# from tensorflow.keras.preprocessing import image
# from tensorflow.keras import layers

# from tensorflow.keras.applications.nasnet import NASNetMobile

# from keras.models import Sequential
# from keras.layers import Dense, Dropout, Flatten
# from keras.layers import Conv2D, MaxPooling2D

# def init_model():
#     base_model = NASNetMobile(include_top=False, input_shape=(224,224,3))

#     model = Sequential()

#     model.add(base_model)
#     model.add(Flatten())
#     model.add(Dense(37, activation="softmax"))

#     model.layers[0].trainable = False

#     model.compile(loss="categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
#     model.load_weights('D:\\OneDrive\\Licenta\\SmallAPIUserAndIngredients\\resources\\save_model.hdf5')

#     return model

# model = init_model()