feat: house price prediction model integration

backend/app/services/house_price_predictor.py

@@ -1,16 +1,17 @@
+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.
     """
-
-    async def predict(
-        self, square_feet: float, bedrooms: int, bathrooms: float
-    ) -> float:
-        base_price = square_feet * 200
-        bedroom_value = bedrooms * 25000
-        bathroom_value = bathrooms * 15000
-
-        predicted_price = base_price + bedroom_value + bathroom_value
-
-        return predicted_price
+    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