def predict(text):
    model = TestNeuroConfig.loaded_model
    d = keras.datasets.imdb.get_word_index()
    words = text.split()
    review = []
    for word in words:
        if word not in d:
            review.append(2)
        else:
            review.append(d[word] + 3)

    review = keras.preprocessing.sequence.pad_sequences([review],
                                                        truncating='pre', padding='pre', maxlen=10000)
    prediction = model.predict(review)
    print("Prediction (0 = negative, 1 = positive) = ", end="")
    print("%0.4f" % prediction[0][0])
    return prediction[0][0]