from typing import Union, Tuple
import datetime

class NextPlaceBaseModel:


    def __init__(self):
        self._load_model()


    def _load_model(self):
        """
        Perform any actions needed to load the model.
        EX: Establish API connections, download an ML model for inference, etc...
        """
        print("Loading model...")
        # Optional model loading
        print("Model loaded.")


    def _sale_date_predictor(self, daysOnMarket: int):
        """
        Calculate the expected sale date based on the national average
        :param daysOnMarket: number of days this house has been on the market
        :return: the predicted sale date, based on the national average of 34 days
        """
        national_average = 34
        if daysOnMarket < national_average:
            days_until_sale = national_average - daysOnMarket
            sale_date = datetime.date.today() + datetime.timedelta(days=days_until_sale)
            return sale_date
        else:
            return datetime.date.today() + datetime.timedelta(days=1)


    def run_inference(self, input_data: dict[str, Union[str, int, float]]) -> Tuple[float, str]:
        """
        Predict the sale price and sale date for the house represented by `input_data`
        :param input_data: a formatted Synapse from the validator, representing a currently listed house
        :return: the predicted sale price and predicted sale date for this home
        """
        predicted_sale_price = float(input_data['price']) if ('price' in input_data) else 1.0
        predicted_sale_date = self._sale_date_predictor(input_data['dom']) if ('dom' in input_data) else datetime.date.today() + datetime.timedelta(days=1)
        predicted_sale_date = predicted_sale_date.strftime("%Y-%m-%d")
        print(f"Predicted sale price: {predicted_sale_price}")
        print(f"Predicted sale date: {predicted_sale_date}")
        return predicted_sale_price, predicted_sale_date