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Trump12.py
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Trump12.py
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import nltk, os, markovify, re
from random import randint
class POSifiedText(markovify.Text):
def word_split(self, sentence):
words = re.split(self.word_split_pattern, sentence)
words = [ "::".join(tag) for tag in nltk.pos_tag(words) ]
return words
def word_join(self, words):
sentence = " ".join(word.split("::")[0] for word in words)
return sentence
def gen_sentences(sen, model):
result = ""
for i in range(sen):
result += (model.make_sentence()) + '\n'
return result
def full_corpus_trump():
models = []
for file in os.listdir("./Trump/"):
if file.endswith(".speech"):
with open("./Trump/" + file) as f:
text = f.read()
models.append(markovify.Text(text))
model_combo = markovify.combine(models)
return gen_sentences(randint(1,3), model_combo)
def full_corpus_clinton():
models = []
for file in os.listdir("./Clinton"):
if file.endswith(".speech"):
with open ("./Clinton/" + file) as f:
text = f.read()
models.append(markovify.Text(text))
model_combo = markovify.combine(models)
return gen_sentences(randint(1,3), model_combo)
def trump_respond(text):
output = ""
tw = nltk.word_tokenize(text)
tt = nltk.pos_tag(tw)
nouns = [item[0] for item in tt if item[1][0] == 'N']
if nouns == []:
output = full_corpus_trump()
else:
#idx = nltk.text.ContextIndex([word.lower() for word in nltk.corpus.brown.words()])
save = []
for word in nouns:
save.append(unicode(word))
#similar = idx.similar_words(word)
#for item in similar:
# save.append(item)
lines = []
for file in os.listdir("./Trump/"):
if file.endswith(".speech"):
with open("./Trump/" + file) as f:
text = f.readlines()
for i, line in enumerate(text):
#if any(q in line for q in save):
if set(line).intersection(save) != set([]):
lines.append(line)
if lines == []:
output = full_corpus_trump()
else:
models = []
for line in lines:
models.append(markovify.Text(line))
model_combo = markovify.combine(models)
output = gen_sentences(randint(1,3), model_combo)
return output.decode('utf-8', 'ignore')
def clinton_respond(text):
output = ""
tw = nltk.word_tokenize(text)
tt = nltk.pos_tag(tw)
nouns = [item[0] for item in tt if item[1][0] == 'N']
if nouns == []:
output = full_corpus_clinton()
else:
#idx = nltk.text.ContextIndex([word.lower() for word in nltk.corpus.brown.words()])
save = []
for word in nouns:
save.append(unicode(word))
#similar = idx.similar_words(word)
#for item in similar:
# save.append(item)
lines = []
for file in os.listdir("./Clinton/"):
if file.endswith(".speech"):
with open("./Clinton/" + file) as f:
text = f.readlines()
for i, line in enumerate(text):
if set(line).intersection(save) != set([]):
lines.append(line)
if lines == []:
output = full_corpus_clinton()
else:
models = []
for line in lines:
models.append(markovify.Text(line))
model_combo = markovify.combine(models)
output = gen_sentences(randint(1,3), model_combo)
return output.decode('utf-8', 'ignore')