get_result_score.py

from keras import load_model
model = load_model(path) # open saved model/weights from .h5 file

def predict_image(image):
    image = np.array(image, dtype='float32')
    image /= 255
    pred_array = model.predict(image)

    # model.predict() returns an array of probabilities - 
    # np.argmax grabs the index of the highest probability.
    result = gesture_names[np.argmax(pred_array)]
    
    # A bit of magic here - the score is a float, but I wanted to
    # display just 2 digits beyond the decimal point.
    score = float("%0.2f" % (max(pred_array[0]) * 100))
    print(f'Result: {result}, Score: {score}')
    return result, score