[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

model_optimization.py

@@ -1,19 +1,24 @@
+import numpy as np
 from tensorflow.keras.applications import MobileNetV2
 from tensorflow.keras.preprocessing.image import img_to_array, load_img
-import numpy as np
+
 
 def load_optimized_model():
-    model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+    model = MobileNetV2(
+        weights="imagenet", include_top=False, input_shape=(224, 224, 3)
+    )
     print("Loaded MobileNetV2 model for optimized face detection.")
     return model
 
+
 def preprocess_image(image_path):
     image = load_img(image_path, target_size=(224, 224))
     image = img_to_array(image)
     image = image / 255.0  # Normalize image
     image = np.expand_dims(image, axis=0)
     return image
 
+
 # Example usage
 if __name__ == "__main__":
     model = load_optimized_model()

test_face_recognition.py

@@ -1,20 +1,27 @@
 import unittest
+
 from face_recognition_module import detect_faces, generate_embeddings
 
+
 class TestFaceRecognition(unittest.TestCase):
     def test_detect_faces(self):
         # Test with a sample image
         image_path = "test_images/sample.jpg"
         faces = detect_faces(image_path)
-        self.assertGreater(len(faces), 0, "No faces detected in the sample image.")
+        self.assertGreater(
+            len(faces), 0, "No faces detected in the sample image.")
 
     def test_generate_embeddings(self):
         # Test with a dummy face data
         face_data = "sample_face_data"
         embedding = generate_embeddings(face_data)
-        self.assertIsNotNone(embedding, "Embedding generation failed for the given face data.")
-        self.assertEqual(len(embedding), 128, "Embedding length should be 128 dimensions.")  # Example dimension
+        self.assertIsNotNone(
+            embedding, "Embedding generation failed for the given face data."
+        )
+        self.assertEqual(
+            len(embedding), 128, "Embedding length should be 128 dimensions."
+        )  # Example dimension
+
 
 if __name__ == "__main__":
     unittest.main()
-