image_model.py

from tensorflow.keras.applications import VGG16
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam

def create_image_model():
    """Create and compile the image model using VGG16."""
    base_model = VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    x = Flatten()(base_model.output)
    x = Dense(1024, activation='relu')(x)
    predictions = Dense(1, activation='sigmoid')(x)
    model = Model(inputs=base_model.input, outputs=predictions)
    
    for layer in base_model.layers:
        layer.trainable = False
    
    model.compile(optimizer=Adam(learning_rate=0.0001), loss='binary_crossentropy', metrics=['accuracy'])
    return model

def train_image_model(X_images, y_images):
    """Train the image model."""
    model = create_image_model()
    model.fit(X_images, y_images, epochs=10, batch_size=32, validation_split=0.2)
    return model