How to use the markovify.NewlineText function in markovify

def test_custom_regex(self):
        with self.assertRaises(Exception) as context:
            model = markovify.NewlineText('This sentence contains a custom bad character: #.', reject_reg=r'#')

        with self.assertRaises(Exception) as context:
            model = markovify.NewlineText('This sentence (would normall fail')

        model = markovify.NewlineText('This sentence (would normall fail', well_formed = False)

def get_text_model(channel, user, quotes):
    if quotes and user:
        lines = db.Quote.find(channel=channel, user=user)
    elif quotes:
        lines = db.Quote.find(channel=channel)
    elif user:
        lines = db.Message.find(channel=channel, user=user)
    else:
        lines = (
            db.Message.select()
            .where(db.Message.channel == channel)
            .where(db.Message.user != 'jarvis'))
    lines = lines.order_by(db.peewee.fn.Random()).limit(1000)
    text = '\n'.join([i.text for i in lines])
    return markovify.NewlineText(text)

def command_markov(self, event, entity):
        if isinstance(entity, DiscoUser):
            q = Message.select().where(Message.author_id == entity.id).limit(500000)
        else:
            q = Message.select().where(Message.channel_id == entity.id).limit(500000)

        text = [msg.content for msg in q]
        self.models[entity.id] = markovify.NewlineText('\n'.join(text))
        event.msg.reply(u':ok_hand: created markov model for {} using {} messages'.format(entity, len(text)))

c.execute('SELECT text FROM markov_rants')
        rants = c.fetchall()

    # Normalize the quote data... Get rid of IRC junk
    clean_quotes = [normalize_quote(d['quote']) for d in quotes]

    # Normalize the inspire data... Just lightly prune authors
    clean_inspirations = [normalize_inspiration(d['text']) for d in inspirations]

    # Normalize the rant data... just remove ending punctuation
    clean_rants = [normalize_rant(d['text']) for d in rants]

    # Create the three models, and combine them.
    # More heavily weight our quotes and rants
    rants_model = markovify.NewlineText('\n'.join(clean_rants))
    quotes_model = markovify.NewlineText('\n'.join(clean_quotes))
    inspire_model = markovify.NewlineText('\n'.join(clean_inspirations))
    return markovify.combine([quotes_model, rants_model, inspire_model], model_weights)

def augment_data(x_data, y_data,augment_size=50):
    '''augment the data by markovify to improve the imbalance issue
    https://towardsdatascience.com/nlg-for-fun-automated-headlines-generator-6d0459f9588f'''
    #To-do, solve the generate None bug
    x_dataaug,y_dataaug=[],[]
    for label in set(y_data):
        label_indices=np.where(y_data==label)
        n_samples=len(label_indices[0])
        if n_samples

id = str(acc['id'])
      try:
        since_id = self.scrape_id(id, since=acctjson[id])
      except:
        since_id = self.scrape_id(id)
      acctjson[id] = since_id
    
    with open(acctfile, 'w') as f:
      json.dump(acctjson, f)
      
    # generate the whole corpus after scraping so we don't do at every runtime
    combined_model = None
    for (dirpath, _, filenames) in os.walk(self.corpus_dir_name):
      for filename in filenames:
        with open(os.path.join(dirpath, filename)) as f:
          model = markovify.NewlineText(f, retain_original=False)
          if combined_model:
            combined_model = markovify.combine(models=[combined_model, model])
          else:
            combined_model = model
    with open(self.model_name,'w') as f:
      f.write(combined_model.to_json())

# Fetch iconic FOSS rants
        c.execute('SELECT text FROM markov_rants')
        rants = c.fetchall()

    # Normalize the quote data... Get rid of IRC junk
    clean_quotes = [normalize_quote(d['quote']) for d in quotes]

    # Normalize the inspire data... Just lightly prune authors
    clean_inspirations = [normalize_inspiration(d['text']) for d in inspirations]

    # Normalize the rant data... just remove ending punctuation
    clean_rants = [normalize_rant(d['text']) for d in rants]

    # Create the three models, and combine them.
    # More heavily weight our quotes and rants
    rants_model = markovify.NewlineText('\n'.join(clean_rants))
    quotes_model = markovify.NewlineText('\n'.join(clean_quotes))
    inspire_model = markovify.NewlineText('\n'.join(clean_inspirations))
    return markovify.combine([quotes_model, rants_model, inspire_model], model_weights)