# importing dependencies

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Dropout, Flatten

from tensorflow.keras.preprocessing.image import ImageDataGenerator

# training and testing dataset directories path

TRAIN_DATA_PATH = 'X-ray Images/train'

TEST_DATA_PATH = 'X-ray Images/test'

VALID_DATA_Path = 'X-ray Images/validation'

# cnn-model architecture

model = Sequential()

model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)))

model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(64, (3, 3), activation='relu'))

model.add(MaxPooling2D((2, 2)))

model.add(Conv2D(128, (3, 3), activation='relu'))

model.add(MaxPooling2D((2, 2)))

model.add(Flatten())

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

model.add(Dropout(0.5))

model.add(Dense(3, activation='softmax'))

# compiling the model

model.compile(optimizer='adam',

              loss='categorical_crossentropy',

              metrics=['accuracy'])

# data preprocessing

train_datagen = ImageDataGenerator(rescale=1./255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True)

test_datagen = ImageDataGenerator(rescale=1./255)

training_set = train_datagen.flow_from_directory(TRAIN_DATA_PATH, target_size=(224, 224), batch_size=32, class_mode='categorical')

validation_set = test_datagen.flow_from_directory(VALID_DATA_Path, target_size=(224, 224), batch_size=32, class_mode='categorical')

# Train the model

history = model.fit(training_set, epochs=10, validation_data=validation_set)