def give_sentence():
text = tokenize("corpus.txt")
# re.sub(r'(?<=\n).*', 'START END', text)
model = MarkovModel(text)
sentence = model.random_walk()
sentence = " ".join(sentence[:-1])
# print()
# (?<=\n).*
re.sub(r'(?<=\n).*', '', sentence)
new_sentence = ""
sentence = sentence.split('\n')
# print(sentence)
if type(sentence) is list:
return max(sentence, key=len)
else:
return sentence
def butcher_word(word):
butchered = list(word)
butchered = ["START"] + butchered + ["END"]
model = MarkovModel(butchered)
new_word = model.random_walk()
return "".join(new_word[:-1])