from methods.battery_optimizer import optimize_grid_setpoint, train_artifact
from methods.common import save_model

FORECAST_ID = 23


def train(data_obj):
    aid = data_obj["config"]["anlagen_id"]
    return {"trained": True, "path": save_model(aid, FORECAST_ID, train_artifact("var_21_22"))}


def predict(data_obj, pv_dict, load_dict):
    return optimize_grid_setpoint(data_obj, pv_dict, load_dict, forecast_id=23)