def main(_):
args = params_setup()
print("[args]: ", args)
if args.mode == 'train':
train(args)
elif args.mode == 'test':
predict(args)
elif args.mode == 'chat':
chat(args)
import rospy
from roboy_communication_cognition.srv import DetectIntent
rospack = rospkg.RosPack()
sys.path.insert(0, rospack.get_path('roboy_intents_classification'))
from include import skipthoughts
neighbors = 1
stemmer = LancasterStemmer()
words = []
classes = []
documents = []
sentences = []
ignore_words = ['?', ',', 'roboy', 'Roboy', '\n', '.']
args = params_setup()
def read_intents():
import os
intents_path = args.intents_path#os.getcwd() + "/intents/";
training_data = []
for filename in os.listdir(intents_path):
with open(intents_path + filename) as f:
for line in f:
training_data.append({"class": filename, "sentence": line})
return training_data
def sanitize_sentence(sentence):
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
while sentence:
predicted_sentence = get_predicted_sentence(
args, sentence, vocab, rev_vocab, model, sess)
if isinstance(predicted_sentence, list):
for sent in predicted_sentence:
print(" (%s) -> %s" % (sent['prob'], sent['dec_inp']))
else:
print(sentence, ' -> ', predicted_sentence)
sys.stdout.write("> ")
sys.stdout.flush()
sentence = sys.stdin.readline()
def main():
"""
Evaluate seq2seq
"""
evaluate(FLAGS)
if __name__ == '__main__':
FLAGS = params_setup()
main()
async def service_callback():
async with websockets.connect('ws://localhost:9090') as websocket:
# advertise the service
await websocket.send("{ \"op\": \"advertise_service\",\
\"type\": \"roboy_communication_cognition/GenerateAnswer\",\
\"service\": \"/roboy/cognition/generative_nlp/answer\"\
}")
i = 1 # counter for the service request IDs
with tf.Session() as sess:
# Create model and load parameters.
logging.info("Loading the model")
args = params_setup()
args.batch_size = 1 # We decode one sentence at a time.
model = create_model(sess, args)
# Load vocabularies.
vocab_path = os.path.join(args.data_dir,
"vocab%d.in" % args.vocab_size)
vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
logging.info(
"Service /roboy/cognition/generative_nlp/answer is ready")
# wait for the service request, generate the answer, and send it back
while True:
try:
request = await websocket.recv()
sentence = json.loads(request)["args"]["text_input"]
model_response = get_predicted_sentence(
args, sentence, vocab, rev_vocab, model, sess)
srv_response = {}
answer = {}
if isinstance(model_response, list):
text = model_response[0]['dec_inp']
else:
text = model_response['dec_inp']
answer["text_output"] = ''.join([
i if ord(i) < 128 else '' for i in text
]) # strip down unicode
srv_response["values"] = answer
srv_response["op"] = "service_response"
srv_response[
"id"] = "service_request:/roboy/cognition/generative_nlp/answer:" + str(
i)
srv_response["result"] = True
srv_response[
"service"] = "/roboy/cognition/generative_nlp/answer"
i += 1
await websocket.send(json.dumps(srv_response))
except Exception as e:
logging.exception(
"Oopsie! Got an exception in generative_nlp")