"""Write the evaluation report (CLAUDE.md §6).

Aggregates the three measurements into ``eval/REPORT.md``:
1. Retrieval Recall@10 (deterministic, no LLM) — from ``eval.recall_eval``.
2. Conversational Recall@10 + groundedness — from ``eval.replay`` (multi-turn,
   one router LLM call per turn).
3. Behavior probes — from ``eval.probes``.

    python eval/report.py                 # scripted replay
    python eval/report.py --llm-user      # LLM-simulated user for the replay

LLM-dependent sections degrade gracefully: if calls fail (e.g. rate limit), the
affected traces/probes are flagged, and the deterministic retrieval section is
always populated.
"""

from __future__ import annotations

import argparse
import sys
from pathlib import Path

ROOT = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(ROOT))

from eval import probes as probes_mod  # noqa: E402
from eval import recall_eval, replay  # noqa: E402

REPORT_PATH = ROOT / "eval" / "REPORT.md"


def build_report(llm_user: bool = False) -> str:
    # 1. Deterministic retrieval recall (assembly + router query).
    retr = recall_eval.evaluate(k=10, use_router=False, assemble=True)

    # 2. Conversational replay (multi-turn through the real agent).
    conv = replay.run(mode="llm" if llm_user else "scripted")

    # 3. Behavior probes.
    probe_results = probes_mod.run_probes()

    lines: list[str] = []
    lines += ["# Evaluation Report", ""]
    lines += [
        "Generated by `python eval/report.py`. Three views: deterministic "
        "retrieval recall, multi-turn conversational recall, and behavior probes.",
        "",
        "## Headline",
        "",
        f"- **Retrieval Recall@10 (deterministic):** {retr['mean_recall']:.4f}",
        f"- **Conversational Recall@10 ({conv['mode']} user):** "
        f"{conv['mean_recall']:.4f} "
        f"(scored {conv['n_scored']}/{len(conv['rows'])}; "
        f"{conv['n_errored']} LLM-errored)",
        f"- **Groundedness:** {conv['groundedness']:.4f} (URLs in catalog)",
        f"- **Probes:** {sum(p.passed for p in probe_results)}/{len(probe_results)} passed",
        "",
    ]

    lines += ["## Retrieval Recall@10 (per trace)", ""]
    lines += ["| Trace | Recall | Hits/Exp | Missed |", "|--|--:|--:|--|"]
    for row in retr["rows"]:
        missed = ", ".join(row["missed"]) if row["missed"] else "—"
        lines.append(f"| {row['id']} | {row['recall']:.2f} | "
                     f"{row['hits']}/{row['expected']} | {missed} |")
    lines.append("")

    lines += ["## Conversational Recall@10 (multi-turn replay)", ""]
    lines += ["| Trace | Recall | Hits/Exp | Turns | Ended | Recs | Note |",
              "|--|--:|--:|--:|--|--:|--|"]
    for row in conv["rows"]:
        note = ("LLM error (excluded)" if row["excluded"]
                else "recovered after transient error" if row["llm_error"] else "")
        lines.append(f"| {row['id']} | {row['recall']:.2f} | "
                     f"{row['hits']}/{row['expected']} | {row['turns']} | "
                     f"{row['ended']} | {row['n_recs']} | {note} |")
    lines.append("")

    lines += ["## Behavior probes", ""]
    lines += ["| Probe | Result | Detail |", "|--|--|--|"]
    for p in probe_results:
        lines.append(f"| {p.name} | {'PASS' if p.passed else 'FAIL'} | {p.detail} |")
    lines.append("")
    lines += [
        "See `eval/TUNING_LOG.md` for the before/after that produced these "
        "numbers, and `APPROACH.md` for the method.",
        "",
    ]
    return "\n".join(lines)


def main() -> None:
    ap = argparse.ArgumentParser(description=__doc__)
    ap.add_argument("--llm-user", action="store_true")
    args = ap.parse_args()
    report = build_report(llm_user=args.llm_user)
    REPORT_PATH.write_text(report, encoding="utf-8")
    print(report)
    print(f"\nWrote {REPORT_PATH}")


if __name__ == "__main__":
    main()