/// <summary>
/// 页面加载
/// </summary>
/// <param name="sender"></param>
/// <param name="e"></param>
private void Form1_Load(object sender, EventArgs e)
{
//初始化客户端
client = new Nlp(PartOfSpeech.API_KEY, PartOfSpeech.SECRET_KEY);
//设置超时时间
client.Timeout = 60000;
}
public static void SimNet()
{
var nlp = new Nlp("Api Key", "Secret Key");
var result = nlp.Simnet("你好百度", "你好世界");
Console.Write(result);
}
public void WordSeg()
{
Nlp nlp = new Nlp("Api Key", "Secret Key");
var result = nlp.WordSeg("今天天气不错");
Console.Write(result);
}
// 短文本相似度
// 短文本相似度接口用来判断两个文本的相似度得分
public static SimnetSerialize SimnetDemo(string text1, string text2)
{
//"浙富股份","万事通自考网"
client = new Nlp(API_KEY, SECRET_KEY);
// 调用短文本相似度，可能会抛出网络等异常，请使用try/catch捕获
var result = client.Simnet(text1, text2);
//System.Diagnostics.Debug.WriteLine(result);
/*
* // 如果有可选参数
* var options = new Dictionary<string, object>{
* {"model", "CNN"}
* };
*
* // 带参数调用短文本相似度
* result = client.Simnet(text1, text2, options);
* //System.Diagnostics.Debug.WriteLine(result);
*/
SimnetSerialize res = new SimnetSerialize();
res.score = result["score"].ToString();
return(res);
}
// 词法分析
public static ResultSerialize LexerDemo(string text)
{
client = new Nlp(API_KEY, SECRET_KEY);
JObject result = client.Lexer(text);
//System.Diagnostics.Debug.WriteLine(result);
ResultSerialize res = new ResultSerialize();
res.log_id = result["log_id"].ToString();
res.text = result["text"].ToString();
res.items = (JArray)result["items"];
for (int i = 0; i < res.items.Count; i++)
{
LexerSerialize _item = new LexerSerialize();
//loc_details =
_item.byte_offset = int.Parse(res.items[i]["byte_offset"].ToString());
_item.uri = res.items[i]["uri"].ToString();
_item.pos = res.items[i]["pos"].ToString();
_item.ne = res.items[i]["ne"].ToString();
_item.item = res.items[i]["item"].ToString();
//basic_words =
_item.byte_length = int.Parse(res.items[i]["byte_length"].ToString());
_item.formal = res.items[i]["formal"].ToString();
//System.Diagnostics.Debug.Print("item是 " + res.items[i]["item"].ToString());
}
return(res);
}
public static void WordPos()
{
var nlp = new Nlp("Api Key", "Secret Key");
var result = nlp.WordPos("今天天气不错");
Console.Write(result);
}
public void WordSeg()
{
Nlp nlp = new Nlp(AiKeySecret.ApiKey, AiKeySecret.SecretKey);
var result = nlp.WordSeg("今天天气不错");
Console.Write(result);
}
public static void Dnnlm()
{
var nlp = new Nlp("Api Key", "Secret Key");
var result = nlp.DNN_LM_Cn("今天天气不错");
Console.Write(result);
}
// 词义相似度
// 两个文本相似度得分
public static WordSimEmbeddingSerialize WordSimEmbeddingDemo(string word1, string word2, Dictionary <string, object> options = null)
{
//"北京", "上海"
client = new Nlp(API_KEY, SECRET_KEY);
// 调用词义相似度，可能会抛出网络等异常，请使用try/catch捕获
var result = client.WordSimEmbedding(word1, word2);
//System.Diagnostics.Debug.WriteLine(result);
// 如果有可选参数
/*
* var options = new Dictionary<string, object>{
* {"mode", 0}
* };
*/
// 带参数调用词义相似度
//var result = client.WordSimEmbedding(word1, word2, options);
//System.Diagnostics.Debug.WriteLine(result);
WordSimEmbeddingSerialize res = new WordSimEmbeddingSerialize();
res.score = result["score"].ToString();
return(res);
}
/// <summary>
/// 文本纠错，主要程序
/// </summary>
/// <param name="path">excel试题库</param>
/// <param name="resultpath">错误内容存放的位置</param>
public static void Ecnet(string path, string resultpath, out int count)
{
count = 0;
DataTable dt = util.ExcelToDataTable(path, true);
List <Question> list = util.DatatableConvertToQuestion(dt);
Nlp client = util.CreateClient();
StringBuilder sb = null;
StreamWriter errorprint = new StreamWriter("error.txt", true);
StreamWriter ResultPrint = new StreamWriter(resultpath, true);
foreach (Question question in list)
{
sb = new StringBuilder(question.Title.Replace("_______", util.GetAnswerStr(question, question.Answer)));
sb.Append(question.Choosea);
sb.Append(question.Chooseb);
sb.Append(question.Choosec);
sb.Append(question.Choosed);
sb.Append(question.Explain);
int i = 0;
while (sb.Length - 255 * i > 0)
{
try
{
JObject result = client.Ecnet(sb.Length - 255 * i > 255 ? sb.ToString().Substring(i * 255, 255) : sb.ToString().Substring(i * 255, sb.Length - 255 * i));
JToken error_code;
//如果发生错误进行记录
if (result.TryGetValue("error_code", out error_code))
{
string ErrorPrint = question.AllID + "||" + error_code.ToString() + "||" + result["error_msg"];
errorprint.WriteLine(ErrorPrint);
errorprint.Flush();
Thread.Sleep(500);
break;
}
else //如果获得正确结果的处理
{
decimal score = Convert.ToDecimal(result["item"]["score"]);
string CorrectPrint = "Count:" + count + "||path:" + path + "||ALLID:" + question.AllID + "||SNID:" + question.SNID + "||log_id:" + result["log_id"];
Console.WriteLine(CorrectPrint);
//如果有需要纠错的内容
if (score != 0)
{
ResultPrint.WriteLine(CorrectPrint + result.ToString());
ResultPrint.Flush();
Console.WriteLine(result);
}
}
i++;
Thread.Sleep(500);
}
catch (Exception e)
{
errorprint.WriteLine(e.Message); errorprint.Flush();
}
}
count++;
}
errorprint.Close();
ResultPrint.Close();
}
public static void WordPos()
{
var nlp = new Nlp(AiKeySecret.ApiKey, AiKeySecret.SecretKey);
var result = nlp.WordPos("今天天气不错");
Console.Write(result);
}
public void Lexer()
{
Nlp nlp = new Nlp("Api Key", "Secret Key");
var result = nlp.Lexer("今天天气不错");
Console.Write(result);
}
public static void CommentTag()
{
var nlp = new Nlp("Api Key", "Secret Key");
var result = nlp.CommentTag("个人觉得这车不错，外观漂亮年轻，动力和操控性都不错", 10);
Console.Write(result);
}
public static void SentimentClassify()
{
var nlp = new Nlp("Api Key", "Secret Key");
var result = nlp.SentimentClassify("个人觉得这车不错，外观漂亮年轻，动力和操控性都不错");
Console.Write(result);
}
// 依存句法分析
public static ResultSerialize DepParserDemo(string text)
{
client = new Nlp(API_KEY, SECRET_KEY);
// 调用依存句法分析，可能会抛出网络等异常，请使用try/catch捕获
JObject result = client.DepParser(text);
//System.Diagnostics.Debug.WriteLine(result);
ResultSerialize res = new ResultSerialize();
res.log_id = result["log_id"].ToString();
res.text = result["text"].ToString();
res.items = (JArray)result["items"];
for (int i = 0; i < res.items.Count; i++)
{
DepParserSerialize _gammar = new DepParserSerialize();
//_gammar.id = res.items[i]["id"].ToString();
_gammar.word = res.items[i]["word"].ToString();
_gammar.postag = res.items[i]["postag"].ToString();
_gammar.head = res.items[i]["head"].ToString();
_gammar.deprel = res.items[i]["deprel"].ToString();
}
return(res);
// 如果有可选参数
var options = new Dictionary <string, object> {
{ "mode", 1 }
};
// 带参数调用依存句法分析
result = client.DepParser(text, options);
}
public Form1()
{
InitializeComponent();
txtRecorder = new TxtRecorder();
GetSettings();
nlp = new Nlp(txtAppKey.Text, txtSecretKey.Text);
imageClassify = new ImageClassify(txtAppKey.Text, txtSecretKey.Text);
}
private Nlp CreateLanguageClient()
{
var apiKey = string.IsNullOrEmpty(App.CloudKey) ? App.ApiKey : App.CloudKey;
var secretKey = string.IsNullOrEmpty(App.CloudSecret) ? App.SecretKey : App.CloudSecret;
var client = new Nlp(apiKey, secretKey);
client.Timeout = 60000;
return(client);
}
private Nlp getClient()
{
if (this.nlp != null)
{
return(this.nlp);
}
this.nlp = new Nlp(APIKEY, SECRETKEY);
this.nlp.Timeout = 60000;
return(this.nlp);
}
public static void WordEmbedding()
{
var nlp = new Nlp("Api Key", "Secret Key");
// 词相似度
var result = nlp.WordEmbeddingSimilarity("北京", "上海");
Console.Write(result);
// 词向量
result = nlp.WordEmbeddingVector("北京");
Console.Write(result);
}
public static void DepParser()
{
var nlp = new Nlp(AiKeySecret.ApiKey, AiKeySecret.SecretKey);
var options = new Dictionary <string, object>()
{
{ "mode", 1 }
};
var result = nlp.DepParser("今天天气不错", options);
Console.Write(result);
}
public async Task <string> AnalyseAsync(long id)
{
var news = await _context.News.FindAsync(id);
if (news.Analyseresult == null)
{
Encoding.RegisterProvider(CodePagesEncodingProvider.Instance);
StreamReader sr = new StreamReader("AnalyzerConfigs/apiconfig.json");
string json = sr.ReadToEnd();
//json文件转为 对象 T 创建的类 字段名 应该和json文件中的保持一致
var data = JsonConvert.DeserializeObject <T>(json);
var APP_ID = data.appid;
var API_KEY = data.apikey;
var SECRET_KEY = data.secretkey;
var client = new Nlp(API_KEY, SECRET_KEY);
client.Timeout = 60000; // 修改超时时间
string src = news.Content;
if (news.Content.Length > 1020)
{
if (news.Content.Length > 2980)
{
_ = src.Substring(0, 2980);
}
var content = src;
var maxSummaryLen = 300;
// 如果有可选参数
var options = new Dictionary <string, object> {
{ "title", news.Title }
};
// 带参数调用新闻摘要接口
var ress = client.NewsSummary(content, maxSummaryLen, options);
Console.WriteLine(ress);
src = (string)ress["summary"];
}
var result = client.SentimentClassify(src);
String[] res = { "negative", "neutral", "positive" };
if (result.ContainsKey("error_code"))
{
return(result.ToString());
}
news.Analyseresult = res[(int)result["items"][0]["sentiment"]];
_context.Entry(news).State = EntityState.Modified;
await _context.SaveChangesAsync();
return(res[(int)result["items"][0]["sentiment"]]);
}
return(news.Analyseresult);
}
private void SaveSettings()
{
Settings.Default.AppKey = txtAppKey.Text;
Settings.Default.SecretKey = txtSecretKey.Text;
Settings.Default.ConnectString = txtConnectString.Text;
Settings.Default.TextTable = txtTable.Text;
Settings.Default.TextColumn = txtColumn.Text;
Settings.Default.OverLoad = txtOverLoad.Text;
Settings.Default.TopicLimit = txtTopicLimit.Text;
Settings.Default.TestTik = txtTestTik.Text;
Settings.Default.Save();
nlp = new Nlp(txtAppKey.Text, txtSecretKey.Text);
}
public BaiduAi(ILogger <BaiduAi> logger, Orc orc, FaceRecognition faceRecognition, BodyAnalysis bodyAnalysis, Speech speech,
ImageClassify imageClassify, ImageSearch imageSearch, ImageEffects imageEffects, Nlp nlp)
{
_logger = logger;
_orc = orc;
_faceRecognition = faceRecognition;
_bodyAnalysis = bodyAnalysis;
_speech = speech;
_imageClassify = imageClassify;
_imageSearch = imageSearch;
_imageEffects = imageEffects;
_nlp = nlp;
}
// 词向量表示
public static WordEmbeddingSerialize WordEmbeddingDemo(string text)
{
client = new Nlp(API_KEY, SECRET_KEY);
// 调用词向量表示，可能会抛出网络等异常，请使用try/catch捕获
JObject result = client.WordEmbedding(text);
//System.Diagnostics.Debug.WriteLine(result);
WordEmbeddingSerialize res = new WordEmbeddingSerialize();
res.word = result["word"].ToString();
res.vec = (JArray)result["vec"];
return(res);
}
public static void SentimentClassify()
{
var nlp = new Nlp(AppKey, AppSecret);
const string sqlStr = "SELECT id,accountid,t_mk,vm_id FROM dbo.Sys_TaskDaily";
var taskDailyList = DapperHelper.Query <TaskDailyEntity>(sqlStr, null);
foreach (
var result in
from item in taskDailyList
where !string.IsNullOrWhiteSpace(item.T_mk)
select nlp.SentimentClassify(item.T_mk))
{
SimpleLog.Instance.WriteLogForFile("用户反馈情感分析", JsonConvert.SerializeObject(result));
}
Console.WriteLine("执行完毕！");
}
// 评论观点抽取
public static CommentTagSerialize CommentTagDemo(string text, Dictionary <string, object> options = null)
{
//"三星电脑电池不给力"
client = new Nlp(API_KEY, SECRET_KEY);
// 调用评论观点抽取，可能会抛出网络等异常，请使用try/catch捕获
JObject result = null;
try
{
//result = client.CommentTag(text); //不传option会报错
//System.Diagnostics.Debug.WriteLine(result);
// 如果有可选参数
//var options = new Dictionary<string, object>{{"type", 13}};
// 带参数调用评论观点抽取
result = client.CommentTag(text, options);
}
catch
{
//System.Console.WriteLine("是否存在 ==>> " + (result != null));
//System.Console.WriteLine("result子物体 ==>> " + result.Count);
}
CommentTagSerialize res = new CommentTagSerialize();
res.items = (JArray)result["items"];
//System.Console.WriteLine("res子物体");
//System.Console.WriteLine("finally ==>> " + res.items.First.First);
for (int i = 0; i < res.items.Count; i++)
{
CommentTag_Sub _item = new CommentTag_Sub();
_item.prop = res.items[i]["prop"].ToString();
_item.adj = res.items[i]["adj"].ToString();
_item.sentiment = int.Parse(res.items[i]["sentiment"].ToString());
_item.begin_pos = int.Parse(res.items[i]["begin_pos"].ToString());
_item.end_pos = int.Parse(res.items[i]["end_pos"].ToString());
_item.abs = res.items[i]["abstract"].ToString();
}
return(res);
}
protected override void Init()
{
base.Init();
nlpData = new Dictionary <WordType, string>();
client = new Nlp(API_KEY, SECRET_KEY)
{
Timeout = 60000
};
recognizer = new DictationRecognizer
{
AutoSilenceTimeoutSeconds = 1
};
recognizer.DictationComplete += DictationComplete;
recognizer.DictationResult += DictationResult;
recognizer.DictationError += DictationError;
recognizer.DictationHypothesis += DictationHypothesis;
recognizer.Start();
}
/// <summary>
/// 分词
/// </summary>
public static void Nlp(string text)
{
try
{
if (nlp == null)
{
string APP_ID = "14902717";
string API_KEY = "1qHCYskEsmQyMYYwGa2b4RI9";
string SECRET_KEY = "34uRO8hYapy7OTKGzuEDK3EeLyDsZOMt";
nlp = new Nlp(API_KEY, SECRET_KEY);
nlp.Timeout = 60000; // 修改超时时间
}
// 调用词法分析，可能会抛出网络等异常，请使用try/catch捕获
var result = nlp.Lexer(text);
BaiduNlp nlpEntity = JsonConvert.DeserializeObject <BaiduNlp>(result.ToString());
}
catch (Exception)
{
throw;
}
}
// DNN语言模型
public static DnnlmCnSerialize DnnlmCnDemo(string text)
{
//床前明月光;
client = new Nlp(API_KEY, SECRET_KEY);
// 调用DNN语言模型，可能会抛出网络等异常，请使用try/catch捕获
JObject result = client.DnnlmCn(text);
//System.Diagnostics.Debug.WriteLine(result);
DnnlmCnSerialize res = new DnnlmCnSerialize();
res.log_id = result["log_id"].ToString();
res.text = result["text"].ToString();
res.items = (JArray)result["items"];
for (int i = 0; i < res.items.Count; i++)
{
DnnlmCn_Sub _item = new DnnlmCn_Sub();
_item.word = res.items[i]["word"].ToString();
_item.prob = double.Parse(res.items[i]["prob"].ToString());
}
res.ppl = double.Parse(result["ppl"].ToString());
return(res);
}
public void Test()
{
nlp = new Nlp("GksxjvtX80wjcSN660ZW8QZu", "ccTZ6jSyR2OMukV9CIPukoHy27GjGzVy");
nlp.Timeout = 3000;
bool shouldContinue = true;
while (shouldContinue)
{
Console.WriteLine(" 开始英译中测试\n 输入esc退出测试 输入\'?\'百度翻译\n");
while (TestEng2ChiSingle())
{
;
}
wordList.Reset();
Console.WriteLine(" 开始中译英测试\n 输入esc退出测试 输入\'?\'百度翻译\n");
while (TestChi2EngSingle())
{
;
}
if (shouldMarkAsPass && testPassFlag.Key && testPassFlag.Value)
{
MarkAsPass();
}
shouldContinue = chooseContinue();
if (shouldContinue)
{
wordList.Reset();
}
}
}
