app.py

import tensorflow as tf
from tensorflow.keras.models import load_model, Model

# Function to preprocess the image
def preprocess_image(filename, target_shape=(160, 160)):
    image_string = tf.io.read_file(filename)
    image = tf.image.decode_jpeg(image_string, channels=3)
    image = tf.image.convert_image_dtype(image, tf.float32)
    image = tf.image.resize(image, target_shape)
    return image

# 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('facenet_siamese_embedding.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())