from __future__ import annotations

from pathlib import Path
from typing import Any

import joblib
import pandas as pd

from app.ml.features import build_feature_matrix


DEFAULT_MODEL_PATH = Path("models/scalping_model.joblib")


class ScalpingModel:
    def __init__(self, model_path: str | Path = DEFAULT_MODEL_PATH):
        self.model_path = Path(model_path)
        self.bundle: dict[str, Any] | None = None

    @property
    def available(self) -> bool:
        return self.model_path.exists()

    def load(self) -> bool:
        if not self.available:
            return False
        self.bundle = joblib.load(self.model_path)
        return True

    @property
    def version(self) -> str:
        if self.bundle is None and not self.load():
            return ""
        assert self.bundle is not None
        return str(self.bundle.get("created_at", ""))

    def score(self, row: dict[str, Any]) -> dict[str, float]:
        if self.bundle is None and not self.load():
            return {}

        assert self.bundle is not None
        frame = pd.DataFrame([row])
        features, _ = build_feature_matrix(
            frame,
            self.bundle["feature_columns"],
            self.bundle.get("medians"),
        )

        scores: dict[str, float] = {}
        for target, model in self.bundle.get("models", {}).items():
            if hasattr(model, "predict_proba"):
                scores[target] = float(model.predict_proba(features)[0][1])
            else:
                scores[target] = float(model.predict(features)[0])
        return scores