def render_page():
my_list = read_file('tintern_abbey.txt')
chain = MarkovChain(my_list)
num_words = int(10) - 1
my_sentence = chain.walk(num_words)
my_list2 = read_file("the_rime.txt")
chain2 = MarkovChain(my_list2)
num_words2 = int(10) - 1
my_sentence2 = chain2.walk(num_words2)
return render_template('index.html',
sentence=my_sentence,
sentence2=my_sentence2)
def make_the_words():
# build Histogram
# my_file = open("./words.txt", "r")
# # absolute path -> ./file.ext ## more fuctional for live deploy
# lines = my_file.readlines()
filename = "transient.txt"
lines = open(filename, "r").readlines()
transient_txt_words = [] # word_list
for line in lines:
wordslist = line.split(' ')
for word in wordslist:
word = word.strip(' . , ;" \n _ ?')
transient_txt_words.append(word)
my_histogram = histogram(transient_txt_words)
# put together words into a sentence
sentence = ''
num_words = 10
''' # comment out to impliment markov
for i in range(num_words):
word = sample_by_frequency(my_histogram)
sentence = sentence + " " + word '''
# uncomment to impliment markov
markovchain = MarkovChain(transient_txt_words)
sentence = markovchain.walk(num_words)
return sentence
def markov(num=0):
list_of_words = words_list()
markovChain = MarkovChain(list_of_words)
sentence = markovChain.walk(10)
return sentence
def gen_word():
my_file = open("./words.txt", "r")
lines = my_file.readlines()
my_histogram = histogram(lines)
sentence = ""
num_words = 10
# for i in range(num_words):
# word = sample(my_histogram)
# sentence += " " + word
markovchain = MarkovChain(lines)
sentence = markovchain.walk(num_words)
return sentence
def generate_words():
'''my_histogram = (lines)
sentence = ""
num_words = 10
for i in range (num_words):
word = weighted_sample(my_histogram)
sentence += " " + word
return sentence'''
markovchain = MarkovChain(
["one", "fish", "two", "fish", "red", "fish", "blue", "fish"])
return markovchain.walk(10)
def sample(self, outf, nr_frames=1e6, n=3):
'''Sample using an n-gram into the given file.'''
chain = MarkovChain(n)
chain.add_sequence(self.buf)
gen = chain.walk()
out = wave.open(outf, 'wb')
out.setparams(self.params)
out.setnframes(nr_frames)
chunk = nr_frames / 100
for k in xrange(int(nr_frames)):
if k % chunk == 0:
print k / chunk, "%"
out.writeframes(self.repr_to_pcm(next(gen)))
def render_page():
my_list = read_file('plato.txt')
chain = MarkovChain(my_list)
num_words = int(10) - 1
my_sentence = chain.walk(num_words)
my_sentence_2 = chain.walk(num_words)
my_sentence_3 = chain.walk(num_words)
my_sentence_4 = chain.walk(num_words)
my_sentence_5 = chain.walk(num_words)
return render_template('index.html', sentence=my_sentence,
sentence2=my_sentence_2,
sentence3=my_sentence_3,
sentence4=my_sentence_4,
sentence5=my_sentence_5)
def create_sentence(word_num):
source_text = "nietsche.txt"
with open(source_text, "r") as file:
og_text = file.read()
word_list = og_text.split()
for index, word in enumerate(word_list):
word_list[index] = word.rstrip()
chain = MarkovChain(word_list)
chain.print_chain()
sentence_words = []
sentence = chain.walk(word_num)
return sentence
def generate_words():
my_file = open("./words.txt", "r")
lines = my_file.readlines()
my_histogram = histogram(lines)
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
sentence = ""
num_words = 10
# for i in range(num_words):
# word = sample_by_frequency(my_histogram)
# sentence += " " + word
markovchain = MarkovChain(word_list)
sentence = markovchain.walk(num_words)
return sentence
def generate_words():
words_list = []
with open('./EAP.text') as f:
lines = f.readlines()
for line in lines:
for word in line.split():
words_list.append(word)
#lines = Dictogram(['one', 'fish', 'two', 'fish', 'red', 'fish', 'blue', 'fish'])
markovchain = MarkovChain(words_list)
'''sentence = ""
num_words = 20
for i in range(num_words):
word = lines.sample()
sentence += " " + word
return sentence'''
sentence = markovchain.walk(24)
return render_template('index.html', sentence=sentence)
def hello():
# hs = histogram("words.txt")
# samp = sample(hs)
my_file = open("./words.txt", "r")
lines = my_file.readlines()
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
print(word_list)
markovchain = MarkovChain(word_list)
# return samp
# num_words = 10
return (markovchain.walk(20))
def generate_words():
#Build a histogram
my_file = histogram("./text.txt")
lines = my_file.readlines()
my_histogram = histogram(lines)
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
sentence = ""
num_words = 10
# for i in range(num_words):
# #sample/frequency goes here
# word = sample(my_histogram)
# sentence += " " + word
# return sentence
markovchain = MarkovChain(word_list)
sentence = markovchain.walk(num_words)
return sentence
def generate_words():
#build a histogram
my_file = open("words.txt","r")
lines = my_file.readlines()
my_histogram = Histogram(lines)
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
word = weighted_sample(my_histogram)
#return word
sentence = ""
num_words = 10
# for i in range(num_words):
# word = weighted_sample(my_histogram)
# sentence += " " + word
markovChain = MarkovChain(word_list)
sentence = markovChain.walk(num_words)
print("sentence", sentence)
return sentence
def generate_words():
#build a histogram
# my_file = open("despacito.txt","r")
lines = "one fish two fish red fish blue fish"
my_histogram = histogram(lines)
word_list = []
for line in lines:
for word in line.split():
word_list.append(word)
word = sample(my_histogram)
#return word
sentence = ""
num_words = 10
# for i in range(num_words):
# word = weighted_sample(my_histogram)
# sentence += " " + word
markovChain = MarkovChain(word_list)
sentence = markovChain.walk(num_words)
print("sentence", sentence)
return sentence
def markov():
word_list = words_list()
markov_chain = MarkovChain(word_list)
sentence = markov_chain.walk(10)
return render_template('index.html', tweet=sentence)
def walk_corpus(fname):
with open(fname, 'r') as f:
words = f.read().split()
chain = MarkovChain(5)
chain.add_sequence(words)
return chain.walk()
def hello_world():
temp = MarkovChain("YOU UNDERSTAND MY NAME AND YOU HEAR IT IN YOUR BONES AND IN YOUR TEETH AND YOU KNOW WHO I AM AND YOU KNOW WHY YOU MUST LISTEN TO ME".split())
return temp.walk(10)
def generate_words():
textFile = open('./text.txt')
text = textFile.read().split()
chain = MarkovChain(text)
sentence = chain.walk(20).capitalize() + '.'
return render_template('index.html', sentence=sentence)
def hello_world():
temp = MarkovChain('one fish two fish red fish blue fish'.split())
return temp.walk(5)
def walk_corpus(fname):
with open(fname, 'r') as f:
words = f.read().split()
chain = MarkovChain(5)
chain.add_sequence(words)
return chain.walk()
def walk_corpus():
chain = MarkovChain(mc_nodes)
chain.add_sequence(corpus)
return chain.walk()
