"""Offline role-audit — gemma4 reviews borderline titles and proposes role terms.

Runs only from the CLI (``gimme-job proactive role-audit``) and never in the
runtime pipeline (CLAUDE.md: AI-assisted learning, non-AI runtime). Proposals
land in ``workspace/manifests/role-audit-YYYY-MM-DD.md`` and, with ``--write``,
in the machine-owned ``sites/role_terms.auto.yaml`` which ``ProactivePlan.load``
merges into ``verification.role_terms``.
"""
from __future__ import annotations

import json
from datetime import date
from pathlib import Path
from typing import Optional

from loguru import logger

from gimme_job.proactive.roles import (
    NON_CLINICAL,
    SUPPORT,
    TARGET,
    UNKNOWN,
    read_role_candidates,
)
from gimme_job.utils.paths import manifests_dir, sites_dir

DEFAULT_MODEL = "gemma4:26b-mlx"
DEFAULT_BASE_URL = "http://127.0.0.1:11434"
_VALID_ROLES = {TARGET, SUPPORT, NON_CLINICAL, UNKNOWN}

# Terms too generic to ever apply automatically (would misfire on every title)
_BLOCKED_TERMS = {
    "a", "an", "the", "and", "or", "of", "to", "in", "at", "on", "for", "with",
    "by", "is", "are", "be", "no", "not", "all", "any", "new", "other",
    "job", "jobs", "career", "careers", "work", "role", "position", "positions",
    "staff", "team", "member", "full", "part", "time",
}


def build_audit_prompt(entries: list[dict], max_titles: int = 60) -> str:
    lines: list[str] = []
    seen: set[str] = set()
    for entry in entries:
        title = (entry.get("title") or "").strip()
        if not title or title.lower() in seen:
            continue
        seen.add(title.lower())
        lines.append(f"- [{entry.get('role', '?')}] {title}")
        if len(lines) >= max_titles:
            break
    listed = "\n".join(lines)
    return (
        "You review job titles collected by an orthodontist job search.\n"
        "target = orthodontist / dentist / hidden dentist titles "
        "(staff dentist, dentist II, dentist III, dental officer, specialty dentist)\n"
        "support = dental assistant / hygienist / technician / coordinator / receptionist\n"
        "non_clinical = finance / accounting / HR / marketing / manager / IT\n"
        "unknown = cannot tell from the title\n\n"
        "Titles:\n"
        f"{listed}\n\n"
        "Return ONLY JSON, no prose:\n"
        '{"items":[{"title":"...","role":"target|support|non_clinical|unknown"}],'
        '"proposals":[{"role":"support","term":"short lowercase keyword","reason":"why"}]}\n'
        "Rules for proposals:\n"
        "- Propose a term only when the classification above looks wrong.\n"
        "- Never propose words already implied by the category descriptions "
        "(e.g. dentist, orthodontist, assistant, coordinator, hygienist, accountant).\n"
        "- Prefer short terms (1-3 words) that generalize beyond a single posting."
    )


def parse_audit_response(text: str) -> dict:
    """Extract the JSON block from a model response; tolerate prose around it."""
    start = text.find("{")
    end = text.rfind("}")
    if start == -1 or end <= start:
        return {"items": [], "proposals": [], "raw": text}
    try:
        data = json.loads(text[start : end + 1])
    except json.JSONDecodeError:
        return {"items": [], "proposals": [], "raw": text}
    items = [i for i in data.get("items", []) if isinstance(i, dict)]
    proposals = []
    for p in data.get("proposals", []):
        if not isinstance(p, dict):
            continue
        role = str(p.get("role") or "").strip()
        term = str(p.get("term") or "").strip().lower()
        if role in _VALID_ROLES and term and len(term) <= 40:
            proposals.append(
                {"role": role, "term": term, "reason": str(p.get("reason") or "")[:200]}
            )
    return {"items": items, "proposals": proposals, "raw": text}


def call_ollama(
    prompt: str,
    base_url: str = DEFAULT_BASE_URL,
    model: str = DEFAULT_MODEL,
    timeout: float = 300.0,
) -> str:
    import httpx

    url = f"{base_url.rstrip('/')}/api/generate"
    payload = {
        "model": model,
        "prompt": prompt,
        "temperature": 0.1,
        "stream": False,
        "options": {"num_predict": 4096},
    }

    def _post(body: dict) -> str:
        resp = httpx.post(url, json=body, timeout=timeout)
        resp.raise_for_status()
        data = resp.json()
        if data.get("error"):
            raise RuntimeError(str(data["error"])[:200])
        text = (data.get("response") or "").strip()
        if not text and data.get("done_reason") == "length":
            raise RuntimeError(
                "model exhausted its token budget before answering "
                "(reasoning model?) — try --limit smaller or another --model"
            )
        return text

    try:
        return _post({**payload, "think": False})
    except httpx.HTTPStatusError:
        # Older Ollama builds reject the `think` flag — retry without it.
        return _post(payload)


def append_auto_terms(
    proposals: list[dict], path: Optional[Path] = None
) -> tuple[Path, int]:
    """Append proposed terms to the machine-owned role_terms.auto.yaml.

    Terms already covered by the code defaults are skipped.
    """
    import yaml

    from gimme_job.proactive.roles import DEFAULT_ROLE_TERMS
    from gimme_job.utils.json_io import read_yaml

    path = path or sites_dir() / "role_terms.auto.yaml"
    data: dict = {}
    if path.exists():
        data = read_yaml(path) or {}
    terms: dict[str, list[str]] = data.setdefault("role_terms", {})
    added = 0
    for proposal in proposals:
        role, term = proposal["role"], proposal["term"]
        if term in DEFAULT_ROLE_TERMS.get(role, []):
            continue  # already covered by code defaults
        if term in _BLOCKED_TERMS or len(term) < 2:
            logger.debug(f"[role-audit] blocked generic term: {term!r}")
            continue
        current = terms.setdefault(role, [])
        if term not in current:
            current.append(term)
            added += 1
    header = (
        "# 자동 제안 role 용어 — `gimme-job proactive role-audit --write`가 생성/갱신.\n"
        "# 수동 편집 금지: 직접 추가하려면 sites/proactive.yaml의 verification.role_terms.\n\n"
    )
    path.parent.mkdir(parents=True, exist_ok=True)
    path.write_text(
        header
        + yaml.dump(
            {"role_terms": terms},
            allow_unicode=True,
            sort_keys=False,
            default_flow_style=False,
        ),
        encoding="utf-8",
    )
    logger.info(f"[role-audit] auto terms: {added} new → {path}")
    return path, added


def build_audit_report(
    entries: list[dict],
    parsed: dict,
    model: str,
    run_date: date,
) -> str:
    by_role: dict[str, int] = {}
    for entry in entries:
        role = entry.get("role", "?")
        by_role[role] = by_role.get(role, 0) + 1

    lines = [f"# Role audit — {run_date.isoformat()} ({model})", ""]
    lines.append(
        f"검토 후보 {len(entries)}건: "
        + ", ".join(f"{role} {count}" for role, count in sorted(by_role.items()))
    )
    lines.append("")

    proposals = parsed.get("proposals") or []
    lines.append(f"## 제안 용어 ({len(proposals)})")
    if proposals:
        for p in proposals:
            lines.append(f"- `{p['term']}` → {p['role']} — {p['reason']}")
    else:
        lines.append("제안 없음.")
    lines.append("")

    items = parsed.get("items") or []
    if items:
        lines.append(f"## 모델 분류 ({len(items)})")
        for item in items:
            lines.append(f"- [{item.get('role', '?')}] {item.get('title', '')}")
        lines.append("")

    raw = parsed.get("raw") or ""
    if not items and not proposals and raw:
        lines.append("## 원문 응답")
        lines.append("```")
        lines.append(raw[:4000])
        lines.append("```")
    return "\n".join(lines)


_SERP_SOURCES = {"duckduckgo", "bing", "googlejobs"}


def run_audit(
    run_date: date,
    model: str = DEFAULT_MODEL,
    base_url: str = DEFAULT_BASE_URL,
    max_titles: int = 60,
    titles: Optional[list[str]] = None,
) -> tuple[Path, dict, list[dict]]:
    """Collect candidates, ask the model, write the markdown report."""
    if titles:
        entries = [
            {"title": t, "role": "unknown", "url": "", "source": "manual"}
            for t in titles
        ]
    else:
        entries = read_role_candidates(run_date)
    if not entries:
        raise ValueError("no candidates logged for this date")
    # Real job titles from ATS boards first — SERP entries are often
    # navigational pages and make the audit noisy.
    entries.sort(key=lambda e: 1 if e.get("source") in _SERP_SOURCES else 0)

    prompt = build_audit_prompt(entries, max_titles=max_titles)
    raw = call_ollama(prompt, base_url=base_url, model=model)
    parsed = parse_audit_response(raw)

    report = build_audit_report(entries, parsed, model, run_date)
    manifests_dir().mkdir(parents=True, exist_ok=True)
    path = manifests_dir() / f"role-audit-{run_date.isoformat()}.md"
    path.write_text(report, encoding="utf-8")
    logger.info(f"[role-audit] report saved: {path}")
    return path, parsed, entries