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#Install  Keras Tuner
!pip install -U keras-tuner
import kerastuner as kt

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generator = generator_model()
discriminator = discriminator_model()
generator_optimizer = tf.keras.optimizers.Adam(0.0002,beta_1=0.5)
discriminator_optimizer = tf.keras.optimizers.Adam(0.0002, beta_1=0.5)

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@tf.function
def train_step(images, generator, discriminator, generator_optimizer, discriminator_optimizer):
    noise = tf.random.normal([BATCH_SIZE, NOISE_DIM])

    with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:
        generated_images = generator(noise, training=True)
        real_output = discriminator(images, training=True)
        fake_output = discriminator(generated_images, training=True)
        gen_loss = generator_loss(fake_output)
        disc_loss = discriminator_loss(real_output, fake_output)

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fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator', alpha=0.5)
plt.plot(losses.T[1], label='Generator', alpha=0.5)
plt.title("Training Losses")
plt.legend()

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def discriminator_model():
    model = tf.keras.Sequential()
    model.add(layers.Conv2D(128, (5, 5), strides=(2, 2), padding='same', input_shape=[32, 32, 3]))
    model.add(layers.LeakyReLU())
   
    model.add(layers.Conv2D(256, (5, 5), strides=(2, 2), padding='same'))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Conv2D(512, (5, 5), strides=(2, 2), padding='same'))

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def generator_model():
    model = tf.keras.Sequential()
    model.add(layers.Dense(4*4*512, use_bias=False, input_shape=(100,)))
    model.add(layers.BatchNormalization())
    model.add(layers.LeakyReLU())

    model.add(layers.Reshape((4, 4, 512)))
    assert model.output_shape == (None, 4, 4, 512) # Note: None is the batch size

    model.add(layers.Conv2DTranspose(256, (5, 5), strides=(2, 2), padding='same', use_bias=False))

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def scale(x, feature_range=(-1, 1)):
    # scale to (0, 1)
    x = ((x - x.min())/(255 - x.min()))
    # scale to feature_range
    min, max = feature_range
    x = x * (max - min) + min
    return x
      
class Dataset:
    def __init__(self, train, test, val_frac=0.5, shuffle=False, scale_func=None):

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from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm

!mkdir data
data_dir = 'data/'

if not isdir(data_dir):
    raise Exception("Data directory doesn't exist!")

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class MainActivity : AppCompatActivity() {
    private lateinit var mClassifier: Classifier
    private lateinit var mBitmap: Bitmap

    private val mCameraRequestCode = 0
    private val mGalleryRequestCode = 2

    private val mInputSize = 224
    private val mModelPath = "plant_disease_model.tflite"
    private val mLabelPath = "plant_labels.txt"

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 fun scaleImage(bitmap: Bitmap?): Bitmap {
        val orignalWidth = bitmap!!.width
        val originalHeight = bitmap.height
        val scaleWidth = mInputSize.toFloat() / orignalWidth
        val scaleHeight = mInputSize.toFloat() / originalHeight
        val matrix = Matrix()
        matrix.postScale(scaleWidth, scaleHeight)
        return Bitmap.createBitmap(bitmap, 0, 0, orignalWidth, originalHeight, matrix, true)
    }