# Standalone inference helper for Demand Forecaster
import json
from pathlib import Path
import numpy as np
import pandas as pd
import joblib

def load_forecaster(model_dir="."):
    path = Path(model_dir)
    config = json.loads((path / "config.json").read_text())
    models = joblib.load(path / "models.joblib")
    features = config["feature_names"]
    q_correction = config["calibrator"]["q_correction"] if config.get("calibrator") else 0.0

    def predict(df_features, apply_calibration=True):
        X = df_features[features]
        p10 = models[0.1].predict(X)
        p50 = models[0.5].predict(X)
        p90 = models[0.9].predict(X)
        stacked = np.sort(np.vstack([p10, p50, p90]), axis=0)
        p10, p50, p90 = stacked[0], stacked[1], stacked[2]
        if apply_calibration:
            p10 -= q_correction
            p90 += q_correction
            stacked_cal = np.sort(np.vstack([p10, p50, p90]), axis=0)
            p10, p50, p90 = stacked_cal[0], stacked_cal[1], stacked_cal[2]
        return pd.DataFrame({"p10": p10, "p50_point": p50, "p90": p90}, index=df_features.index)

    return predict