlib import Path

sys.path.insert(0, str(Path(__file__).resolve().parent))
from classifier import CATEGORY_ORDER, CATEGORY_META, classify  # noqa: E402

HERE = Path(__file__).resolve().parent
GOLD = HERE / "gold.json"
RESULTS = HERE / "results.json"
REPORT = HERE / "audit_report.md"


def load_gold():
    return json.loads(GOLD.read_text(encoding="utf-8"))


def main():
    data = load_gold()
    samples = data["samples"]
    per = []
    correct = 0
    for s in samples:
        predicted, scores = classify(s["text"])
        per.append({"text": s["text"], "expected": s["expected"],
                    "predicted": predicted, "scores": scores})
        correct += 1 if predicted == s["expected"] else 0

    n = len(samples)
    accuracy = correct / n

    per_cat = {}
    for c in CATEGORY_ORDER:
        tp = sum(1 for p in per if p["expected"] == c and p["predicted"] == c)
        fp = sum(1 for p in per if p["predicted"] == c and p["expected"] != c)
        fn = sum(1 for p in per if p["expected"] == c and p["predicted"] != c)
        precision = tp / (tp + fp) if (tp + fp) else 0.0
        recall = tp / (tp + fn) if (tp + fn) else 0.0
        f1 = (2 * precision * recall / (precision + recall)) if (precision + recall) else 0.0
        per_cat[c] = {"tp": tp, "fp": fp, "fn": fn,
                      "precision": precision, "recall": recall, "f1": f1,
                      "label": CATEGORY_META[c]}

    out = {
        "n_samples": n,
        "correct": correct,
        "accuracy": accuracy,
        "per_category": per_cat,
        "samples": per,
    }
    RESULTS.write_text(json.dumps(out, indent=2), encoding="utf-8")

    lines = []
    lines.append("# Triage classifier audit")
    lines.append("")
    lines.append(f"- Generated: {HERE / 'audit_report.md'}")
    lines.append(f"- Samples: {n}  |  Correct: {correct}  |  Accuracy: {accuracy:.1%}")
    lines.append("")
    lines.append("## Per-category metrics (precision / recall / f1)")
    lines.append("")
    lines.append("| Category | Precision | Recall | F1 | TP | FP | FN |")
    lines.append("|---|---|---|---|---|---|---|")
    for c in CATEGORY_ORDER:
        pc = per_cat[c]
        lines.append(f"| {c} ({pc['label']}) | {pc['precision']:.2f} | {pc['recall']:.2f} | "
                     f"{pc['f1']:.2f} | {pc['tp']} | {pc['fp']} | {pc['fn']} |")
    lines.append("")
    lines.append("## Sample outcomes")
    lines.append("")
    lines.append("| Text | Expected | Predicted |")
    lines.append("|---|---|---|")
    for p in per:
        mark = "OK" if p["predicted"] == p["expected"] else "MISMATCH"
        lines.append(f"| {p['text']} | {p['expected']} | {p['predicted']} [{mark}] |")
    lines.append("")
    REPORT.write_text("\n".join(lines), encoding="utf-8")

    print(f"n={n} correct={correct} accuracy={accuracy:.1%}")
    for c in CATEGORY_ORDER:
        pc = per_cat[c]
        print(f"  {c:20s} P={pc['precision']:.2f} R={pc['recall']:.2f} F1={pc['f1']:.2f} "
              f"(TP={pc['tp']} FP={pc['fp']} FN={pc['fn']})")
    print("Wrote", RESULTS, "and", REPORT)


if __name__ == "__main__":
    main()