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268 lines (227 loc) · 8.43 KB
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#! /bin/env python
# -*- coding: utf-8 -*-
"""
泛读模式,提供点赞数最多并且差评数较少的回答
修改版
"""
import requests
from bs4 import BeautifulSoup
import jieba
import urllib
# from tqdm import *
import time, threading
import random
import get_weather
import sys
sys.path.append('/mnt/omada/router/code')
import twserver
from twserver import main
# from selenium import webdriver
# from time import ctime
# from sklearn.feature_extraction.text import CountVectorizer
# from sklearn.feature_extraction.text import TfidfTransformer
stopwords = ['是', '的', '谁', '什么', '和', '了', '我', '你', '知道', '哪', '?', '?', ',', ',', '.', '。', ':', ':']
def clean_question(question):
ques = list(jieba.cut(question))
for w in stopwords:
if w in ques: ques.remove(w)
return ques
def match_key_words(main_ques, other):
# if len(other) < 8:
# return True
for word in main_ques:
if word in other:
return True
return False
def parse_subweb(url, ques):
badwords = []
with open("./badwords.txt", 'r', encoding='utf-8') as b:
for line in b.readlines():
badwords.append(line.strip('\n'))
url_sub = url.get('href')
wb_data_sub = requests.get(url_sub)
wb_data_sub.encoding = ('gbk')
soup_sub = BeautifulSoup(wb_data_sub.content, 'lxml')
best_answer = soup_sub.find('div', class_="best-text mb-10")
agree_point_p = soup_sub.find('span', class_="iknow-qb_home_icons evaluate evaluate-32 ")
disagree_point_p = soup_sub.find('span', class_="iknow-qb_home_icons evaluate evaluate-bad evaluate-32 ")
agree_point = 0
disagree_point = 0
if agree_point_p != None and disagree_point_p != None:
agree_point = int(agree_point_p.get('data-evaluate'))
disagree_point = int(disagree_point_p.get('data-evaluate'))
# print(agree_point, disagree_point)
if best_answer != None:
best = best_answer.get_text(strip=True)
# 如果问题的关键词,出现在了答案中,则判断是好的回答,改进,可以根据点赞比判断
# 长度小于100,过滤“展开全部”
contain_badword = [badword for badword in badwords if badword in best]
if match_key_words(ques, best) and len(best) < 1000 \
and len(contain_badword) == 0:
best = best.strip("展开全部")
best = best.strip("展开")
return best
else:
better_answer = soup_sub.find_all('div', class_="answer-text mb-10 line")
if better_answer != None:
for i_better, better_answer_sub in enumerate(better_answer):
better = better_answer_sub.get_text(strip=True)
contain_badword = [badword for badword in badwords if badword in better]
if match_key_words(ques, better) and len(better) < 1000 \
and len(contain_badword) == 0:
better = better.strip("展开全部")
better = better.strip("展开")
return better
def get_top_page(ques, one, url):
evidences = []
page_question_No = 1 + one
# print("url: " + url)
wb_data = requests.get(url)
wb_data.encoding = ('gbk')
soup = BeautifulSoup(wb_data.content, 'lxml')
webdata = soup.select('a.ti')
# import multiprocessing
# pool = multiprocessing.Pool(processes=4)
for title, url in zip(webdata, webdata):
# evidence = pool.apply_async(parse_subweb, (url, ques, ))
# if evidence.get() != None:
# evidences.append(evidence.get())
evidence = parse_subweb(url, ques)
if evidence != None:
evidences.append(evidence)
break
page_question_No += 1
# pool.close()
# pool.join()
return evidences
def get_page(ques, one, url):
evidences = []
page_question_No = 1 + one
# print("url: " + url)
wb_data = requests.get(url)
wb_data.encoding = ('gbk')
soup = BeautifulSoup(wb_data.content, 'lxml')
webdata = soup.select('a.ti')
# import multiprocessing
# pool = multiprocessing.Pool(processes=4)
for title, url in zip(webdata, webdata):
# evidence = pool.apply_async(parse_subweb, (url, ques, ))
# if evidence.get() != None:
# evidences.append(evidence.get())
evidence = parse_subweb(url, ques)
if evidence != None:
evidences.append(evidence)
page_question_No += 1
# pool.close()
# pool.join()
return evidences
evidencess = []
def get_evidences(question, pages=2):
print('Getting eivdences from baiduzhidao....')
url = "https://zhidao.baidu.com/search?word=" + urllib.parse.quote(question) + "&pn="
ques = clean_question(question)
evidences_list = []
for one in range(0, pages, 1):
evidencess = []
# evidences = get_multi_thread_page(ques, one, url + str(one))
# evidences = get_page(ques, one, url + str(one))
evidences = get_top_page(ques, one, url + str(one))
if evidences != []:
evidences_list.extend(evidences)
# time.sleep(1)
print('evidences: ', len(evidences_list))
# evidences_list = rank(evidneces_list)
return evidences_list
# ---------------------------------
def rule_engine(input):
"""
目前是比较简单的规则(随机数)
:param input:
:return:
"""
random.shuffle(input)
return input[0]
# evidencess = []
lock = threading.Lock()
def get_href(ques, title, url):
url_sub = url.get('href')
wb_data_sub = requests.get(url_sub)
wb_data_sub.encoding = ('gbk')
soup_sub = BeautifulSoup(wb_data_sub.content, 'lxml')
best_answer = soup_sub.find('pre', class_="best-text mb-10")
evidences = ['no_answer']
if best_answer != None:
best = best_answer.get_text(strip=True)
if match_key_words(ques, best):
if lock.acquire():
evidencess.append(best)
lock.release()
# print(evidencess)
else:
better_answer = soup_sub.find_all('div', class_="answer-text line")
if better_answer != None:
for i_better, better_answer_sub in enumerate(better_answer):
better = better_answer_sub.get_text(strip=True)
if match_key_words(ques, better):
if lock.acquire():
evidencess.append(better)
lock.release()
# print(evidencess)
# return 1 #evidences
def get_multi_thread_page(ques, one, url):
threads = []
# evidences = []
page_question_No = 1 + one
wb_data = requests.get(url)
wb_data.encoding = ('gbk')
soup = BeautifulSoup(wb_data.content, 'lxml')
webdata = soup.select('a.ti')
nb_thread = len(webdata)
for i in range(nb_thread):
t = threading.Thread(target=get_href(ques, webdata[i], webdata[i]), name='LoopThread')
threads.append(t)
t.start()
for t in threads:
t.join()
# href_evidences = t.get_result()
# evidneces.extend(href_evidences)
return evidencess
def webQA(user_input):
"""
主程序
:param user_input: userid + "$" + question
:return: userid + "$" + "服务器编号" + "#" + answer + "*the_end"
"""
id = user_input.split('$')[0]
question = user_input.split('$')[1]
answer = ""
wq = get_weather.weather_query()
flag = 0
weather_words = ["天气", "气温", "风速", "风向", "温度"]
for item in weather_words:
if item in question:
flag = 1
if flag:
date, city_name = wq.match_rule(question)
info = wq.go(city_name, date)
# print(info)
if info == "这个城市没有查不到..." or info == "抱歉,您查找的天气信息暂时没有哦~":
evidences = get_evidences(question)
# 规则模块
rule_rank = rule_engine(list(range(len(evidences))))
answer = evidences[rule_rank]
else:
answer = info
else:
evidences = get_evidences(question)
# 规则模块
rule_rank = rule_engine(list(range(len(evidences))))
answer = evidences[rule_rank]
# evidences = get_evidences(question)
# # 规则模块
# rule_rank = rule_engine(list(range(len(evidences))))
# answer = evidences[rule_rank]
output = id + '$' + '4' + '#' + answer + "*the_end"
return output
if __name__ == '__main__':
main(webQA, 10004, '0.0.0.0')