"""maild classification: rules first, LLM optional, conservative threshold.

The LLM never decides alone: any result below ``CAYCE_OPS_LLM_REVIEW_THRESHOLD``
confidence becomes ``needs_review``. With the ``null`` provider (default) only
deterministic rules run.
"""

from __future__ import annotations

import json
import re

from cayceops.config import Config
from cayceops.models import MailClassification

_RULES: list[tuple[MailClassification, re.Pattern[str], float]] = [
    # (class, pattern, confidence when matched)
    (MailClassification.REJECTION, re.compile(r"\b(declin|reject|not able to|unable to|unfortunately|we regret|won.t be able)\w*", re.I), 0.9),
    (MailClassification.APPROVAL, re.compile(r"\b(approv|accept|welcome to the|you.re (in|approved)|live|activated|glad to)\w*", re.I), 0.85),
    (MailClassification.QUESTION, re.compile(r"\b(could you|can you|please (provide|clarify|confirm)|what (is|are)|how (do|does|can)|need more (info|details))\b", re.I), 0.7),
]

_LLM_PROMPT = """Classify this merchant email into exactly one of:
approval, rejection, question, other.
Reply with strict JSON: {"classification": "...", "confidence": 0.0-1.0}

Subject and body:
{body}"""


def _rules(text: str) -> tuple[MailClassification, float]:
    for kind, pattern, confidence in _RULES:
        if pattern.search(text):
            return kind, confidence
    return MailClassification.OTHER, 0.3


def _llm_classify(text: str, cfg: Config) -> tuple[MailClassification, float] | None:
    """Ask the configured LLM. Returns None on any failure (rules take over)."""
    from cayceops.llm import get_provider

    try:
        provider = get_provider(cfg)
        raw = provider.complete(_LLM_PROMPT.format(body=text[:4000]), system="You classify merchant emails. Reply with JSON only.")
        data = json.loads(re.search(r"\{.*\}", raw, re.S).group(0))
        kind = MailClassification(data["classification"].lower())
        confidence = float(data["confidence"])
    except Exception:  # noqa: BLE001 — LLM is best-effort by design
        return None
    if kind not in MailClassification or kind is MailClassification.NEEDS_REVIEW:
        return None
    return kind, min(max(confidence, 0.0), 1.0)


def classify(text: str, cfg: Config) -> tuple[MailClassification, float]:
    """Classify merchant text. Degrades to rules when no LLM is configured."""
    if cfg.llm_provider != "null":
        llm_result = _llm_classify(text, cfg)
        if llm_result is not None:
            kind, confidence = llm_result
        else:  # LLM failed — fall through to rules
            kind, confidence = _rules(text)
    else:
        kind, confidence = _rules(text)

    if confidence < cfg.llm_review_threshold:
        return MailClassification.NEEDS_REVIEW, confidence
    return kind, confidence