from transformers import pipeline

classifier = pipeline("text-classification",
                      model='bhadresh-savani/distilbert-base-uncased-emotion',
                      return_all_scores=True)


def get_emotion(text='No text yet'):
    prediction = classifier(text)[0]
    result = sorted(prediction, key=lambda x: x['score'])[::-1]
    return result


sentiment_map = {'anger': 'neg', 'sadness': 'neg', 'fear': 'neg',
                 'joy': 'pos', 'love': 'pos', 'surprise': 'pos'}
good_arcs = ['neg - pos', 'pos - neg']
great_arcs = ['pos - neg - pos', 'neg - pos - neg']


def get_sentiment_arc_evaluation(emotions):
    sentiment_arc = []
    for emo in emotions:
        sentiment = sentiment_map[emo]
        if sentiment_arc and sentiment_arc[-1] == sentiment:
            continue
        sentiment_arc.append(sentiment)
    sentiment_arc_str = ' - '.join(sentiment_arc)
    if sentiment_arc_str in great_arcs:
        return 'What a great plot! Excellent! 😍'
    elif sentiment_arc_str in good_arcs:
        return 'Story plot seems nice! But you can do better. 😉'
    elif len(sentiment_arc) < 2:
        return "No judgment, but... The plot might be too simple! 🤓"
    else:
        return "The plot seems complicated. 🤔 But maybe I am just too stupid to understand!"