import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import json
import matplotlib.pyplot as plt
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.utils import load_img, img_to_array
from tensorflow.keras.utils import image_dataset_from_directory


# model_name = "beerd_25_04_2023.keras"
model_name = "beerd_imagenet_25_04_2023.keras"
img = load_img("photo_2023-04-25_10-02-25.jpg", color_mode="rgb")
img = tf.image.resize(img, (180, 180, ), "bilinear")
img_array = img_to_array(img)

test_model = keras.models.load_model(model_name)
test_loss = test_model.predict(np.expand_dims(img_array, 0))


list_labels = [fname for fname in os.listdir("assets/dog")]
list_labels.sort()
dict_names = {}
for i, label in enumerate(list_labels):
    dict_names[i] = label

with open("beerds.json", "w") as  f:
    f.write(json.dumps(dict_names))

max_val = 0
max_num = 0
for i, val in enumerate(test_loss[0]):
    if val < max_val:
        continue
    max_val = val
    max_num = i

print("-----------------------")
print(list_labels)
print(test_loss)
print(max_num, max_val, dict_names[max_num])