def __main__():
logging_lvl = logging.INFO
args = _get_cl_args()
if args.debug == "DEBUG":
logging_lvl = logging.DEBUG
else:
# set level to display nothing
logging_lvl = logging.INFO
global kb_ID
kb_client = KnowledgeBaseClient(False)
kb_ID = (kb_client.register())['details']
logging.basicConfig(stream=sys.stderr, level=logging_lvl)
tags = {
TAG_DRS : {'desc' : 'DRS structure', 'doc' : DESC_DRS},
TAG_ANSWER : {'desc' : 'stupid answer', 'doc' : DESC_ANSWER}
}
logging.info("\tQA module registered")
#TODO register tags nedeed?
qa_service = QaService(kb_ID,logging_lvl)
t1 = Qa_Thread(qa_service)
t1.start()
k_service = ConstantFromkB(kb_ID,logging_lvl)
t2 = K_Thread(k_service)
t2.start()
def __main__():
kb_client = KnowledgeBaseClient(False)
kb_ID = (kb_client.register())['details']
tags = [{ TAG_ANSWER : {'desc' : 'Fake by ENLP', 'doc' : 'FAKE by ENLP'}},
{ TAG_VISION : {'desc' : 'Fake by ENLP', 'doc' : 'FAKE by ENLP'}},
{ TAG_USER_TRANSCRIPT : {'desc' : 'Fake by ENLP', 'doc' : 'FAKE by ENLP'}}]
kb_client.registerTags(kb_ID, tags[0])
kb_client.registerTags(kb_ID, tags[1])
kb_client.registerTags(kb_ID, tags[2])
obj_from_vision = {
"tag": TAG_VISION,
"is_interlocutor" : "True",
"time_stamp" : str(datetime.datetime.now()),
'emotion': {
'sadness': 0.34,
'calm': 0.48,
'disgust': 0.54,
'anger': 0.67,
'surprise': 0.77,
'fear': 0.54,
'happiness': 0.23
}
}
obj_from_erasmus = {
"tag": TAG_ANSWER,
"text": "Risposta degli erasmus. bla bla bla",
"user_query" : "Dove è la lezione del professor attardi?",
"time_stamp" : 1,
"language" : "it"
}
obj_from_stt = {
"tag": TAG_USER_TRANSCRIPT,
"text": "Dove è la lezione del professor attardi",
"language": "it"
}
print("sending Vision data:")
res = kb_client.addFact(kb_ID, TAG_VISION, 1, 100, obj_from_vision)
print("vision data response:")
print(res)
time.sleep(1)
print("sending STT data:")
res = kb_client.addFact(kb_ID, TAG_USER_TRANSCRIPT, 1, 100, obj_from_stt)
print("STT data response:")
print(res)
time.sleep(1)
print("sending erasmus data:")
res = kb_client.addFact(kb_ID, TAG_ANSWER, 1, 100, obj_from_erasmus)
print("erasmus data response:")
print(res)
def __main__():
kb_client = KnowledgeBaseClient(False)
kb_ID = (kb_client.register())['details']
tags = {TAG_ANSWER: {'desc': 'Fake by ENLP', 'doc': 'FAKE by ENLP'}}
kb_client.registerTags(kb_ID, tags)
obj_from_erasmus = {
"tag": TAG_ANSWER,
"text": "Professor Attardi",
"user_query": "What time Prof. Gervasi teaches Smart Application?",
"time_stamp": 1
}
res = kb_client.addFact(kb_ID, TAG_ANSWER, 1, 100, obj_from_erasmus)
print(res)
def __main__():
kb_client = KnowledgeBaseClient(False)
kb_ID = (kb_client.register())['details']
kb_client.registerTags(kb_ID, { TAG_USER_TRANSCRIPT : {'desc' : 'Fake by ENLP', 'doc' : 'FAKE by ENLP'} })
obj_from_stt = {
"tag": TAG_USER_TRANSCRIPT,
"timestamp" : 7,
"ID": "fake_stt",
"text": "We will build a great wall",
"language": "en",
"valence" :0.5,
"arousal" : 0.5
}
res = kb_client.addFact(kb_ID, TAG_USER_TRANSCRIPT, 1, 100, obj_from_stt)
print(res)
def __main__():
logging_lvl = logging.INFO
args = _get_cl_args()
if args.debug == "DEBUG":
logging_lvl = logging.DEBUG
else:
# set level to display nothing
logging_lvl = logging.INFO
kb_client = KnowledgeBaseClient(True)
global kb_ID
kb_ID = (kb_client.register())['details']
logging.basicConfig(stream=sys.stderr, level=logging_lvl)
tags = { TAG_USER_EMOTION : {'desc' : 'Emotion of what user said', 'doc' : DESC_USER_EMOTION},
TAG_ELF_EMOTION : {'desc' : 'Internal emotion of ELF', 'doc' : DESC_ELF_EMOTION},
TAG_COLORED_ANSWER : {'desc' : 'Reply to the user with emotion content in it', 'doc' : DESC_COLORED_ANSWER}
}
tag_list = [TAG_USER_EMOTION, TAG_ELF_EMOTION, TAG_COLORED_ANSWER]
kb_client.registerTags(kb_ID, tags)
"""
for tag in tag_list:
check_tag = kb_client.getTagDetails([tag])
if not check_tag['success']:
res = kb_client.registerTags(kb_ID, { tag : tags[tag] } )
if not res['success']:
logging.critical(res['details'])
return
"""
logging.info("\tEmotional NLP module registered")
ett_service = EttService(kb_ID, logging_lvl)
t1 = EttThread(ett_service)
t1.start()
tte_service = TteService(kb_ID,logging_lvl)
t2 = TteThread(tte_service)
t2.start()
ies_service = IESService(kb_ID, logging_lvl)
t3 = IESThread(ies_service)
t3.start()
import json
from kb import KnowledgeBaseClient
k = KnowledgeBaseClient(True)
myID = k.register()['details']
registering = k.registerTags(myID, {
"RDF": "an rdf triple",
"TEST": "test data"
})
print(registering)
if (registering['success'] == 0):
print('registration failed')
def callbfun(res):
print("callback:")
print(res)
print(k.subscribe(myID, {"_data": {"prova": "$x"}}, callbfun))
print(k.addFact(myID, "TEST", 1, 50, {"prova": 1}))
print(k.addFact(myID, "TEST", 1, 50, {"prova": 2}))
print(k.addFact(myID, "TEST", 1, 50, {"prova": 3}))
print(k.removeFact(myID, {"_data": {"prova": 2}}))
print(k.queryBind({"_data": {"prova": "$x"}}))
print(k.addFact(myID, "TEST", 1, 50, {"prova": "callb"}))
class GNLP_Service:
def __init__(self):
listTag = {
'NLP_ANSWER': {
'desc': 'general_nlp_answer',
'doc': 'nlp_answer_doc'
},
'NLP_ANALYSIS': {
'desc': 'parse_trees_and_entity_rec',
'doc': 'nlp_analysis_doc'
}
}
self.KBC = KnowledgeBaseClient(True)
self.ID = (self.KBC.register())['details']
nlp_answer_info = {
'desc': 'Query answer from General NLP',
'doc': 'doc about nlp_answer'
}
nlp_analysis_info = {
'desc': 'Query analysis from General NLP',
'doc': 'doc about nlp_analysis'
}
self.KBC.registerTags(self.ID, {
'NLP_ANSWER': nlp_answer_info,
'NLP_ANALYSIS': nlp_analysis_info
})
print("Registered to the KB")
def analyse(self, *res):
'''
Callback that analyse the user query
TODO: Handle the different intents and querys the KB for the
needed informations
'''
print("Analysing...")
print(res)
obj = res[0]['details'][0]['object']['_data']
question = obj['text']
lang = obj['language']
ts = obj['timestamp']
print(question)
#question = question['text']
luis_analysis = NLP_Understand(question, language=lang)
spacy_analysis = get_dependency_tree(question, language=lang)
self.KBC.addFact(
self.ID, "NLP_ANALYSIS", 1, 50, {
"tag": "NLP_ANALYSIS",
"language": lang,
"entities": luis_analysis,
"dependencies": spacy_analysis,
"user_query": question,
"timestamp": ts
})
# Logging some infos
pp = pprint.PrettyPrinter()
pp.pprint(luis_analysis)
pp.pprint(spacy_analysis)
print(question)
self.answer(question, lang, ts)
def answer(self, question, lang, ts):
'''
Callback that answer the user query
'''
print("Answering...")
answer = "Non ho barzellette al momento per ora!"
self.KBC.addFact(
self.ID, "NLP_ANSWER", 1, 50, {
"tag": "NLP_ANSWER",
"language": lang,
"text": answer,
"user_query": question,
"timestamp": ts
})
def start_service(self):
TAG_USER_TRANSCRIPT = "AV_IN_TRANSC_EMOTION"
TAG_CRW_RAW_INFO = "CRAWLER_DATA_ENTRY"
TAG_REASONER_OUTPUT = "REASONING_FRAME"
self.KBC.subscribe(
self.ID, {
"_data": {
"tag": TAG_USER_TRANSCRIPT,
"text": "$d",
"language": "$lang",
"timestamp": "$ts"
}
}, self.analyse)
print("Subscribed to the KB")
from kb import KnowledgeBaseClient
kb = KnowledgeBaseClient(True)
if (sys.argv[1] == "query"): r = kb.query(json.loads(sys.argv[2]))
elif (sys.argv[1] == "addfact"):
r = kb.addFact(sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5],
json.loads(sys.argv[6]))
elif (sys.argv[1] == "addrule"):
r = kb.addRule(sys.argv[2], sys.argv[3], sys.argv[4])
elif (sys.argv[1] == "removefact"):
r = kb.removeFact(sys.argv[2], json.loads(sys.argv[3]))
elif (sys.argv[1] == "removerule"):
r = kb.removeRule(sys.argv[2], json.loads(sys.argv[3]))
elif (sys.argv[1] == "updatefact"):
r = kb.updateFactByID(sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5],
sys.argv[6], json.loads(sys.argv[7]))
elif (sys.argv[1] == "registertags"):
r = kb.registerTags(sys.argv[2], json.loads(sys.argv[3]))
elif (sys.argv[1] == "tagdetails"):
r = kb.getTagDetails(sys.argv[2:])
elif (sys.argv[1] == "getalltags"):
r = kb.getAllTags(
len(sys.argv) >= 3 and sys.argv[2].lower() in ["true", "yes", "y"])
elif (sys.argv[1] == "register"):
r = kb.register()
else:
r = "invalid argument"
print(sys.argv[2:])
print(r)
import sys
from interface_tags import PATH_TO_KB_MODULE
sys.path.insert(0, PATH_TO_KB_MODULE)
from kb import KnowledgeBaseClient
client = KnowledgeBaseClient(False)
kb_id = (client.register())['details']
rules = [
'{"_meta":{"tag":"TEACHING"}, "teach": "$prof", "room": "$room", "course" : "$course" } <- {"_meta":{"tag":"crawler_course"}, "_data":{"data": {"name" : "$course", "teacher_name": "$prof"}}};{"_meta":{"tag":"crawler_room_event"},"_predicates":[["containsString", ["$course2", "$course"]]], "_data": {"data": {"aula" : "$room", "descrizione" : "$course2"}}}'
#'{"_meta":{"tag":"TEACHING"}, "teach": "$prof", "room": "$room", "course" : "$course" } <- {"_meta":{"tag":"crawler_course"}, "_data":{"data": {"name" : "$course", "teacher_name": "$prof"}}};{"_meta":{"tag":"crawler_room_event"}, "_data": {"data": {"aula" : "$room", "descrizione" : "$course"}}}'
#'{"tag":"teaching": "$prof", "room": "$room", "course" : "$course" } <- {"data":{"name" : "$course", "teacher_name": "$prof"}};{"data":{"aula" : "$room", "descrizione" : "$course"}}'
]
"""client.removeRule(kb_id,2)
for rule in rules:
x = client.addRule(kb_id, "ENLP_EMOTIVE_ANSWER", rule)
print(x)
"""
#print(client.query({"_data" : {"name" : "nlpcourse", "teacher_name": "Giuseppe Attardi"}}))
#print(client.query({"_data" : {"aula" : "$X1", "descrizione": "nlpcourse"}}))
res = client.query({"_data": {"teach": "GIUSEPPE ATTARDI", "room": "$x"}})
#res = client.query({"_data":{"teach":"$x"}})
print(res)
# DO NOT THIS ANYMORE
#res = client.query({"_data": "$x"})