# import necessary libraries

from keras.preprocessing.image import ImageDataGenerator

from keras.applications.vgg16 import VGG16

from keras.layers import Dense, LSTM, Dropout, Flatten, Reshape

from keras.models import Sequential

# set up the data generators

train_dir = 'X-ray Images/train'

val_dir = 'X-ray Images/validation'

test_dir = 'X-ray Images/test'

img_height = 224

img_width = 224

batch_size = 32

train_datagen = ImageDataGenerator(rescale=1./255)

val_datagen = ImageDataGenerator(rescale=1./255)

test_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(

    train_dir,

    target_size=(img_height, img_width),

    batch_size=batch_size,

    class_mode='categorical')

val_generator = val_datagen.flow_from_directory(

    val_dir,

    target_size=(img_height, img_width),

    batch_size=batch_size,

    class_mode='categorical')

test_generator = test_datagen.flow_from_directory(

    test_dir,

    target_size=(img_height, img_width),

    batch_size=batch_size,

    class_mode='categorical')

# load the pre-trained VGG16 model and extract features from the images

vgg_model = VGG16(weights='imagenet', include_top=False, input_shape=(img_height, img_width, 3))

# freeze the layers in the VGG16 model

for layer in vgg_model.layers:

    layer.trainable = False

# create the CNN-LSTM model

model = Sequential()

model.add(vgg_model)

model.add(Flatten())

model.add(Reshape((1, -1)))

model.add(LSTM(256, return_sequences=False))

model.add(Dropout(0.5))

model.add(Dense(128, activation='relu'))

model.add(Dropout(0.5))

model.add(Dense(train_generator.num_classes, activation='softmax'))

# compile the model

model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# train the model

history = model.fit_generator(

    train_generator,

    steps_per_epoch=train_generator.n // batch_size,

    epochs=10,

    validation_data=val_generator,

    validation_steps=val_generator.n // batch_size)

# evaluate the model on the test data

test_loss, test_acc = model.evaluate_generator(test_generator, steps=test_generator.n // batch_size)

print('Test accuracy:', test_acc)

# make predictions on new data

predictions = model.predict_generator(test_generator, steps=test_generator.n // batch_size)