import tensorflow as tf
import numpy as np
from tensorflow import keras
import os
import random
from PIL import Image

img_height = 180
img_width = 180
class_names=['glass','metal','paper','plastic']
model = tf.keras.models.load_model('./saved_model_vers2')
def predict():
    path="./dane_testowe"
    files=os.listdir(path)
    d=random.choice(files)
    im = Image.open("./dane_testowe/" + d)
    im.show()
    img = keras.preprocessing.image.load_img(
        "./dane_testowe/" + d, target_size=(img_height, img_width)
    )
    img_array = keras.preprocessing.image.img_to_array(img)
    img_array = tf.expand_dims(img_array, 0) # Create a batch
    predictions = model.predict(img_array)

    score = tf.nn.softmax(predictions[0])

    print(
        "This image most likely belongs to {} with a {:.2f} percent confidence."
        .format(class_names[np.argmax(score)], 100 * np.max(score))

    )