def __init__(self):
multiwheelUnit._init()
multiwheelUnit.set_value('userid', 1)
self._aiml_kernal = aiml_cn.Kernel()
self._aiml_kernal.learn('../../resource/template.aiml')
self._aiml_kernal.learn('../../resource/contain_template.aiml')
self._aiml_kernal.learn('../../resource/multiwheelQA.aiml')
self._aiml_kernal.learn('../../resource/time_template.aiml')
def answer_business_readerCard_00(cls, entity_dict, graph):
respons_str=""
if (len(rdfPrepare.rdf_query_propertiy("办理读书卡", "pro_step1", graph)) != 0):
respons_str += rdfPrepare.rdf_query_propertiy("办理读书卡", "pro_step1", graph)[0]
if multiwheelUnit.get_value('business') == None:
multiwheelUnit.set_value('business', "办理读书卡")
if multiwheelUnit.get_value('step') == None:
multiwheelUnit.set_value('step', "_step1")
return respons_str
else:
return None
def answer_business_readerCard_no(cls, entity_dict, graph):
if multiwheelUnit.get_value('business')=="办理读书卡":
if multiwheelUnit.get_value('step')!=None:
arr = multiwheelUnit.get_value('step').split('_')
step=arr[len(arr) - 1]
if (len(rdfPrepare.rdf_query_propertiy("办理读书卡", "pro_"+step+"_no", graph)) != 0):
step_ans =rdfPrepare.rdf_query_propertiy("办理读书卡", "pro_"+step+"_no", graph)[0]
respons_str=rdfPrepare.rdf_query_propertiy("办理读书卡", "pro_"+step_ans, graph)[0]
multiwheelUnit.set_value('step',multiwheelUnit.get_value('step')+"_"+step_ans)
return respons_str
else:
return None
def question_answer_hub(self, question_str):
"""
问答总控，基于aiml构建问题匹配器
:param question_str:问句输入
:return:
"""
g = rdfPrepare.load_graph()
question_replaced, entity_dict = entityMatch2.match_and_replace_all(
question_str, g)
#print(question_replaced,entity_dict)
navi_g = rdfPrepare.load_navi_graph()
navi_question_replaced, navi_entity_dict = entityMatch2.match_and_replace_all(
question_str, navi_g)
#print(navi_question_replaced, navi_entity_dict)
# question_replaced, entity_dict = entityMatch.match_and_replace_all(question_str)
'''
arr = []
if len(entity_dict['room']) > 0:
for i in entity_dict['room']:
if len(i) == 0:
continue
index = question_str.find(i[0])
arr.append(index)
# print(arr)
arr_index = np.argsort(np.array(arr))
# print(arr_index)
entity_dict2 = []
for i in entity_dict['room']:
if len(i) == 0:
continue
entity_dict2.append(i)
for i in range(len(entity_dict['room'])):
if len(entity_dict['room'][i]) == 0:
continue
# print(arr_index[i],entity_dict2[arr_index[i]])
entity_dict['room'][i] = entity_dict2[arr_index[i]]
'''
if multiwheelUnit.get_value('business') == "办理读书卡":
if "answer" not in multiwheelUnit.get_value('step'):
question_replaced += "读卡"
else:
multiwheelUnit.set_value('business', None)
multiwheelUnit.set_value('step', None)
aiml_respons = self._aiml_kernal.respond(question_replaced)
if 'multiwheeltask_' in aiml_respons:
print("aiml_respons: ", str(aiml_respons))
# print("entity_dict: ", str(entity_dict))
graph_respons = rdfBotMul.task_response(aiml_respons, entity_dict,
question_str, g)
return graph_respons
elif 'task_' in aiml_respons:
print("aiml_respons: ", str(aiml_respons))
#print("entity_dict: ", str(entity_dict))
if aiml_respons == 'task_room_pos':
graph_respons = rdfBot.task_response(aiml_respons,
navi_entity_dict,
question_str, navi_g)
else:
graph_respons = rdfBot.task_response(aiml_respons, entity_dict,
question_str, g)
return graph_respons
else:
return aiml_respons