"""PoC: Artikel/Absatz-Zuordnung für CRA-Controls (Pfad B2b).

Pro Control: semantische Suche (Go-SDK /rag/search, nomic-embed + Qdrant) holt
die besten artikel-getaggten CRA-Chunks; Haiku wählt die passende Fundstelle und
gibt {article, paragraph, confidence}. Schreibt NICHTS in die DB — nur Report zur
Validierung (50er-Stichprobe). Lauf:

  ssh macmini 'docker exec -i -e POC_N=8 bp-compliance-backend python3 -' \
      < scripts/qa/poc_cra_article_assign.py
"""
import json
import os
import re

import httpx
from sqlalchemy import create_engine, text

N = int(os.environ.get("POC_N", "50"))
DB = os.environ.get("COMPLIANCE_DATABASE_URL") or os.environ["DATABASE_URL"]
SDK = os.environ.get("SDK_URL", "http://ai-compliance-sdk:8090")
AKEY = (os.environ.get("ANTHROPIC_API_KEY") or "").strip()
MODEL = os.environ.get("POC_HAIKU_MODEL", "claude-haiku-4-5-20251001")

_MARKER = re.compile(r"^\[([^\]]+)\]")
_BINDING = ("artikel", "anhang", "annex", "art.", "teil")
_JSON = re.compile(r"\{.*\}", re.DOTALL)


def is_binding(marker: str) -> bool:
    m = marker.lower()
    return any(k in m for k in _BINDING)


def retrieve(query: str) -> list:
    """Top binding (article/annex) CRA chunks for the control text."""
    try:
        r = httpx.post(
            f"{SDK}/sdk/v1/rag/search",
            json={"query": query, "collection": "bp_compliance_ce",
                  "top_k": 12, "regulations": ["cra_2024"]},
            timeout=20.0,
        )
        res = r.json().get("results", [])
    except Exception as e:  # noqa: BLE001
        return [{"_err": str(e)}]
    cands = []
    for x in res:
        t = x.get("text") or ""
        m = _MARKER.match(t)
        if m and is_binding(m.group(1)):
            cands.append({"marker": m.group(1).strip(),
                          "text": t[:400], "score": x.get("score", 0.0)})
        if len(cands) >= 3:
            break
    return cands


def haiku(control_text: str, cands: list) -> dict:
    block = "\n".join(
        f"[{i+1}] {c['marker']}: {c['text'][:300]}" for i, c in enumerate(cands)
    )
    prompt = (
        "Eine CRA-Compliance-Pflicht soll der korrekten Fundstelle im Cyber "
        "Resilience Act (Verordnung (EU) 2024/2847) zugeordnet werden.\n\n"
        f"PFLICHT:\n{control_text}\n\n"
        f"KANDIDATEN-FUNDSTELLEN (aus dem CRA-Volltext):\n{block}\n\n"
        "Wähle die Fundstelle, die die Pflicht am genauesten verankert. "
        "Antworte NUR mit JSON: "
        '{"article":"Artikel N|Anhang X","paragraph":"Absatz N|","candidate":N,'
        '"confidence":0.0}. Wenn keine passt: '
        '{"article":"","paragraph":"","candidate":0,"confidence":0.0}'
    )
    r = httpx.post(
        "https://api.anthropic.com/v1/messages",
        headers={"x-api-key": AKEY, "anthropic-version": "2023-06-01",
                 "content-type": "application/json"},
        json={"model": MODEL, "max_tokens": 200,
              "messages": [{"role": "user", "content": prompt}]},
        timeout=60.0,
    )
    data = r.json()
    if "content" not in data:
        return {"_err": str(data)[:200]}
    m = _JSON.search(data["content"][0]["text"])
    return json.loads(m.group(0)) if m else {"_err": "no json"}


def main() -> None:
    eng = create_engine(DB)
    with eng.connect() as c:
        c.execute(text("SET search_path TO compliance, core, public"))
        rows = c.execute(text("""
            SELECT cc.id::text uid, cc.control_id,
                   trim(coalesce(cc.title,'') || '. ' || coalesce(cc.objective,'')) ctext,
                   cpl.source_article existing
            FROM atom_classification ac
            JOIN canonical_controls cc ON cc.id = ac.control_uuid
            JOIN control_parent_links cpl ON cpl.control_uuid = ac.control_uuid
            WHERE ac.use_case = 'cra' AND ac.relevant = true
            ORDER BY md5(cc.control_id)
            LIMIT :n
        """), {"n": N}).fetchall()

    print(f"PoC CRA Artikel-Zuordnung — {len(rows)} Controls, Modell {MODEL}\n")
    n_assigned = n_conf = n_changed = n_nocand = 0
    for row in rows:
        cands = retrieve(row.ctext or row.control_id)
        if cands and cands[0].get("_err"):
            print(f"[{row.control_id}] RAG-ERR {cands[0]['_err'][:80]}")
            continue
        if not cands:
            n_nocand += 1
            print(f"[{row.control_id}] alt={row.existing!r:30} → KEINE Artikel-Kandidaten")
            continue
        v = haiku(row.ctext, cands)
        if v.get("_err"):
            print(f"[{row.control_id}] HAIKU-ERR {v['_err'][:80]}")
            continue
        art = v.get("article", "")
        para = v.get("paragraph", "")
        conf = v.get("confidence", 0.0)
        if art:
            n_assigned += 1
        if conf >= 0.7:
            n_conf += 1
        if art and art.lower().replace("artikel", "art").strip() not in (row.existing or "").lower():
            n_changed += 1
        newref = f"{art}{(' ' + para) if para else ''}"
        print(f"[{row.control_id}] conf={conf:.2f} NEU={newref!r:24} ALT={row.existing!r:30} "
              f"| top-cand={cands[0]['marker'][:18]!r}")
        print(f"      pflicht: {(row.ctext or '')[:95]}")

    print(f"\n--- Summe ({len(rows)}) ---")
    print(f"  Artikel zugeordnet : {n_assigned}")
    print(f"  confidence >= 0.70 : {n_conf}")
    print(f"  abweichend von ALT : {n_changed}")
    print(f"  keine Kandidaten   : {n_nocand}")


if __name__ == "__main__":
    main()