import os
import joblib
import numpy as np

from backend.app.dtos.house.house_features import HouseFeatures

class HousePricePredictor:
    """
    Mock ML model that predicts house prices.
    In a real scenario, this would load a trained model.
    """
    def __init__(self):
        self.model = joblib.load("backend/app/ai_models/price_predictor.pkl")
        
    def predict(self, features: HouseFeatures) -> float:
        X = np.array([[features.square_feet, features.bedrooms, features.bathrooms]])
        return self.model.predict(X)[0] * 100000