Make the hosue price predictor more simple

backend/app/services/house_price_predictor.py

@@ -1,45 +1,16 @@
-import random
-from dataclasses import dataclass
-from typing import List
-
-
-@dataclass
-class Prediction:
-    predicted_price: float
-    confidence_score: float
-    similar_listings: List[float]
-
-
 class HousePricePredictor:
     """
     Mock ML model that predicts house prices.
     In a real scenario, this would load a trained model.
     """
 
-    def __init__(self):
-        # Mock initialization - in reality would load model weights
-        pass
-
-    def predict(
+    async def predict(
         self, square_feet: float, bedrooms: int, bathrooms: float
-    ) -> Prediction:
-        """
-        Mock prediction method that returns:
-        - predicted price
-        - confidence score
-        - similar listing prices
-        """
-        # Mock prediction logic
+    ) -> float:
         base_price = square_feet * 200
         bedroom_value = bedrooms * 25000
         bathroom_value = bathrooms * 15000
 
         predicted_price = base_price + bedroom_value + bathroom_value
 
-        # Add some randomness to make it interesting
-        confidence_score = random.uniform(0.8, 0.99)
-        similar_listings = [
-            predicted_price * random.uniform(0.9, 1.1) for _ in range(3)
-        ]
-
-        return Prediction(predicted_price, confidence_score, similar_listings)
+        return predicted_price