def answer():
# 接收前端发来的语音消息，并指定路径保存
reco_file = request.files.get('reco')
# 调用uuid三方模块生成唯一文件名
from uuid import uuid4
filename = f'{uuid4()}.wav'
filepath = os.path.join(os.path.dirname(__file__), 'audio', 'questions', filename)
reco_file.save(filepath)
# print('语音问题保存路径：',filepath)
# 调用语音识别模块，对语音信息进行格式转换保存在指定目录，然后进行识别，得到文字信息
from ASR import asr
text_question = asr(filepath)
# print('语音问题文本',text_question)
# 调用自然语言处理模块，对文字信息进行处理，得到回复文字信息
from NLP import nlp
text_answer = nlp(text_question)
# print('语音回答文本',text_answer)
# 调用语音合成模块，对回复的文字信息进行合成并保存在指定目录下
from TTS import tts
answer_filepath = tts(text_answer)
print('语音回答文件路径：', answer_filepath)
# 获取语音应答消息文件名并返回
answer_filename = os.path.basename(answer_filepath)
return {'filename': answer_filename}
def cont_search(self, query):
l = []
n = nlp()
chunks = n.process_content(query)
print chunks
for i in xrange(1, 4):
d = {}
for x in chunks:
d[x] = []
w = 'P' + str(i)
# print w
db = Client[w]
# ld = db["linkData"]
# ll = db["linkList"]
linkData = db["linkData"]
# for j in linkData.find():
# print j["name"]
for res in linkData.find():
v = 0
texts = res["Contents"]
for i in d:
for y in texts:
if i in y:
v = v + 1
# if res["URL"] not in d[i]:
# d[i].append(res["URL"])
if v != 0:
d[i].append({res["URL"]: v})
#d[i][1].append(v)
# print d
for u in d.values()[0]:
# print u.keys()
# print ("--------------")
c = 0
for x in range(1, len(d)):
for t in d.values()[x]:
if u.keys() == (t.keys()):
c = c + 1
if c == (len(d) - 1):
l.append(u)
return l
# vp=search()
# q="sharedspace"
# print(vp.cont_search(q))
def cont_search(self, query):
l = []
n = nlp()
chunks = n.process_content(query)
print chunks
db = Client["IDF"]
coll = db["Fri"]
d = {}
for x in chunks:
d[x] = []
for res in coll.find():
v = 0
texts = res["Representatives"]
for i in d:
for y in texts:
if i in y:
v = v + 1
if v != 0:
d[i].append(res["URL"])
for u in d.values()[0]:
c = 0
for x in range(1, len(d)):
for t in d.values()[x]:
if u == t:
c = c + 1
if c == (len(d) - 1):
if u not in l:
l.append(u)
print len(l)
return l
# vp=search()
# q="mdn"
# print vp.cont_search(q)
from NLP import nlp
from pymongo import MongoClient
meaningful=nlp()
Client = MongoClient()
linkk = "http://w3schools.com/css/default.asp"
# lin = "https://youtube.com/"
que = "HTML java"
class search1(object):
def __init__(self):
self.s = []
self.p = []
def process(self,query):
chunks = meaningful.process_content(query)
# print chunks
for cnt in xrange(1,4):
d = {}
e = {}
for x in chunks:
d[x]=[]
e[x]=[]
w = "P" + str(cnt)
# print w
db = Client[w]
linkData = db["linkData"]
from NLP import nlp
from pymongo import MongoClient
Client = MongoClient()
n = nlp()
class search(object):
def cont_search(self, query):
l = []
n = nlp()
chunks = n.process_content(query)
print chunks
for i in xrange(1, 4):
d = {}
for x in chunks:
d[x] = []
w = 'P' + str(i)
# print w
db = Client[w]
# ld = db["linkData"]
# ll = db["linkList"]
linkData = db["linkData"]
# for j in linkData.find():
# print j["name"]
for res in linkData.find():
v = 0
texts = res["Contents"]
for i in d:
for y in texts: