def nlp_generate_actionname():
if request.method == 'POST':
global server_ip_req
global flag_server1_train
global flag_server2_train
global flag_timer
global R1
global R2
if server_ip_req == server_ip_arr[1]:
rep = server_process.server_jump(request, request.method,
server_ip_req, port[0])
rep = jsonify(rep)
return rep
elif server_ip_req == server_ip_arr[0]:
req_d = request.json
bId = req_d["botId"]
action_flag = req_d["action_flag"]
if not bId == botId:
return rep_body.rep_common(201, {})
if action_flag == "n":
return rep_body.rep_common(200, {})
elif action_flag == "y":
# domain文件的文件路径
basePath = os.path.abspath(
os.path.dirname(os.path.dirname(__file__)))
domainPath = basePath + slash + "training" + slash + "domain.yml"
# 生成自定义动作名
dic_actionname = action_name_generate.action_name_generate(
domainPath)
return rep_body.rep_common(200, dic_actionname)
return rep_body.rep_common(201, {})
def task_core_server_start():
if request.method == 'POST' and 'timestr' in request.json:
timestr = request.json['timestr']
# .sh脚本文件绝对路径
path = "/rasa/zndhjqr_nlp/rasa_shell/startTaskCoreServer.sh"
os.popen(path + " " + timestr)
return rep_body.rep_common(200, {})
return rep_body.rep_common(201, {})
def axiosT():
if request.method == 'GET':
# 返回体
dictData = {
"msg": "并发1成功"
}
return rep_body.rep_common(200, dictData)
return rep_body.rep_common(201, {})
def sd():
if request.method == 'POST':
if request.json["sd"] == 1:
# 返回体
dictData = {
"msg": "post请求成功"
}
return rep_body.rep_common(200, dictData)
return rep_body.rep_common(201, {})
def po():
if request.method == 'POST':
if request.json["a"] == 1:
# 返回体
dictData = {
"msg": "并发2成功"
}
return rep_body.rep_common(200, dictData)
return rep_body.rep_common(201, {})
def rasa_server_stop():
if request.method == 'GET':
# .sh脚本文件绝对路径
path = "/rasa/zndhjqr_nlp/rasa_shell/stopRasaServer.sh"
try:
os.popen(path)
except Exception as e:
print(e)
return rep_body.rep_common(201, {})
return rep_body.rep_common(200, {})
return rep_body.rep_common(201, {})
def iT():
if request.method == 'GET':
# 返回体
dictData = {
"token": "abc"
}
return jsonify(dictData)
return rep_body.rep_common(201, {})
def nlp_text_anyq():
if request.method == "POST":
try:
if request.form:
params = request.form.to_dict()
visitor_id = params["visitorId"]
bot_id = int(params["botId"])
user_type = params["userType"]
else:
params = request.json
visitor_id = params["visitorId"]
bot_id = int(params["botId"])
user_type = params["userType"]
if type(visitor_id) == str:
visitor_id = int(visitor_id.replace("voice", ""))
input_text = params["inputText"]
api_key = params["apiKey"]
if api_key == apiKey and bot_id == 1:
return "中间件之间测试成功了！"
# # AnyQ一问一答返回结果
# anyq_url = anyq_request_url + "?question=" + input_text
# # print("anyq_url:" + anyq_url)
# anyq_response = requests.get(anyq_url)
# if anyq_response.content:
# anyq_service_response = anyq_response.json()
# # print("anyq_service_response:" % anyq_service_response)
#
# # 一问一答型机器人置信度
# if len(anyq_service_response) != 0:
# bot_confidence1 = anyq_service_response[0]["confidence"]
# else:
# bot_confidence1 = 0
#
# # 一问一答型机器人回复
# # round 返回浮点数的后几位 ps: round(3.1415,2) ---> 3.14
# anyq_answer = anyq_service_response[0]["answer"]
# # print("ananyq_answer:" % anyq_answer)
# bot_type = 1
# askfor_data = {
# "botId": bot_id,
# "botType": bot_type,
# "replyText": [anyq_answer],
# "confidence": bot_confidence1,
# "notHeard": "N",
# "unexpectCount": unexpect_count
# }
# return rep_body.rep_common(200, askfor_data)
except Exception as e:
return rep_body.rep_common(201, {"err_msg": str(e)})
def file_process(fileList):
# 获取当前文件所在的目录的上级目录
basePath = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
counter = 0
try:
while counter <= 2:
f = fileList["file" + "[" + str(counter) + "]"]
# 限定post方法，文件列表不为空且为一问一答类型
if allowed_file(f.filename):
# 拼凑文件存储路径
filePath = basePath + slash + "training" + slash + f.filename
# 打印路径
print(filePath)
# 将文件保存到相应路径
f.save(filePath)
counter += 1
except:
return rep_body.rep_common(201, {})
# 返回项目根目录
return basePath + slash
def nlp_task_ed():
if request.method == 'POST' and request.files and request.form["d"]:
global server_ip_req
global flag_server1_train
global flag_server2_train
global flag_timer
global R1
global R2
if flag_timer:
return rep_body.rep_common(200, {"status": "正在训练中"})
else:
if server_ip_req == server_ip_arr[1]:
flag_server2_train = "y"
rep = server_process.server_jump(request, request.method,
server_ip_req, port[0])
flag_server2_train = "n"
rep = jsonify(rep)
flag_timer = True
time.sleep(3600)
R1.clear()
flag_timer = False
return rep
elif server_ip_req == server_ip_arr[0]:
flag_server1_train = "y"
fqList = request.files
d = json.loads(request.form["d"])
bot_id = d["botId"]
# 判断botId值
if not bot_id == botId:
return rep_body.rep_common(201, {})
# 处理培训数据
path_prefix = file_process(fqList)
# 生成时间戳
time_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
# nlu和core的目录路径
nlu_path = path_prefix + "now_models" + slash + "nlu"
core_path = path_prefix + "now_models" + slash + "core"
# 使用训练服务器进行训练
# 1.关闭rasa服务
req_host = server_ip_req + ":" + port[1]
req_url1 = req_host + "/rasa_server_stop"
rasa_rep = requests.get(req_url1).json()
if rasa_rep["code"] == 200:
# 删除运行模型目录下的nlu模型和core模型
if os.path.exists(nlu_path) and os.path.exists(core_path):
shutil.rmtree(nlu_path)
shutil.rmtree(core_path)
# 新建运行模型目录
os.mkdir(nlu_path)
os.mkdir(nlu_path + slash + "qa")
os.mkdir(nlu_path + slash + "task")
os.mkdir(core_path)
# 训练nlu和core模型
task_model_path = bot_train.train_task_nlu(
path_prefix, slash, time_str)
core_model_path = bot_train.train_core(path_prefix, slash,
time_str)
# 经过替换
now_task_path = task_model_path.replace('old', 'now')
now_core_path = core_model_path.replace('old', 'now')
# 复制训练模型(包括nlu、core)到运行模型目录
shutil.copytree(task_model_path, now_task_path)
shutil.copytree(core_model_path, now_core_path)
# 如果有自定义行为
if any(d["action"]):
# 取得自定义action的dict
dic_action = d["action"]
# 以下字段均为自定义action信息
method = dic_action["method"] # 请求方式
action_name = dic_action["action_name"] # 动作名字
action_url = dic_action["url"] # 请求url
action_data = dic_action["data"] # 请求数据
action_rule = dic_action["rule"] # 返回模板
# 后端给定参数s
headers = {'Content-Type': 'application/json'} # 头部
mid_url = "http://" + "" # 中间件url
path = "../action.py" # 写入的action.py文件的路径
generate_action_script.new_action(method, action_name,
action_url, action_data,
action_rule, headers,
mid_url, path)
# 3.开启rasa相关服务
# nlu服务
req_url2 = req_host + "/nlu_server_start"
rasa_nlu_rep = requests.get(req_url2).json()
# qa服务
rasa_qa_data = {"timestr": time_str}
h = {"Content-Type": "application/json"}
req_url3 = req_host + "/qa_core_server_start"
rasa_qa_rep = requests.post(req_url3,
data=json.dumps(rasa_qa_data),
headers=h).json()
# task服务
rasa_qa_data = {"timestr": time_str}
h = {"Content-Type": "application/json"}
req_url4 = req_host + "/task_core_server_start"
rasa_task_rep = requests.post(req_url4,
data=json.dumps(rasa_qa_data),
headers=h).json()
# 动作服务
req_url5 = req_host + "/rasa_server_sdk"
rasa_action_rep = requests.get(req_url5).json()
if rasa_nlu_rep["code"] == 200 and rasa_qa_rep["code"] == 200 and rasa_task_rep["code"] == 200 and \
rasa_action_rep["code"] == 200:
# 新模型加载到内存
nlu_service_params1 = {
"q": "",
"project": "qa",
"model": "qa_" + time_str
}
requests.post(nlu_service_url1,
data=json.dumps(nlu_service_params1)).json()
nlu_service_params2 = {
"q": "",
"project": "task",
"model": "task_" + time_str
}
requests.post(nlu_service_url1,
data=json.dumps(nlu_service_params2)).json()
flag_server1_train = "n"
flag_timer = True
time.sleep(3600)
R2.clear()
flag_timer = False
return rep_body.rep_common(200, {})
return rep_body.rep_common(201, {})
def nlp_qa_ed():
if request.method == 'POST' and request.files and request.form["d"]:
global server_ip_req
global flag_server1_train
global flag_server2_train
global flag_timer
global R1
global R2
if flag_timer:
return rep_body.rep_common(200, {"status": "正在训练中"})
else:
if server_ip_req == server_ip_arr[1]:
flag_server2_train = "y"
rep = server_process.server_jump(request, request.method,
server_ip_req, port[0])
flag_server2_train = "n"
rep = jsonify(rep)
flag_timer = True
time.sleep(3600)
R1.clear()
flag_timer = False
return rep
elif server_ip_req == server_ip_arr[0]:
# 训练标志设置为y
flag_server1_train = "y"
fqList = request.files
d = json.loads(request.form["d"])
id = d["botId"]
# 判断botId值
if not id == botId:
return rep_body.rep_common(201, {})
# 处理培训数据
path_prefix = file_process(fqList)
# 生成时间戳
time_str = time.strftime("%Y%m%d%H%M%S", time.localtime())
# nlu和core的目录路径
nlu_path = path_prefix + "now_models" + slash + "nlu"
core_path = path_prefix + "now_models" + slash + "core"
# 使用训练服务器进行训练
# 1.关闭rasa服务
req_host = server_ip_req + ":" + port[1]
req_url1 = req_host + "/rasa_server_stop"
rasa_rep = requests.get(req_url1).json()
if rasa_rep["code"] == 200:
# 删除运行模型目录下的nlu模型和core模型
if os.path.exists(nlu_path) and os.path.exists(core_path):
shutil.rmtree(nlu_path)
shutil.rmtree(core_path)
# 新建运行模型目录
os.mkdir(nlu_path)
os.mkdir(nlu_path + slash + "qa")
os.mkdir(nlu_path + slash + "task")
os.mkdir(core_path)
# 2.训练nlu和core模型
qa_model_path = bot_train.train_qa_nlu(
path_prefix, slash, time_str)
core_model_path = bot_train.train_core(
path_prefix, slash, time_str)
# 经过替换
now_qa_path = qa_model_path.replace('old', 'now')
now_core_path = core_model_path.replace('old', 'now')
# 复制训练模型(包括nlu、core)到运行模型目录
shutil.copytree(qa_model_path, now_qa_path)
shutil.copytree(core_model_path, now_core_path)
# 3.开启rasa相关服务
# nlu服务
req_url2 = req_host + "/nlu_server_start"
rasa_nlu_rep = requests.get(req_url2).json()
# qa服务
rasa_qa_data = {"timestr": time_str}
h = {"Content-Type": "application/json"}
req_url3 = req_host + "/qa_core_server_start"
rasa_qa_rep = requests.post(req_url3,
data=json.dumps(rasa_qa_data),
headers=h).json()
# task服务
rasa_qa_data = {"timestr": time_str}
h = {"Content-Type": "application/json"}
req_url4 = req_host + "/task_core_server_start"
rasa_task_rep = requests.post(
req_url4, data=json.dumps(rasa_qa_data),
headers=h).json()
# 动作服务
req_url5 = req_host + "/rasa_server_sdk"
rasa_action_rep = requests.get(req_url5).json()
if rasa_nlu_rep["code"] == 200 and rasa_qa_rep["code"] == 200 and rasa_task_rep["code"] == 200 and \
rasa_action_rep["code"] == 200:
# 新模型加载到内存
nlu_service_params1 = {
"q": "",
"project": "qa",
"model": "qa_" + time_str
}
requests.post(
nlu_service_url1,
data=json.dumps(nlu_service_params1)).json()
nlu_service_params2 = {
"q": "",
"project": "task",
"model": "task_" + time_str
}
requests.post(
nlu_service_url1,
data=json.dumps(nlu_service_params2)).json()
flag_server1_train = "n"
flag_timer = True
time.sleep(3600)
R2.clear()
flag_timer = False
return rep_body.rep_common(200, {})
return rep_body.rep_common(201, {})
def nlp_text_task():
if request.method == 'POST':
try:
# 变量声明
local_useType = "0"
botType = 0
core_service_url = ''
global unexpect_count
if request.form:
print("form对象")
params = request.form.to_dict()
print(params)
visitorId = params['visitorId']
botId = int(params['botId'])
useType = params['useType']
local_useType = useType
else:
print("json对象")
params = request.json
visitorId = params['visitorId']
botId = params['botId']
useType = params['useType']
local_useType = useType
print(params)
inputText = params['inputText']
print(inputText)
api_key = params['apiKey']
# 验证参数
if api_key == apiKey and botId == 1:
# 获取最新的qa和task模型名称
basePath = os.path.abspath(
os.path.dirname(os.path.dirname(__file__)))
qa_path = basePath + slash + "now_models" + slash + "nlu" + slash + "qa"
qa_model_num = 0
for index, filename in enumerate(os.listdir(qa_path)):
if index != 0:
temp_filename_num = int(filename.replace("qa_", ""))
if temp_filename_num >= qa_model_num:
qa_model_num = temp_filename_num
else:
qa_model_num = int(filename.replace("qa_", ""))
task_path = basePath + slash + "now_models" + slash + "nlu" + slash + "task"
task_model_num = 0
for index, filename in enumerate(os.listdir(task_path)):
if index != 0:
temp_filename_num = int(filename.replace("task_", ""))
if temp_filename_num >= task_model_num:
task_model_num = temp_filename_num
else:
task_model_num = int(filename.replace("task_", ""))
# 注释：加入qa后取消注释
qa_model_name = "qa_" + str(qa_model_num)
nlu_service_params1 = {
"q": inputText,
"project": "qa",
"model": qa_model_name
}
# nlu_service_response1 = requests.post(nlu_service_url1, data=json.dumps(nlu_service_params1)).json()
nlu_service_response1 = requests.post(
test_server_ip,
data=json.dumps(nlu_service_params1)).json()
task_model_name = "task_" + str(task_model_num)
nlu_service_params2 = {
"q": inputText,
"project": "task",
"model": task_model_name
}
# nlu_service_response2 = requests.post(nlu_service_url1, data=json.dumps(nlu_service_params2)).json()
nlu_service_response2 = requests.post(
test_server_ip,
data=json.dumps(nlu_service_params2)).json()
print('nlu_service_response1: %s' % nlu_service_response1)
print('nlu_service_response2: %s' % nlu_service_response2)
# 一问一答型和任务型机器人的置信度
bot_confidence1 = nlu_service_response1["intent"]["confidence"]
bot_confidence2 = nlu_service_response2["intent"]["confidence"]
print("qa_intent %s" % nlu_service_response1["intent"])
print('task_intent %s' % nlu_service_response2["intent"])
if (bot_confidence1 > bot_confidence2 and
len(nlu_service_response1["entities"]) == 0 and
nlu_service_response2["intent"]['name'] == "task_askfor_again" and
local_useType == "2") or \
(bot_confidence1 < bot_confidence2 and
nlu_service_response2["intent"]['name'] == "task_askfor_again" and
local_useType == "2"):
askfor_data = {
"botId": botId,
"botType": 2,
"replyText": [],
"confidence": 0,
"notHeard": "T",
"unexpectCount": unexpect_count
}
return rep_body.rep_common(200, askfor_data)
# 取一问一答的置信度
# qa机器人置信度>task机器人置信度，且qa机器人的实体数量不为0
elif bot_confidence1 > bot_confidence2 and \
len(nlu_service_response1["entities"]) != 0:
# core_service_url = qa_service_url
core_service_url = test_qa_core_ip
bot_confidence = bot_confidence1
botType = 1
# 取任务型机器人的置信度
# 1.qa机器人置信度>task机器人置信度，且qa机器人的实体数量为0，意图不为task_askfor_again
# 2.qa机器人置信度<=task机器人置信度，且意图不为task_askfor_again
elif (bot_confidence1 > bot_confidence2 and
len(nlu_service_response1["entities"]) == 0 and
nlu_service_response2["intent"]['name'] != "task_askfor_again") or \
(bot_confidence1 <= bot_confidence2 and
nlu_service_response2["intent"]['name'] != "task_askfor_again"):
# core_service_url = task_service_url
core_service_url = test_task_core_ip
bot_confidence = bot_confidence2
botType = 2
# 如果测试窗出现识别出用户未听清的情况,按未识别处理
elif (bot_confidence1 > bot_confidence2 and
len(nlu_service_response1["entities"]) == 0 and
nlu_service_response2["intent"]['name'] == "task_askfor_again" and
local_useType == "1") or \
(bot_confidence1 < bot_confidence2 and
nlu_service_response2["intent"]['name'] == "task_askfor_again" and
local_useType == "1"):
unexpect_count += 1
test_list = []
test_list.append(
random_reply_generate.unrecognized_reply())
askfor_data = {
"botId": botId,
"botType": botType,
"replyText": test_list,
"confidence": random_pro_generate.random_generate(),
"notHeard": "N",
"unexpectCount": unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, askfor_data)
# 2.把语句发送给合适的nlu对应的core服务
core_service_params = {
"sender": visitorId,
"message": inputText
}
core_service_headers = {'content-type': 'application/json'}
core_service_response = requests.post(
core_service_url,
data=json.dumps(core_service_params),
headers=core_service_headers).json()
print(core_service_response)
# 3.触发后备动作的记录
# 遍历core_service_response列表，拼接字符串
linklist = []
if len(core_service_response) == 0:
unexpect_count += 1
# 返回体
dictData = {
"botId": botId,
"botType": botType,
"replyText":
random_reply_generate.unrecognized_reply(),
"confidence": random_pro_generate.random_generate(),
"notHeard": "N",
"unexpectCount": unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, dictData)
for j in core_service_response:
linklist.append(j["text"])
replyText = linklist
print(replyText)
print(local_useType)
# 测试窗
if local_useType == "1":
if replyText[0] == '我还比较小' or replyText[0] == '':
unexpect_count += 1
bot_confidence = random_pro_generate.random_generate()
# 返回体
dictData = {
"botId": botId,
"botType": botType,
"replyText": [],
"confidence": bot_confidence,
"notHeard": "N",
"unexpectCount": unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, dictData)
else:
# 返回体
dictData = {
"botId": botId,
"botType": botType,
"replyText": replyText,
"confidence": bot_confidence,
"notHeard": "N",
"unexpectCount": unexpect_count
}
return rep_body.rep_common(200, dictData)
# ccone
elif local_useType == "2":
if replyText[0] == '我还比较小' or replyText[0] == '':
unexpect_count += 1
replyText = []
replyText.append(
random_reply_generate.unrecognized_reply())
replyText.append(
random_reply_generate.morehelp_reply())
bot_confidence = random_pro_generate.random_generate()
# 返回体
dictData = {
"botId": botId,
"botType": botType,
"replyText": replyText,
"confidence": bot_confidence,
"notHeard": "F",
"unexpectCount": unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, dictData)
else:
# 返回体
dictData = {
"botId": botId,
"botType": botType,
"replyText": replyText,
"confidence": bot_confidence,
"notHeard": "F",
"unexpectCount": unexpect_count
}
return rep_body.rep_common(200, dictData)
except Exception as e:
print(e)
return rep_body.rep_common(201, {})
def nlp_text_re():
if request.method == 'POST':
try:
# 变量声明
bot_type = 0
core_service_url = ''
global unexpect_count
if request.form:
# print("form对象")
params = request.form.to_dict()
# print("form对象的params：" % params)
visitor_id = params['visitorId']
bot_id = int(params['botId'])
use_type = params['useType']
else:
# print("json对象")
params = request.json
# print("json对象的params：" % params)
visitor_id = params['visitorId']
bot_id = params['botId']
use_type = params['useType']
if type(visitor_id) == str:
visitor_id = int(visitor_id.replace("voice", ""))
# print("解析出的对象:" % params)
input_text = params['inputText']
# print("input_text" + input_text)
api_key = params['apiKey']
# 验证参数
if api_key == apiKey and bot_id == 1:
# 获取最新的qa和task模型名称
base_path = os.path.abspath(
os.path.dirname(os.path.dirname(__file__)))
task_path = base_path + slash + "now_models" + slash + "nlu" + slash + "task"
task_model_num = 0
for index, filename in enumerate(os.listdir(task_path)):
if index != 0:
temp_filename_num = int(filename.replace("task_", ""))
if temp_filename_num >= task_model_num:
task_model_num = temp_filename_num
else:
task_model_num = int(filename.replace("task_", ""))
# AnyQ一问一答返回结果
anyq_url = anyq_request_url + "?question=" + input_text
# print("anyq_url:" + anyq_url)
anyq_response = requests.get(anyq_url)
if anyq_response.content:
anyq_service_response = anyq_response.json()
# global anyq_service_response # zhang 声明 anyq_service_response为全局变量 2019/10/23 10：00 添加
# print("anyq_service_response:" % anyq_service_response)
task_model_name = "task_" + str(task_model_num)
nlu_service_params2 = {
"q": input_text,
"project": "task",
"model": task_model_name
}
# print("nlu_request_url:" + nlu_request_url)
# print("nlu_service_params2:" % nlu_service_params2)
nlu_service_response2 = requests.post(
nlu_request_url,
data=json.dumps(nlu_service_params2)).json()
# print('nlu_service_response2: %s' % nlu_service_response2)
# 一问一答型机器人置信度
if anyq_response.content:
if len(anyq_service_response) != 0:
bot_confidence1 = anyq_service_response[0][
"confidence"]
else:
bot_confidence1 = 0
else:
bot_confidence1 = 0
# 任务型机器人置信度
if any(nlu_service_response2):
bot_confidence2 = nlu_service_response2["intent"][
"confidence"]
else:
return rep_body.rep_common(201, {})
# # print("qa_intent %s" % nlu_service_response1["intent"])
# print('task_intent %s' % nlu_service_response2["intent"])
# 一问一答型机器人回复
# round 返回浮点数的后几位 ps: round(3.1415,2) ---> 3.14
if (bot_confidence1 > bot_confidence2) or (round(
bot_confidence1, 1) == round(
bot_confidence2, 1)) and anyq_response.content:
anyq_answer = anyq_service_response[0]["answer"]
# print("ananyq_answer:" % anyq_answer)
bot_type = 1
askfor_data = {
"botId": bot_id,
"botType": bot_type,
"replyText": [anyq_answer],
"confidence": bot_confidence1,
"notHeard": "N",
"unexpectCount": unexpect_count
}
return rep_body.rep_common(200, askfor_data)
# 任务型机器人回复
else:
bot_type = 2
# 测试窗
if use_type == "1":
not_heard = "N"
# 1.排除测试窗的外呼意图、转人工意图和用户听不清楚的影响
if (nlu_service_response2['intent']['name']
== 'task_kuandai_huifang'
or nlu_service_response2['intent']['name']
== 'task_to_people'
or nlu_service_response2['intent']['name']
== 'task_askfor_again'):
unexpect_count += 1
reply_text = []
reply_text.append(
random_reply_generate.unrecognized_reply())
reply_text.append(
random_reply_generate.morehelp_reply())
bot_confidence = random_pro_generate.random_generate(
)
# 返回体
dict_data = {
"botId": bot_id,
"botType": use_type,
"replyText": reply_text,
"confidence": bot_confidence,
"notHeard": not_heard,
"unexpectCount": unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, dict_data)
# 2.测试窗正常处理
else:
core_service_url = task_request_url
bot_confidence = bot_confidence2
# 请求task的core服务
core_service_params = {
"sender": visitor_id,
"message": input_text
}
# print("测试窗的core_service_params：" % core_service_params)
core_service_headers = {
'content-type': 'application/json'
}
core_service_response = requests.post(
core_service_url,
data=json.dumps(core_service_params),
headers=core_service_headers).json()
# print("core_service_response" % core_service_response)
# 机器人响应的处理
linklist = []
if len(core_service_response) == 0:
unexpect_count += 1
# 返回体
dict_data = {
"botId":
bot_id,
"botType":
bot_type,
"replyText":
random_reply_generate.unrecognized_reply(),
"confidence":
random_pro_generate.random_generate(),
"notHeard":
not_heard,
"unexpectCount":
unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, dict_data)
for j in core_service_response:
linklist.append(j["text"])
reply_text = linklist
# print("测试窗的reply_text:" % reply_text)
if reply_text[0] == '我还比较小' or reply_text[0] == '':
unexpect_count += 1
reply_text = []
reply_text.append(
random_reply_generate.unrecognized_reply())
reply_text.append(
random_reply_generate.morehelp_reply())
bot_confidence = random_pro_generate.random_generate(
)
# 返回体
dict_data = {
"botId": bot_id,
"botType": bot_type,
"replyText": reply_text,
"confidence": bot_confidence,
"notHeard": not_heard,
"unexpectCount": unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, dict_data)
else:
# 返回体
dict_data = {
"botId": bot_id,
"botType": bot_type,
"replyText": reply_text,
"confidence": bot_confidence,
"notHeard": not_heard,
"unexpectCount": unexpect_count
}
return rep_body.rep_common(200, dict_data)
# 语音
elif use_type == "2":
not_heard = "F"
# 1.处理转人工意图
if nlu_service_response2['intent'][
'name'] == 'task_to_people':
reply_text = []
not_heard = "P"
reply_text.append(
random_reply_generate.morehelp_reply())
bot_confidence = nlu_service_response2['intent'][
'confidence']
# 返回体
dict_data = {
"botId": bot_id,
"botType": use_type,
"replyText": reply_text,
"confidence": bot_confidence,
"notHeard": not_heard,
"unexpectCount": unexpect_count
}
return rep_body.rep_common(200, dict_data)
# 2.语音出现用户未听清的情况
elif nlu_service_response2["intent"][
'name'] == "task_askfor_again":
not_heard = "T"
# 语音出现用户未听清的情况
askfor_data = {
"botId": bot_id,
"botType": bot_type,
"replyText": [],
"confidence": 0,
"notHeard": not_heard,
"unexpectCount": unexpect_count
}
return rep_body.rep_common(200, askfor_data)
# 3.用户听清机器人的回复
elif nlu_service_response2["intent"][
'name'] != "task_askfor_again":
# 处理用户的再见意图
if nlu_service_response2['intent'][
'name'] == 'goodbye':
not_heard = "G"
core_service_url = task_request_url
bot_confidence = bot_confidence2
# 请求task的core服务
core_service_params = {
"sender": visitor_id,
"message": input_text
}
# print("语音的core_service_params:" % core_service_params)
core_service_headers = {
'content-type': 'application/json'
}
core_service_response = requests.post(
core_service_url,
data=json.dumps(core_service_params),
headers=core_service_headers).json()
# print("语音的core_service_response:" % core_service_response)
# 机器人响应的处理
linklist = []
if len(core_service_response) == 0:
unexpect_count += 1
# 返回体
dict_data = {
"botId":
bot_id,
"botType":
bot_type,
"replyText":
random_reply_generate.unrecognized_reply(),
"confidence":
random_pro_generate.random_generate(),
"notHeard":
not_heard,
"unexpectCount":
unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, dict_data)
for j in core_service_response:
linklist.append(j["text"])
reply_text = linklist
# print("语音的reply_text：" % reply_text)
if reply_text[0] == '我还比较小' or reply_text[0] == '':
unexpect_count += 1
reply_text = []
reply_text.append(
random_reply_generate.unrecognized_reply())
reply_text.append(
random_reply_generate.morehelp_reply())
bot_confidence = random_pro_generate.random_generate(
)
# 返回体
dict_data = {
"botId": bot_id,
"botType": bot_type,
"replyText": reply_text,
"confidence": bot_confidence,
"notHeard": not_heard,
"unexpectCount": unexpect_count
}
if unexpect_count == 2:
unexpect_count = 0
return rep_body.rep_common(200, dict_data)
else:
# 返回体
dict_data = {
"botId": bot_id,
"botType": bot_type,
"replyText": reply_text,
"confidence": bot_confidence,
"notHeard": not_heard,
"unexpectCount": unexpect_count
}
return rep_body.rep_common(200, dict_data)
except Exception as e:
# print(type(e))
# print(e)
# print('错误')
return rep_body.rep_common(201, {"err_msg": str(e)})
