def run(execution_context):
"""run the action"""
pause_time = app.get_config('behavior.stop.pause_time')
stop_reacts = app.get_config('behavior.stop.reacts')
time.sleep(pause_time)
tts.say_random(stop_reacts)
def run(execution_context):
"""run the action"""
FLAGS["stop"] = False
openings = app.get_config('story.opening')
no_story = app.get_config('story.no_story')
pause_time = app.get_config('story.pause_time')
stories = app.get_config('story.stories')
tag = execution_context.event.get('tag', None)
if tag is not None:
# filter stories based on tags
stories = list(filter(lambda story: tag in story.get('tags'), stories))
if len(stories) == 0:
tts.say_random(no_story)
return
tts.say_random(openings)
time.sleep(pause_time)
if FLAGS["stop"]:
return
story = stories[random.randint(0, len(stories) - 1)]
with open('cache/stories/' + story.get('file')) as data_file:
text = data_file.read()
read_long_text(text)
def run(execution_context):
"""run the action"""
filtered_word_type = app.get_config('predict.filtered_word_types')
remove_stop_words = app.get_config('train.remove_stop_words')
data_name = execution_context.event.get('data_name', 'default')
text = execution_context.event.get('text')
if text is None:
raise ValueError('text cannot be null')
config = persist.get_data_config(data_name)
tokenizer = TokenizerFactory.get_tokenizer(config.get('tokenizer'))
if config.get('clean_text'):
text = pre_process.clean_text(text, remove_stop_words)
tokenized_text = tokenizer(text)
result_word, result_proba = pre_process.predict(tokenized_text, data_name)
LOGGER.warning('predict %s with probability %2f %%', result_word,
result_proba * 100)
pos_tagged_text = nltk.pos_tag(nltk.word_tokenize(text))
filtered_text = \
[(w, word_type) for w, word_type in pos_tagged_text if not word_type in filtered_word_type]
send_msg(result_word, result_proba, filtered_text)
def say(texts, params=None, do_normalize=True):
"""Speak texts with a specified engine"""
if texts is None:
return
if do_normalize:
texts = normalize(texts, params)
global LAST_SENTENCE
LAST_SENTENCE = texts
from app import APP_INSTANCE as app
engine_name = app.get_config('engine.tts.engine')
osx_voice = app.get_config('engine.tts.osx.voice')
gtts_lang = app.get_config('engine.tts.gTTS.lang')
mary_config = app.get_config('engine.tts.mary_tts')
tts_engines = {
'pyttsx': PyttsxEngine(),
'gTTS': GTTSEngine(gtts_lang),
'ev3': EV3TTSEngine(),
'osx': OSXTTSEngine(osx_voice),
'mary_tts': MaryTTS(mary_config)
}
engine = tts_engines.get(engine_name)
if engine is None:
logging.warning('engine not configured: ' + engine_name)
return
engine.say(texts)
def run(self, execution_context):
from app import APP_INSTANCE as app
app.reload_config()
url = app.get_config('bot.url')
msg = {'name': 'reload', 'args': {}}
execution_context.finish('reload done')
http.call(url, msg)
def run(self, execution_context):
pattern = r'^[\w_]+$'
bot_name = execution_context.event.get('bot')
if bot_name is None or bot_name is '':
raise ValueError('bot name cannot be null or empty')
if not re.match(pattern, bot_name):
raise ValueError('bot name can only contains alphanumeric characters and underscore')
from app import APP_INSTANCE as app
app.reload_config(bot_name)
execution_context.finish('switched to ' + bot_name)
def run(execution_context):
"""run the action"""
event_name = execution_context.event_name.split('.')[1]
config_name = 'behavior.inquire.' + event_name
reacts = app.get_config(config_name)
txt_no_data = app.get_config('behavior.inquire.no_data')
if reacts is None or len(reacts) == 0:
LOGGER.warning('No behavior configured for ' + config_name)
tts.say_random(txt_no_data)
return
tts.say_random(reacts)
def get_vectorizer(config):
"""get the vectorizer based on config"""
vectorizer_config = app.get_config('train.vectorizer')
tokenizer_config = config.get('tokenizer')
tokenizer = TokenizerFactory.get_tokenizer(tokenizer_config)
return VectorizerFactory.get_vectorizer(vectorizer_config,
tokenizer=tokenizer)
def create_api():
"""create a twitter api"""
from twitter import OAuth, Twitter
from app import APP_INSTANCE as app
access_token = app.get_config('api.twitter.access_token')
access_secret = app.get_config('api.twitter.access_secret')
consumer_key = app.get_config('api.twitter.consumer_key')
consumer_secret = app.get_config('api.twitter.consumer_secret')
# temporary fix for certificate error
ssl._create_default_https_context = ssl._create_unverified_context
oauth = OAuth(access_token, access_secret, consumer_key, consumer_secret)
# Initiate the connection to Twitter API
return Twitter(auth=oauth)
def get_fact(group):
"""get fact by name"""
facts = app.get_config('facts')
fact = facts.get(group)
if fact is not None:
return fact
method = getattr(dynamic_facts, 'get_' + group, None)
if method is None:
return "unknown"
return method()
def find_song(song_id):
"""Find a song by id, or pick a random song if song_id is None"""
songs = app.get_config('songs')
if song_id is None:
num_song = len(songs)
rand_song_idx = random.randint(0, num_song - 1)
return songs[rand_song_idx]
else:
return next(filter(lambda s: s.id == song_id, songs), None)
def say(texts):
"""Speak texts with a specified engine"""
texts = normalize(texts)
engine_name = app.get_config('engine.tts.engine')
osx_voice = app.get_config('engine.tts.osx.voice')
gtts_lang = app.get_config('engine.tts.gTTS.lang')
mary_config = app.get_config('engine.tts.mary_tts')
tts_engines = {
'pyttsx': PyttsxEngine(),
'gTTS': GTTSEngine(gtts_lang),
'ev3': EV3TTSEngine(),
'osx': OSXTTSEngine(osx_voice),
'mary_tts': MaryTTS(mary_config)
}
engine = tts_engines.get(engine_name)
if engine is None:
logging.warning('engine not configured: ' + engine_name)
return
engine.say(texts)
def get_weather(city=None):
"""get weather forecast"""
from app import APP_INSTANCE as app
global CACHED_DATA
time_to_live = app.get_config('api.weather.time_to_live')
if has_cache(time_to_live):
LOGGER.warning('Use cached weather data')
return CACHED_DATA['CACHED_RESULT']
app_id = app.get_config('api.weather.key')
# TODO get city dynamically
if city is None:
city = app.get_config('api.weather.city')
server_url = 'http://api.openweathermap.org/data/2.5/weather?q=' + city + '&APPID=' + app_id
res, content = http.call(server_url, None, 'GET')
result = json.loads(content.decode('utf-8'))
if result is None or result.get('cod') != 200:
LOGGER.warning('Result from api server: ' + str(content))
return None
CACHED_DATA['CACHED_RESULT'] = result
CACHED_DATA['CACHED_TIME'] = time.time()
return result
def parse_csv(reader, config):
"""parse the csv file"""
remove_stop_words = app.get_config('train.remove_stop_words')
input_texts = []
output_texts = []
for row in reader:
text = row[0]
if config.get('clean_text'):
text = pre_process.clean_text(row[0], remove_stop_words)
input_texts.append(text)
row.pop(0)
output_texts.append(",".join(row))
return input_texts, output_texts
def send_msg(result_word, result_proba, filtered_text):
"""send the message to bot"""
url = app.get_config('bot.url')
msg = {
'name': result_word,
'args': {
'proba': result_proba,
'tagged_text': filtered_text
}
}
http_client = Http()
content = http_client.request(
uri=url,
method='POST',
headers={'Content-Type': 'application/json; charset=UTF-8'},
body=json.dumps(msg),
)
LOGGER.warning('response from bot: %s', content)
"""Simple REST server""" from app import APP_INSTANCE as app from bootstrap import ApplicationBootstrap app.bootstrap = ApplicationBootstrap() # export WSGI application application = app.api # run the application app.run()
def get_health_react():
"""get health react"""
health = app.get_config('facts.health')
reacts = app.get_config('behavior.health_react.' + health)
return reacts[random.randint(0, len(reacts) - 1)]
def run(self, execution_context, _):
"""run the action"""
from app import APP_INSTANCE as app
app.reload_config()
execution_context.finish()
def get_health_react():
"""get health react"""
from app import APP_INSTANCE as app
health = app.get_config('facts.health')
reacts = app.get_config('behavior.health_react.' + health)
return reacts[random.randint(0, len(reacts) - 1)]
def run(self, execution_context):
from app import APP_INSTANCE as app
app.reload_config()
execution_context.finish()
def get_algorithm():
"""get the algorithm based on config"""
algo_config = app.get_config('train.algorithm')
return AlgorithmFactory.get_algorithm(algo_config)