def main():
if len(argv) != 2:
print("Invalid arguments.")
exit(1)
elif argv[1] == "publish":
pub = Publisher()
pub.start()
elif argv[1] == "listen":
lis = Listener()
lis.start()
else:
print("Invalid argument.")
def nrdf_feed():
# Create logger instance
logger = Logger()
logrep = logger.myLogger()
# Load Config from file
_config_location = r'config.json'
with open(_config_location, 'r') as f:
_config = json.load(f)
logrep.info('Successfully loaded config file : %s' % _config_location)
# Load Selected Mongo Environment
if _config['mongo-type'] == "test":
mongoConfig = _config['mongo-test']
else:
mongoConfig = _config['mongo-live']
# Network Rail Rules state security token must be present
logrep.info('Process Name : {}'.format(_config['process-id']))
logrep.info('Network Rail Security Token : {}'.format(
_config['security-token']))
# Create Mongo Connection
mongodb = MongoClient(mongoConfig['connection-string'],
username=mongoConfig['username'],
password=mongoConfig['password'],
authSource='admin',
authMechanism='SCRAM-SHA-256')
db = mongodb[mongoConfig['db-name']]
logrep.info('MongoDB Connection Established [%s:%s]' %
(mongoConfig['connection-string'], mongoConfig['db-name']))
# Full Installation Process - Potentially needs some tweaking
if (_config['installation']['full'] == True):
logrep.info('Installation = True | Starting Installation Procedure')
ins = Installation(db, logrep, _config)
ins.importCORPUS()
ins.importSMART()
ins.importReference(_config['installation']['geography'])
ins.downloadFullSchedule()
ins.importFullSchedule()
# Create Stomp Logic
mq = stomp.Connection(host_and_ports=[
(_config['stomp-connection']['host-url'],
_config['stomp-connection']['host-port'])
],
keepalive=True,
vhost=_config['stomp-connection']['host-url'],
heartbeats=(20000, 10000))
# Create Listener Object
mq.set_listener('', Listener(mq, logrep, db, _config))
wait = input('')
mq.disconnect()
while mq.is_connected():
sleep(1)
def runLevel(self):
#start level
self.isLevelRunning = True
startTime = time.time() #begin timer
#create listener
listen2Me = Listener('user_output.txt', self.levelToRun.getValidShortcutsList())
listen2Me.start() #start listening user input and stop when shortcuts are done
self.isLevelRunning = False
#end level and record time, user input, and actions
endTime = time.time() #end timer
self.elapsedTime = endTime - startTime #record elapsed time
self.userInput = listen2Me.getCompletedShortcuts()
self.userActions = len(listen2Me.completedShortcuts) #listener needs to be changed to count all actions
ned_response = COMMANDS
else:
ned_response = DEFAULT_RESPONSE
responses.append(ned_response)
# Any karmatic changes that have happened
if commands.karmatic_entities:
karma_response = Karma().process_commands(
commands.karmatic_entities)
responses.append(karma_response)
return responses
except Exception as e:
print(e)
response = 'Great Scott! I seem to have encountered an error :('
return [response]
if __name__ == "__main__":
if slack_client.rtm_connect(with_team_state=False, auto_reconnect=True):
print("Ned is connected and running!")
slack_listener = Listener(slack_client)
while True:
commands = slack_listener.listen(slack_client.rtm_read())
if commands and commands.work_to_do:
responses = _get_responses(commands)
for response in responses:
send_response(slack_client, commands.event['channel'],
response)
time.sleep(RTM_READ_DELAY)
else:
print("Ruh roh! Connection failed. Exception traceback printed above.")
from airflow import DAG
from airflow.operators import PythonOperator, ShortCircuitOperator,\
EmailOperator
from airflow.utils.trigger_rule import TriggerRule
from datetime import datetime
from listener.listener import Listener
from listener.email import EmailService
listener = Listener()
email_service = EmailService()
default_args = {
'owner': 'M & E',
'depends_on_past': False,
'start_date': datetime(2020, 1, 1),
'email': ['*****@*****.**'],
'email_on_failure': True,
'email_on_retry': True,
'retries': 0,
}
with DAG(
'PriceAlerter',
default_args=default_args,
schedule_interval='*/5 9-17 * * *',
catchup=False,
) as dag:
price_listener = PythonOperator(
task_id='listener',
init(autoreset=True)
parser = argparse.ArgumentParser(description='An interactive bot to talk about stars and planets')
parser.add_argument('--dep_tree', default=False, type=bool, help='print dependencies tree of every heard sentence (default: False)')
parser.add_argument("--kb_file", help="file containing the knowledge-base (dafault: the last updated file in the kb directory)")
args = parser.parse_args()
# TO BUILD THE DEPENDENCIES TREE
if args.dep_tree:
stanfordnlp.download('en') # This downloads the English models for the neural pipeline
print('Building pipeline...')
nlp = stanfordnlp.Pipeline() # This sets up a default neural pipeline in English
listener = Listener()
speaker = Speaker()
bot_name = "\t\t\t\t\tAstroBot"
colorAstro = colored(bot_name +':', 'yellow')
agent = Agent(speaker, listener, colorAstro, args.kb_file)
print(f"{colorAstro} Hi, how can I help you?")
speaker.speak("Hi, how can I help you?")
while True:
command = listener.listen()
command = command.strip()
if command == "exit":
print(f"{colorAstro} Bye bye")
speaker.speak("Bye bye")
break