Example #1
0
def rating(rating):
	print 'Adjusting Markov chain...'
	data = request.form['lyrics']
	new_chain = cc.ngrams(data.split(), NGRAM_SIZE)
	for key in new_chain:
		if key in markov_chain:
			for new_item in new_chain[key]:
				for index,item in enumerate(markov_chain[key]):
					if item[0] == new_item[0]:
						if item[1] + rating > 0:
							markov_chain[key][index] = (item[0], item[1] + rating)
		else:
			markov_chain[key] = new_chain[key]
	return str(rating)
Example #2
0
def rating(rating):
    print 'Adjusting Markov chain...'
    data = request.form['lyrics']
    new_chain = cc.ngrams(data.split(), NGRAM_SIZE)
    for key in new_chain:
        if key in markov_chain:
            for new_item in new_chain[key]:
                for index, item in enumerate(markov_chain[key]):
                    if item[0] == new_item[0]:
                        if item[1] + rating > 0:
                            markov_chain[key][index] = (item[0],
                                                        item[1] + rating)
        else:
            markov_chain[key] = new_chain[key]
    return str(rating)
Example #3
0
		self.msg = msg

# initialization
app = Flask(__name__, static_folder='static')
app.config.update(
    DEBUG = True,
)

content_text = ''
for fname in os.listdir('corpus/'):
	if fname.endswith('.txt'):
		f = open('corpus/' + fname, 'r')
		content_text += f.read() + ' '

temp = cc.tokenize(content_text)
markov_chain = cc.ngrams(temp, NGRAM_SIZE)

def generate_title():
	i = 0
	while i < 100:
		i += 1
		title = ' '.join(cc.generate_sentence(markov_chain, 3)).split()
		for j,e in enumerate(title):
			if cc.is_noun(title[j]):
				i = 1000
				break
	return ' '.join(title[:j+1])

@app.route('/get_title')
def get_title():
	return cc.prettify(generate_title())
Example #4
0
        self.title = title
        self.msg = msg


# initialization
app = Flask(__name__, static_folder='static')
app.config.update(DEBUG=True, )

content_text = ''
for fname in os.listdir('corpus/'):
    if fname.endswith('.txt'):
        f = open('corpus/' + fname, 'r')
        content_text += f.read() + ' '

temp = cc.tokenize(content_text)
markov_chain = cc.ngrams(temp, NGRAM_SIZE)


def generate_title():
    i = 0
    while i < 100:
        i += 1
        title = ' '.join(cc.generate_sentence(markov_chain, 3)).split()
        for j, e in enumerate(title):
            if cc.is_noun(title[j]):
                i = 1000
                break
    return ' '.join(title[:j + 1])


@app.route('/get_title')