Update app.py

app.py

@@ -11,27 +11,3 @@ def preprocess_image(filename, target_shape=(160, 160)):
 
 # Load the base FaceNet model
 facenet_model = load_model('facenet_keras.h5', compile=False)
-
-# Create the embedding model using the FaceNet model
-embedding = Model(inputs=facenet_model.input, 
-                  outputs=facenet_model.layers[-2].output, 
-                  name="Embedding")
-
-# Load the weights for your siamese or modified FaceNet model
-embedding.load_weights('base.h5')
-
-# Set all layers to non-trainable
-for layer in embedding.layers:
-    layer.trainable = False
-
-# Function to generate embedding
-def generate_embedding(image_path, model):
-    preprocessed_image = preprocess_image(image_path)
-    preprocessed_image = tf.expand_dims(preprocessed_image, axis=0)  # Add batch dimension
-    embedding = model(preprocessed_image)
-    return embedding
-
-# Example usage
-image_path = 'iman.jpg'  # Update with your image's path
-image_embedding = generate_embedding(image_path, embedding)
-print("Generated Embedding:", image_embedding.numpy())