import numpy as np
import tensorflow as tf
from tensorflow import keras

model = keras.models.load_model('sign_char_detection_model')


class_names = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', 'del', 'nothing', 'space']


def pred_sign_char(path_to_img: str):

    pred = []
    test_image = tf.keras.utils.load_img(path_to_img, target_size = (256, 256))
    test_image = tf.keras.utils.img_to_array(test_image)
    test_image = np.expand_dims(test_image, axis = 0)
    result = model.predict(test_image)


    print(result)
    pred.append(class_names[np.argmax(result)])
    print(pred)
    return pred[0]