# Part 3 - Making new predictions
import numpy as np
from PIL import Image, ImageDraw, ImageFont
from keras.preprocessing import image
from keras.models import model_from_yaml
from keras.preprocessing.image import ImageDataGenerator
import argparse

parser = argparse.ArgumentParser()
parser.add_argument('images', nargs="*", help="images to classify")
args = parser.parse_args()

# load YAML and create model
yaml_file = open('model.yaml', 'r')
loaded_model_yaml = yaml_file.read()
yaml_file.close()
classifier = model_from_yaml(loaded_model_yaml)
# load weights into new model
classifier.load_weights("model.h5")
print("Loaded model from disk")


for f in args.images:
    from keras.preprocessing import image
    test_image = image.load_img(f, target_size = (64, 64))
    test_image = image.img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis = 0)
    result = classifier.predict(test_image)
    print(result)



    #WHAT ARE YOUR CLASSES?
    if result[0][0] == 1:
        prediction = 'clean'
    else:
        prediction = 'annotated'

    print("PREDICTION: {}".format(prediction))


    #WRITE RESULT TO IMAGE
    image = Image.open(f)
    width, height = image.size
    size = (width, height+100)
    layer = Image.new('RGB', size, (255,255,255))
    layer.paste(image, (0,0))

    draw = ImageDraw.Draw(layer)
    font = ImageFont.truetype('SpaceMono-Regular.ttf', size=45)
    (x, y) = (50, height+20)
    message = prediction
    color = 'rgb(0, 0, 0)' # black color
    draw.text((x, y), message, fill=color, font=font)


    layer.save("{}.predicted.png".format(f))