"""
모델 로드/저장/예측 담당
"""
from pathlib import Path
from typing import Optional

import joblib
import numpy as np
from loguru import logger
from sklearn.pipeline import Pipeline

import config

MODEL_FILENAME = "classifier.pkl"


def _get_latest_path() -> Optional[Path]:
    candidates = sorted(config.CLASSIFIER_MODEL_BASE_PATH.rglob(MODEL_FILENAME))
    return candidates[-1] if candidates else None


def _get_save_path() -> Path:
    from datetime import datetime
    date_dir = config.CLASSIFIER_MODEL_BASE_PATH / datetime.now().strftime("%Y%m%d")
    date_dir.mkdir(exist_ok=True)
    existing  = sorted([d for d in date_dir.iterdir() if d.is_dir() and d.name.isdigit()])
    next_idx  = int(existing[-1].name) + 1 if existing else 1
    save_dir  = date_dir / f"{next_idx:04d}"
    save_dir.mkdir(exist_ok=True)
    return save_dir / MODEL_FILENAME


_model: Optional[Pipeline] = None

# 서버 시작 시 최신 모델 자동 로드
_latest = _get_latest_path()
if _latest:
    try:
        _model = joblib.load(_latest)
        logger.info(f"classifier 모델 로드: {_latest} | classes={list(_model.classes_)}")
    except Exception as e:
        logger.warning(f"모델 로드 실패: {e}")


def get_model() -> Optional[Pipeline]:
    return _model


def set_model(pipeline: Pipeline) -> None:
    global _model
    path = _get_save_path()
    joblib.dump(pipeline, path)
    _model = pipeline
    logger.info(f"classifier 모델 저장: {path}")


def predict(
    pipeline: Pipeline,
    text: str,
    threshold: float = 0.6,
) -> tuple[str, float, dict[str, float]]:
    probs      = pipeline.predict_proba([text])[0]
    classes    = pipeline.classes_
    max_idx    = int(np.argmax(probs))
    confidence = float(probs[max_idx])
    label      = "OTHER" if confidence < threshold else classes[max_idx]
    all_probs  = {c: round(float(p), 4) for c, p in zip(classes, probs)}
    return label, round(confidence, 4), all_probs