Format everything

backend/app/services/house_price_predictor.py

@@ -15,12 +15,14 @@ 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(self, square_feet: float, bedrooms: int, bathrooms: float) -> Prediction:
+
+    def predict(
+        self, square_feet: float, bedrooms: int, bathrooms: float
+    ) -> Prediction:
         """
         Mock prediction method that returns:
         - predicted price
@@ -31,14 +33,13 @@ class HousePricePredictor:
         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)
+            predicted_price * random.uniform(0.9, 1.1) for _ in range(3)
         ]
-        
+
         return Prediction(predicted_price, confidence_score, similar_listings)

backend/app/services/investor_predictor.py

@@ -1,5 +1,6 @@
 import random
 
-class InvestorPredictor():
+
+class InvestorPredictor:
     def is_investor(user: User) -> bool:
         return random.random() < 0.5