市面上已经有众多【AI伪原创】工具，看产品说明，介绍是基于NPL卷积神经网络千万语料库机器学习生成的文章。
百度“AI伪原创”，随便找一款产品，测试一下伪原创效果：
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巧了，这个伪原创的内容，跟Google中英互译两次的结果一样：
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所以我们要实现市面上“AI伪原创”的功能，不需要搞“NPL卷积神经网络千万语料库机器学习”神马的，只要调用Google翻译，执行“中--->英--->中”两次翻译即可。于是google搜罗并修改一番，见代码：
import requests
import json
from bs4 import BeautifulSoup
import execjs
from aip import AipNlp
""" 你的 APPID AK SK """
APP_ID = 'you id'
API_KEY = 'you api key'
SECRET_KEY = 'you secret key'
client = AipNlp(APP_ID, API_KEY, SECRET_KEY)
class Py4Js():
def __init__(self):
self.ctx = execjs.compile("""
function TL(a) {
var k = "";
var b = 406644;
var b1 = 3293161072;
var jd = ".";
var $b = "+-a^+6";
var Zb = "+-3^+b+-f";
for (var e = [], f = 0, g = 0; g < a.length; g++) {
var m = a.charCodeAt(g);
128 > m ? e[f++] = m : (2048 > m ? e[f++] = m >> 6 | 192 : (55296 == (m & 64512) && g + 1 < a.length && 56320 == (a.charCodeAt(g + 1) & 64512) ? (m = 65536 + ((m & 1023) << 10) + (a.charCodeAt(++g) & 1023),
e[f++] = m >> 18 | 240,
e[f++] = m >> 12 & 63 | 128) : e[f++] = m >> 12 | 224,
e[f++] = m >> 6 & 63 | 128),
e[f++] = m & 63 | 128)
}
a = b;
for (f = 0; f < e.length; f++) a += e[f],
a = RL(a, $b);
a = RL(a, Zb);
a ^= b1 || 0;
0 > a && (a = (a & 2147483647) + 2147483648);
a %= 1E6;
return a.toString() + jd + (a ^ b)
};
function RL(a, b) {
var t = "a";
var Yb = "+";
for (var c = 0; c < b.length - 2; c += 3) {
var d = b.charAt(c + 2),
d = d >= t ? d.charCodeAt(0) - 87 : Number(d),
d = b.charAt(c + 1) == Yb ? a >>> d: a << d;
a = b.charAt(c) == Yb ? a + d & 4294967295 : a ^ d
}
return a
}
""")
def getTk(self,text):
return self.ctx.call("TL",text)
def buildUrl(text,tk,language):
baseUrl='https://translate.google.cn/translate_a/single'
baseUrl+='?client=t&'
if language == 'en-zh':
baseUrl+='s1=en&'
baseUrl+='t1=zh-CN&'
baseUrl+='h1=zh-CN&'
elif language == 'zh-en':
baseUrl+='sl=zh-CN&'
baseUrl+='tl=en&'
baseUrl+='hl=zh-CN&'
baseUrl+='dt=at&'
baseUrl+='dt=bd&'
baseUrl+='dt=ex&'
baseUrl+='dt=ld&'
baseUrl+='dt=md&'
baseUrl+='dt=qca&'
baseUrl+='dt=rw&'
baseUrl+='dt=rm&'
baseUrl+='dt=ss&'
baseUrl+='dt=t&'
baseUrl+='ie=UTF-8&'
baseUrl+='oe=UTF-8&'
baseUrl+='otf=1&'
baseUrl+='pc=1&'
baseUrl+='ssel=0&'
baseUrl+='tsel=0&'
baseUrl+='kc=2&'
baseUrl+='tk='+str(tk)+'&'
baseUrl+='q='+text
return baseUrl
def translate(language,text):
header={
'authority':'translate.google.cn',
'method':'GET',
'path':'',
'scheme':'https',
'accept':'*/*',
'accept-encoding':'gzip, deflate, br',
'accept-language':'zh-CN,zh;q=0.9',
'cookie':'',
'user-agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.108 Safari/537.36',
'x-client-data':'CIa2yQEIpbbJAQjBtskBCPqcygEIqZ3KAQioo8oBGJGjygE='
}
url=buildUrl(text,js.getTk(text),language)
res=''
try:
r=requests.get(url)
result=json.loads(r.text)
#print (result)
if result[7]!=None:
# 如果我们文本输错，提示你是不是要找xxx的话，那么重新把xxx正确的翻译之后返回
try:
correctText=result[7][0].replace('<b><i>',' ').replace('</i></b>','')
print(correctText)
correctUrl=buildUrl(correctText,js.getTk(correctText),language)
correctR=requests.get(correctUrl)
newResult=json.loads(correctR.text)
res=newResult[0][0][0]
except Exception as e:
print(e)
for r in result[0]:
if r[0] is not None:
res += r[0]
else:
for r in result[0]:
if r[0] is not None:
res += r[0]
except Exception as e:
res=''
print(url)
print("翻译"+text+"失败")
print("错误信息:")
print(e)
finally:
return res
def dnnlm(text):
dnn = client.dnnlm(text)
return dnn["ppl"]
text = "测试一下这个软件好不好用，输出的文字能否读通"
if __name__ == '__main__':
js=Py4Js()
yw = translate('zh-en',text)
res_enzh = translate('en-zh',yw)
print ("原文:",text)
print ("英文:",yw)
print ("伪原创:",res_enzh)
#print (dnnlm(text),dnnlm(res_enzh))
输出结果与Google翻译一致：
image
那么问题来了，这种中英中互译两次出来的文字，搜索引擎能否看出来呢？我们找下百度AI开放平台，自然语言分析里有一项“DNN语言模型”，文档中说明可以判断句子是否符合语言表达习惯。我姑且理解为，判断一句话是人写的概率有多大
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我们依次跑下原始句子，和伪原创句子的通顺度：
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看来对百度爸爸而言，原始句子通顺的多。我们再多测试几个句子：
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蜜汁尴尬^1
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蜜汁尴尬^2
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蜜汁尴尬^3
一些搬运英文视频，添加中文字幕；或通过音频生成文章的自媒体，同理；
微信公众号---->右下角
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