from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense
from keras.preprocessing.image import ImageDataGenerator
from Func.getSubFolders import count_sub_folders

# Step 1: Load and Preprocess Images
# You can use ImageDataGenerator for on-the-fly data augmentation and normalization
train_datagen = ImageDataGenerator(
    rescale=1. / 255,
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True
)

test_datagen = ImageDataGenerator(rescale=1. / 255)

# Step 2: Label the Data
# Assume you have two classes: 'cat' and 'dog'
train_set = train_datagen.flow_from_directory(
    'Data/',
    target_size=(224, 224),  # image size
    batch_size=32,  # batch size
    class_mode='categorical'  # multiple folders in Data
)

test_set = test_datagen.flow_from_directory(
    'Data',
    target_size=(224, 224),
    batch_size=32,
    class_mode='categorical'  # Use categorical for multiple classes
)


# Step 4: Build the Model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(224, 224, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(units=128, activation='relu'))
model.add(Dense(units=count_sub_folders('Data'), activation='softmax'))

# Step 5: Compile the Model
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# Step 6: Train the Model
# model.fit(train_set, epochs=25, validation_data=test_set)

# Step 7: Evaluate the Model
# loss, accuracy = model.evaluate(test_set)
# print(f'Test loss: {loss}, Test accuracy: {accuracy}')

# Step 8: Make Predictions

# Save the trained model
# model.save('Model/Model.h5')