def get(self):
m = MarkovChains()
m.load_db('gquery2')
users = m.db.get_users()
values = {'users': users}
self.response.headers['Content-Type'] = 'text/xml'
self.response.out.write(template.render(self.path, values))
def post(self):
text = self.request.get('sentences')
user = self.request.get('user', default_value=None)
m = MarkovChains()
m.load_db('gquery2')
m.db.store_sentence(text)
values = {}
self.response.out.write(template.render(self.path, values))
def get(self):
m = MarkovChains()
m.load_db('gquery2')
word = self.request.get('word', default_value=None)
text = m.db.fetch_new_sentence()
taskqueue.add(url='/task/talk')
values = {'text': text}
self.response.out.write(template.render(self.path, values))
def get(self):
m = MarkovChains()
m.load_db('gquery')
user = self.request.get('user', default_value=None)
if user:
chains = m.db.uchain.all()
else:
chains = m.db.chain.all()
values = {'chains': chains}
self.response.out.write(template.render(self.path, values))
def get(self):
filename = os.path.join('db','sentence_get.xml')
path = get_path(filename)
m = MarkovChains()
m.load_db('gquery2')
word = self.request.get('first_word', default_value=None)
user = self.request.get('user', default_value=None)
text = m.db.fetch_new_sentence()
taskqueue.add(url='/task/talk')
values = {'text': text}
self.response.headers['Content-Type'] = 'text/xml'
self.response.out.write(template.render(path, values))
def post(self):
if self.request.get('sentences'):
text = self.request.get('sentences')
m = MarkovChains()
m.analyze_sentence(text)
word = self.request.get('first_word', default_value=None)
result = m.make_sentence(word=word)
values = {'result':result}
self.response.headers['Content-Type'] = 'text/xml'
self.response.out.write(template.render(self.path, values))
else:
values = {'result':''}
self.response.headers['Content-Type'] = 'text/xml'
self.response.out.write(template.render(self.path, values))
def post(self):
if self.request.get('sentences'):
text = self.request.get('sentences')
m = MarkovChains()
m.analyze_sentence(text)
word = self.request.get('word', default_value=None)
result = m.make_sentence(word=word)
_chaindic = m.chaindic
chaindic = []
for prewords in _chaindic:
for postword in _chaindic[prewords]:
if _chaindic[prewords][postword].isstart:
chaindic.append((prewords[0],prewords[1],postword))
values = {'result':result, 'chaindic':chaindic, 'original':text}
self.response.out.write(template.render(self.path, values))
else:
values = {'result':''}
self.response.out.write(template.render(self.path, values))
def post(self):
text = self.request.get('sentences')
user = self.request.get('user')
m = MarkovChains()
m.load_db('gquery2')
m.db.store_sentence(text)
def post(self):
m = MarkovChains()
m.load_db('gquery2')
m.db.store_new_sentence()
OAuth の各種キーを読み込む
"""
parser = SafeConfigParser()
parser.readfp(open('config.ini'))
sec = 'oauth'
consumer_key = parser.get(sec, 'consumer_key')
consumer_secret = parser.get(sec, 'consumer_secret')
access_token = parser.get(sec, 'access_token')
access_token_secret = parser.get(sec, 'access_token_secret')
sec = 'twilog'
original_id = parser.get(sec, 'original_id')
sec = 'bot'
tweet_type = int(parser.get(sec, 'tweet_type'))
markov = MarkovChains()
api = twoauth.api(consumer_key, consumer_secret, access_token,
access_token_secret)
class DoReply(db.Model):
flg = db.BooleanProperty()
"""
sentence.txt を読み込む
"""
sentences = []
