app.py

from huggingface_hub import from_pretrained_keras
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.applications import inception_v3

model = from_pretrained_keras("keras-io/deep-dream")

#base_image_path = keras.utils.get_file("sky.jpg", "https://i.imgur.com/aGBdQyK.jpg")
result_prefix = "dream"

# These are the names of the layers
# for which we try to maximize activation,
# as well as their weight in the final loss
# we try to maximize.
# You can tweak these setting to obtain new visual effects.
layer_settings = {
    "mixed4": 1.0,
    "mixed5": 1.5,
    "mixed6": 2.0,
    "mixed7": 2.5,
}

# Playing with these hyperparameters will also allow you to achieve new effects
step = 0.01  # Gradient ascent step size
num_octave = 3  # Number of scales at which to run gradient ascent
octave_scale = 1.4  # Size ratio between scales
iterations = 20  # Number of ascent steps per scale
max_loss = 15.0

def preprocess_image(image_path):
    # Util function to open, resize and format pictures
    # into appropriate arrays.
    img = keras.preprocessing.image.load_img(image_path)
    img = keras.preprocessing.image.img_to_array(img)
    img = np.expand_dims(img, axis=0)
    img = inception_v3.preprocess_input(img)
    return img


def deprocess_image(x):
    # Util function to convert a NumPy array into a valid image.
    x = x.reshape((x.shape[1], x.shape[2], 3))
    # Undo inception v3 preprocessing
    x /= 2.0
    x += 0.5
    x *= 255.0
    # Convert to uint8 and clip to the valid range [0, 255]
    x = np.clip(x, 0, 255).astype("uint8")
    return x
    
 # Get the symbolic outputs of each "key" layer (we gave them unique names).
outputs_dict = dict(
    [
        (layer.name, layer.output)
        for layer in [model.get_layer(name) for name in layer_settings.keys()]
    ]
)

# Set up a model that returns the activation values for every target layer
# (as a dict)
feature_extractor = keras.Model(inputs=model.inputs, outputs=outputs_dict)

def compute_loss(input_image):
    features = feature_extractor(input_image)
    # Initialize the loss
    loss = tf.zeros(shape=())
    for name in features.keys():
        coeff = layer_settings[name]
        activation = features[name]
        # We avoid border artifacts by only involving non-border pixels in the loss.
        scaling = tf.reduce_prod(tf.cast(tf.shape(activation), "float32"))
        loss += coeff * tf.reduce_sum(tf.square(activation[:, 2:-2, 2:-2, :])) / scaling
    return loss
    
 def gradient_ascent_step(img, learning_rate):
    with tf.GradientTape() as tape:
        tape.watch(img)
        loss = compute_loss(img)
    # Compute gradients.
    grads = tape.gradient(loss, img)
    # Normalize gradients.
    grads /= tf.maximum(tf.reduce_mean(tf.abs(grads)), 1e-6)
    img += learning_rate * grads
    return loss, img


def gradient_ascent_loop(img, iterations, learning_rate, max_loss=None):
    for i in range(iterations):
        loss, img = gradient_ascent_step(img, learning_rate)
        if max_loss is not None and loss > max_loss:
            break
        print("... Loss value at step %d: %.2f" % (i, loss))
    return img
    
    
def process_image(imgPath):
  original_img = preprocess_image(base_image_path)
  original_shape = original_img.shape[1:3]

  successive_shapes = [original_shape]
  for i in range(1, num_octave):
      shape = tuple([int(dim / (octave_scale ** i)) for dim in original_shape])
      successive_shapes.append(shape)
  successive_shapes = successive_shapes[::-1]
  shrunk_original_img = tf.image.resize(original_img, successive_shapes[0])

  img = tf.identity(original_img)  # Make a copy
  for i, shape in enumerate(successive_shapes):
      print("Processing octave %d with shape %s" % (i, shape))
      img = tf.image.resize(img, shape)
      img = gradient_ascent_loop(
          img, iterations=iterations, learning_rate=step, max_loss=max_loss
      )
      upscaled_shrunk_original_img = tf.image.resize(shrunk_original_img, shape)
      same_size_original = tf.image.resize(original_img, shape)
      lost_detail = same_size_original - upscaled_shrunk_original_img

      img += lost_detail
      shrunk_original_img = tf.image.resize(original_img, shape)

  return deprocess_image(img.numpy())
  
image = gr.inputs.Image()
label = gr.outputs.Image()

iface = gr.Interface(classify_image,image,label,
	#outputs=[
	#        gr.outputs.Textbox(label="Engine issue"),
       	#	gr.outputs.Textbox(label="Engine issue score")], 
	examples=["sky.jpg"],  title="Image classification on CIFAR-100",
	description = "Model for classifying  images from the CIFAR dataset using a vision transformer trained with small data.",
        article = "Author: <a href=\"https://huggingface.co/joheras\">Jónathan Heras</a>"
#	examples = ["sample.csv"],
)


iface.launch()