import os
import tensorflow as tf
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# Define constants
IMAGE_SIZE = (512, 512)
BATCH_SIZE = 4
EPOCHS = 10
TRAIN_DIR = 'T'
VALID_DIR = 'T'
MODEL_PATH = 'nsfw_classifier.h5'

# Create an image data generator for training data
train_datagen = ImageDataGenerator(rescale=1./255)
train_generator = train_datagen.flow_from_directory(
    TRAIN_DIR,
    target_size=IMAGE_SIZE,
    batch_size=BATCH_SIZE,
    class_mode='binary')

# Create an image data generator for validation data
valid_datagen = ImageDataGenerator(rescale=1./255)
valid_generator = valid_datagen.flow_from_directory(
    VALID_DIR,
    target_size=IMAGE_SIZE,
    batch_size=BATCH_SIZE,
    class_mode='binary')

# Check if the model already exists
if os.path.exists(MODEL_PATH):
    print("Loading existing model")
    model = tf.keras.models.load_model(MODEL_PATH)
else:
    print("Creating new model")
    # Define the model
    model = tf.keras.models.Sequential([
        tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(IMAGE_SIZE[0], IMAGE_SIZE[1], 3)),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Conv2D(64, (3, 3), activation='relu'),
        tf.keras.layers.MaxPooling2D(2, 2),
        tf.keras.layers.Flatten(),
        tf.keras.layers.Dense(512, activation='relu'),
        tf.keras.layers.Dense(1, activation='sigmoid')
    ])

# Compile the model
model.compile(loss='binary_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

# Train the model
history = model.fit(
    train_generator,
    steps_per_epoch=train_generator.samples // BATCH_SIZE,
    epochs=EPOCHS,
    validation_data=valid_generator,
    validation_steps=valid_generator.samples // BATCH_SIZE)

# Save the model
model.save(MODEL_PATH)