diff --git a/src/adverse_weather_classification/weather_classification.py b/src/adverse_weather_classification/weather_classification.py
index 777ddfe..9db16c9 100644
--- a/src/adverse_weather_classification/weather_classification.py
+++ b/src/adverse_weather_classification/weather_classification.py
@@ -8,50 +8,48 @@
 
 
 class AdverseWeatherClassifier:
-    def __init__(self, model_path, model_input_size: Tuple[int, int] = (256, 256)) -> None:
+    def __init__(self, model_path: str, model_input_size: Tuple[int, int] = (256, 256)):
         self.model = None
         self.model_path = model_path
         self.model_input_size = model_input_size
         self.class_labels = ['day', 'night']
 
-    def load(self):
+    def load_model(self):
         start_time = time.time()
         self.model = keras.models.load_model(self.model_path)
-        print("Model loaded in {} seconds.".format(time.time() - start_time))
+        print("Model loaded in {:.2f} seconds.".format(time.time() - start_time))
 
     def __preprocess_frame__(self, frame: np.ndarray) -> np.ndarray:
-        frame = cv2.resize(frame, self.model_input_size)
-        frame = frame.astype('float32') / 255.0
+        frame = cv2.resize(frame, self.model_input_size).astype('float32') / 255.0
         frame = np.expand_dims(frame, axis=0)
         return frame
 
     def predict(self, preprocessed_frame: np.ndarray) -> str:
-        if self.model is not None:
-            predictions = self.model.predict(preprocessed_frame)
-        else:
-            raise ValueError("Model is not loaded. Please call load() method first.")
+        if self.model is None:
+            raise ValueError("Model is not loaded. Please call load_model() method first.")
+        predictions = self.model.predict(preprocessed_frame)
         predicted_class = self.class_labels[np.argmax(predictions)]
         return predicted_class
 
-    def exec(self, frame: np.ndarray) -> str:
-        preprocessed_frame = self.__preprocess_frame__(frame)
+    def execute(self, frame: np.ndarray) -> str:
+        preprocessed_frame = self.preprocess_frame(frame)
         predicted_class = self.predict(preprocessed_frame)
         return predicted_class
 
 
 if __name__ == "__main__":
-    img_dir = "/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/root_dir/testing_imgs"
-    model_path_ = "/src/adverse_weather_classification/output/checkpoints/best_model.h5"
-    adverse_weather_classifier = AdverseWeatherClassifier(model_path_)
-    adverse_weather_classifier.load()
+    img_dir = "/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/testing_imgs"
+    model_path = "/home/ahv/PycharmProjects/Visual-Inertial-Odometry/simulation/CARLA/output/checkpoint_directory"
+    adverse_weather_classifier = AdverseWeatherClassifier(model_path)
+    adverse_weather_classifier.load_model()
     for root, dirs, files in os.walk(img_dir):
-        # shuffle the files
+        # Shuffle the files
         np.random.shuffle(files)
         for file in files:
             img = cv2.imread(os.path.join(root, file))
             visualization_img = img.copy()
-            predicted_class_ = adverse_weather_classifier.exec(img)
-            cv2.putText(visualization_img, predicted_class_, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
+            predicted_class = adverse_weather_classifier.execute(img)
+            cv2.putText(visualization_img, predicted_class, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
             cv2.imshow("Image", visualization_img)
             cv2.waitKey(0)
     cv2.destroyAllWindows()