Use a prediciton dataclass

backend/app/models/prediction.py

@@ -1,12 +0,0 @@
-from pydantic import BaseModel, Field
-from typing import List
-
-class HousePredictionInput(BaseModel):
-    square_feet: float = Field(..., gt=0, description="Square footage of the house")
-    bedrooms: int = Field(..., ge=1, description="Number of bedrooms")
-    bathrooms: float = Field(..., gt=0, description="Number of bathrooms")
-
-class HousePredictionOutput(BaseModel):
-    predicted_price: float
-    confidence_score: float
-    similar_listings: List[float] 

backend/app/services/house_price_predictor.py

@@ -1,5 +1,14 @@
 import random
 from typing import List, Tuple
+from dataclasses import dataclass
+
+
+@dataclass
+class Prediction:
+    predicted_price: float
+    confidence_score: float
+    similar_listings: List[float]
+
 
 class HousePricePredictor:
     """
@@ -11,7 +20,7 @@ class HousePricePredictor:
         # Mock initialization - in reality would load model weights
         pass
     
-    def predict(self, square_feet: float, bedrooms: int, bathrooms: float) -> Tuple[float, float, List[float]]:
+    def predict(self, square_feet: float, bedrooms: int, bathrooms: float) -> Prediction:
         """
         Mock prediction method that returns:
         - predicted price
@@ -32,4 +41,4 @@ class HousePricePredictor:
             for _ in range(3)
         ]
         
-        return predicted_price, confidence_score, similar_listings 
+        return Prediction(predicted_price, confidence_score, similar_listings)