def can_process(self, statement):
self.data = keywords.get_topic(statement.text,
parse_location=False)
if "name" in self.data.keys():
return self.data["name"] == "restaurant"
return False
def main():
print("Hello! I am C1C0. I can answer questions and execute commands.")
while True:
# gets a tuple of phrase and confidence
answer = live_streaming.main()
speech = live_streaming.get_string(answer)
confidence = live_streaming.get_confidence(answer)
print(speech)
# speech = input()
if "quit" in speech or "stop" in speech:
break
if("cico" in speech.lower() or "kiko" in speech.lower() or "c1c0" in speech.lower()) and \
("hey" in speech.lower()):
# filter out cico since it messes with location detection
speech = utils.filter_cico(speech)
if face_recognition.isFaceRecognition(speech):
print(face_recognition.faceRecog(speech))
# task is to transfer over to facial recognition client program
elif path_planning.isLocCommand(speech.lower()):
print("Move command: ")
command = path_planning.process_loc(speech.lower())
print(command)
runFromChatBot(command)
# task is to transfer over to path planning on the system
elif object_detection.isObjCommand(speech.lower()):
print("Object to pick up: " +
object_detection.object_parse(speech.lower()))
# task is to transfer over to object detection on the system
else:
# we don't want the text to be lowercase since it messes with location detection
response = "Sorry, I don't understand."
data = keywords.get_topic(speech, parse_location=False)
keywords.modify_topic_data(data, parse_location=True)
if "name" in data.keys() and data["name"] == "weather":
api_data = weather.lookup_weather_today_city(
data["info"]["location"]["name"])
response = make_response.make_response_api(data, api_data)
elif "name" in data.keys() and data["name"] == "restaurant":
api_data = restaurant.lookup_restaurant_city(
data["info"]["location"]["name"])
response = make_response.make_response_api(data, api_data)
else:
# Q&A system
response = get_topic(speech)
print(response)
def main():
live_streaming.delete_file()
weather.import_keys()
while True:
#gets a tuple of phrase and confidence
answer = live_streaming.main()
speech = live_streaming.get_string(answer)
confidence = live_streaming.get_confidence(answer)
if speech == "quit":
break
topic = keywords.get_topic(speech)
print(topic)
if topic["name"] == "weather":
print("weather is called")
weather_data = weather.lookup_weather_today_city("ithaca new york")
# print(weather_data)
response = make_response.make_response_api(topic, weather_data)
print(response)
elif topic["name"] == "restaurant":
print("restaurant is called")
restaurant_data = restaurant.lookup_restaurant_city(
"ithaca new york")
response = make_response.make_response_api(topic, restaurant_data)
print(response)
def main():
print("Hello! I am C1C0. I can answer questions and execute commands.")
while True:
# gets a tuple of phrase and confidence
answer = live_streaming.main()
speech = live_streaming.get_string(answer)
confidence = live_streaming.get_confidence(answer)
# speech = input()
print(speech)
before = time.time()
response = "Sorry, I don't understand"
if "quit" in speech.lower() or "stop" in speech.lower():
scheduler.close()
break
if ("cico" in speech.lower() or "kiko" in speech.lower()
or "c1c0" in speech.lower()) and ("hey" in speech.lower()):
# filter out cico since it messes with location detection
question, question_type = nlp_util.is_question(speech)
speech = utils.filter_cico(speech) + " "
print("Question type: " + question_type)
if not question and face_recognition.isFaceRecognition(speech):
response = "executing facial recognition..."
face_recognition.faceRecog(speech)
# task is to transfer over to facial recognition client program
elif (not question or question_type
== "yes/no question") and path_planning.isLocCommand(
speech.lower()):
response = path_planning.process_loc(speech.lower())
# task is to transfer over to path planning on the system
scheduler.communicate("path-planning" + ' ' + str(response))
elif (not question or question_type
== "yes/no question") and object_detection.isObjCommand(
speech.lower()):
pick_up = object_detection.object_parse(speech.lower())
if pick_up:
response = "Object to pick up: " + pick_up
# task is to transfer over to object detection on the system
scheduler.communicate("object-detection" + ' ' + response)
else:
response = "Sorry, I can't recognize this object."
else:
if question:
print("C1C0 is thinking...")
data = keywords.get_topic(speech, parse_location=False)
keywords.modify_topic_data(data, parse_location=True)
if "name" in data.keys() and (data["name"] == "weather"
or data["name"]
== "restaurant"):
if USE_AWS:
response = requests.get(
url + weather_restaurant_route,
params={"speech": speech},
)
if response.ok:
response = response.text
else:
response = "Bad request"
elif data["name"] == "weather":
api_data = weather.lookup_weather_today_city(
data["info"]["location"]["name"])
response = make_response.make_response_api(
data, api_data)
elif data["name"] == "restaurant":
api_data = restaurant.lookup_restaurant_city(
data["info"]["location"]["name"])
response = make_response.make_response_api(
data, api_data)
else:
# Q/A section
if USE_AWS:
response = requests.get(url + chatbot_qa_route,
params={"speech": speech})
if response.ok:
response = response.text
# print(response)
response = ast.literal_eval(response)
answers = response["answers"][0]["answer"]
# for i in range(len(answers)):
# print(f'Answer {i}: {answers[i]}')
# this is response with highest score, we need to keep all answers somewhere
# response = response['answers'][0]['answer']
response = response["answers"][0]["answer"]
else:
response = "Bad request"
else:
response = "Sorry, I can't answer this right now."
if type(response) == list:
i = 0
while i < len(response):
answer = response[i]['answer']
if i == 0:
print(
f'I think the answer is {answer}. Is this correct?'
)
else:
print(
f'Ok, got it. Is the answer then {answer}?'
)
user_response = live_streaming.main()
user_response = live_streaming.get_string(
user_response)
user_response = user_response.lower()
print(user_response)
if 'yes' in user_response or 'yeah' in user_response:
break
else:
response = sentiment.get_sentiment(speech, USE_AWS)
print('Response: ', response)
after = time.time()
print("Time: ", after - before)
from util import utils
USE_AWS = True
weather_restaurant_route = "weather_restaurant"
url = "http://3.13.116.251/"
if __name__ == "__main__":
utils.set_classpath()
with open("restuarant_weather_efficiency_questions.txt") as f:
correct = 0
i = 0
faster = 0
aws_faster = 0
for line in f:
start_time = time.time()
topic_data = keywords.get_topic(line)
if topic_data["name"] == "restaurant":
data = restaurant.lookup_restaurant_city(
topic_data["info"]["location"]["name"]
)
response = make_response.make_response_api(topic_data, data)
if topic_data["name"] == "weather":
data = weather.lookup_weather_today_city(
topic_data["info"]["location"]["name"]
)
response = make_response.make_response_api(topic_data, data)
end_time = time.time()
print("--- %s seconds ---" % (end_time - start_time))
start_time_aws = time.time()
response = requests.get(
url + weather_restaurant_route,