import tensorflow as tf
from keras import layers
from keras.models import Sequential
from keras.optimizers import Adam
from keras.utils import to_categorical
from keras.preprocessing.image import ImageDataGenerator
import os
import PIL
import PIL.Image
import numpy


def normalize(image, label):
    return image / 255, label

# Set the paths to the folders containing the training data
train_data_dir = "Training/"


# Set the number of classes and batch size
num_classes = 3
batch_size = 32

# Set the image size and input shape
img_width, img_height = 100, 100
input_shape = (img_width, img_height, 1)

train_ds = tf.keras.utils.image_dataset_from_directory(
  train_data_dir,
  validation_split=0.2,
  subset="training",
  shuffle=True,
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

val_ds = tf.keras.utils.image_dataset_from_directory(
  train_data_dir,
  validation_split=0.2,
  subset="validation",
  shuffle=True,
  seed=123,
  image_size=(img_height, img_width),
  batch_size=batch_size)

class_names = train_ds.class_names
print(class_names)

train_ds = train_ds.map(normalize)
val_ds = val_ds.map(normalize)

# Define the model architecture
model = tf.keras.Sequential([
    layers.Conv2D(16, 3, padding='same', activation='relu', input_shape=(img_height, img_width, 1)),
    layers.MaxPooling2D(),
    layers.Conv2D(32, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Conv2D(64, 3, padding='same', activation='relu'),
    layers.MaxPooling2D(),
    layers.Flatten(),
    layers.Dense(128, activation='relu'),
    layers.Dense(num_classes, activation='softmax')
])

# Compile the model
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
              metrics=['accuracy'])

model.summary()


epochs=10
# Train the model
model.fit(train_ds,
          validation_data=val_ds,
          epochs=epochs)

# Save the trained model
model.save('trained_model.h5')