"""QA harness — 2 % spot-check sample per extraction batch.

Mechanical checks run here; faithfulness (does the extraction match the ad?)
is judged by Claude reading the generated review file. Deviations from
QUALITY_BAR.md ("Extraction quality" section) are logged, not auto-fixed.

Usage:
  python pipeline/qa_sample.py --extractions data/evidence/<slug>.jsonl \
      --ads data/raw/jobs/<slug>_ads.json [--rate 0.02] [--seed <slug>]

Output:
  docs/qa/<batch>-sample.md   ad text + extraction side-by-side for review
  docs/qa/<batch>-issues.log  mechanical deviations (one line each)

Deterministic sampling (seeded by batch name) so re-runs pick the same
records and reviews stay comparable.
"""
import argparse
import json
import os
import random
import sys

sys.path.insert(0, os.path.dirname(__file__))
from extract_local import ARRAY_FIELDS, SENIORITY

BASE = os.path.join(os.path.dirname(__file__), "..")
QA_DIR = os.path.join(BASE, "docs", "qa")


def mechanical_issues(rec):
    issues = []
    hard = {x.lower() for x in rec.get("hard_skills", [])}
    soft = {x.lower() for x in rec.get("soft_skills", [])}
    tools = {x.lower() for x in rec.get("tools", [])}
    if hard & soft:
        issues.append(f"cross-field dupe hard/soft: {sorted(hard & soft)}")
    if hard & tools:
        issues.append(f"cross-field dupe hard/tools: {sorted(hard & tools)}")
    if rec.get("seniority") not in SENIORITY:
        issues.append(f"invalid seniority {rec.get('seniority')!r}")
    for field, (max_items, max_len) in ARRAY_FIELDS.items():
        vals = rec.get(field, [])
        if len(vals) > max_items:
            issues.append(f"{field}: {len(vals)} items > {max_items}")
        for v in vals:
            if "�" in v or len(v) > max_len:
                issues.append(f"{field}: bad item {v!r}")
            # lowercase rule: only flag all-caps sentences, products are fine
            if v.isupper() and len(v) > 6:
                issues.append(f"{field}: shouting {v!r}")
    return issues


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--extractions", required=True)
    ap.add_argument("--ads", required=True)
    ap.add_argument("--rate", type=float, default=0.02)
    ap.add_argument("--seed", default=None, help="defaults to extractions filename")
    args = ap.parse_args()

    batch = os.path.splitext(os.path.basename(args.extractions))[0]
    rng = random.Random(args.seed or batch)

    recs = [json.loads(l) for l in open(args.extractions, encoding="utf-8") if l.strip()]
    ads = {a["job_id"]: a for a in json.load(open(args.ads, encoding="utf-8"))}
    n = max(1, round(len(recs) * args.rate))
    sample = rng.sample(recs, min(n, len(recs)))

    os.makedirs(QA_DIR, exist_ok=True)
    issues_total = 0
    review = [f"# QA sample — {batch}", "",
              f"{len(sample)} of {len(recs)} records ({args.rate:.0%}). "
              "Judge each extraction against QUALITY_BAR.md 'Extraction quality': "
              "faithful, no invented items, plausible seniority.", ""]
    with open(os.path.join(QA_DIR, f"{batch}-issues.log"), "w", encoding="utf-8") as log:
        for rec in sample:
            ad = ads.get(rec["job_id"], {})
            probs = mechanical_issues(rec)
            issues_total += len(probs)
            for p in probs:
                log.write(f"{rec['job_id']}\t{p}\n")
            review += [f"## {ad.get('title', rec['job_id'])} ({ad.get('country','?')})", "",
                       "**Ad (first 2000 chars):**", "",
                       "> " + (ad.get("description", "AD TEXT MISSING")[:2000]
                               ).replace("\n", "\n> "), "",
                       "**Extraction:**", "", "```json",
                       json.dumps({k: v for k, v in rec.items() if k != "job_id"},
                                  ensure_ascii=False, indent=1),
                       "```", ""]
            if probs:
                review += ["**Mechanical issues:** " + "; ".join(probs), ""]
    out = os.path.join(QA_DIR, f"{batch}-sample.md")
    open(out, "w", encoding="utf-8").write("\n".join(review))
    print(f"{len(sample)} sampled, {issues_total} mechanical issues -> {out}")
    if issues_total > len(sample):  # more than 1 issue per record on average
        sys.exit(1)


if __name__ == "__main__":
    main()