How to avoid loading a machine learning model every time?

I am trying to do something very simple. Get a text from command line. Evaluate its sentiment based on a certain model and return the sentiment and its associated score to the the console. How do I avoid loading the model every time?

import argparse
import sys
from flair.models import TextClassifier
from flair.data import Sentence

model=None
def load_model():
    global model
    model = TextClassifier.load('en-sentiment')

def sentiment_score(s, threshold = 0.33):

    sentence = Sentence(s)
    model.predict(sentence)
    total_sentiment = sentence.labels[0]
    assert total_sentiment.value in ['POSITIVE', 'NEGATIVE']
    sign = 1 if total_sentiment.value == 'POSITIVE' else -1
    score = total_sentiment.score * sign

    if score > threshold:
        return ('POSITIVE', score)
    elif score < -threshold:
        return ('NEGATIVE', score)
    else:
        return ('NEUTRAL', score)

def get_input():
    try:
        parser = argparse.ArgumentParser()
        parser.add_argument("text", help="display the sentiment of a text.", type=str)
        args = parser.parse_args()
        return args.text

    except:
        e = sys.exc_info()[0]
        print(e)


if __name__ == "__main__":
    load_model()
    text = get_input()
    print(sentiment_score(text))