

def demo():
    from nltk.corpus import udhr
    langs = ['Kurdish-UTF8', 'Abkhaz-UTF8', 'Farsi_Persian-UTF8', 'Hindi-UTF8', 'Hawaiian-UTF8', 'Russian-UTF8', 'Vietnamese-UTF8', 'Serbian_Srpski-UTF8', 'Esperanto-UTF8']
    friendly = {
        'kmr': 'Northern Kurdish',
        'abk': 'Abkhazian',
        'pes': 'Iranian Persian',
        'hin': 'Hindi',
        'haw': 'Hawaiian',
        'rus': 'Russian',
        'vie': 'Vietnamese',
        'srp': 'Serbian',
        'epo': 'Esperanto',
    }
    tc = TextCat()
    for cur_lang in langs:
        raw_sentences = udhr.sents(cur_lang)
        rows = (len(raw_sentences) - 1)
        cols = list(map(len, raw_sentences))
        sample = ''
        for i in range(0, rows):
            cur_sent = ''
            for j in range(0, cols[i]):
                cur_sent += (' ' + raw_sentences[i][j])
            sample += guess
        print((('Language snippet: ' + sample[0:140]) + '...'))
        guess = tc.guess_language(sample)
        print(('Language detection: %s (%s)' % (guess, friendly[guess])))
        print(('#' * 140))

dataset/ETHPy150Open nltk/nltk/nltk/classify/textcat.py demo/VarMisuse@186/342 `cur_sent`->`guess`