import os
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense

train_data_dir = "garbage_photos"
location = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
train_data_dir = os.path.join(location, train_data_dir)

input_shape = (150, 150, 3)
num_classes = 5
batch_size = 32
epochs = 20

train_datagen = ImageDataGenerator(rescale=1./255)

train_generator = train_datagen.flow_from_directory(
    train_data_dir,
    target_size=(input_shape[0], input_shape[1]),
    batch_size=batch_size,
    class_mode='categorical'
)

model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(128, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))

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

model.fit(train_generator, epochs=epochs)

classes = train_generator.class_indices

model.save("neuralModel.h5")