// POST api/messages
public async Task<IHttpActionResult> Post(JObject message)
{
Console.WriteLine($"Message Received: {message}");
var content = message["content"].Value<string>();
var from = message["from"].Value<string>();
var messageContent = "";
switch (content.Trim())
{
case "info":
case "informação":
case "about":
messageContent = "Meu nome é Jonh Lorem Foo, eu tenho 25 anos. \n\nVocê pode me encontrar por telefone: +55 31 99827 1039 ou através do meu email: [email protected]";
break;
case "formação":
case "formacao":
case "education":
messageContent = "*Mestre em Física Nuclear pela NASA\n*Graduado em Economia pela Faculdade de Harvard - USA";
break;
case "experiência":
case "experiencia":
case "experience":
messageContent = "Possuo 10 anos de experiência em análise de dados complexos. Meus últimos trabalhos foram para:\n\nGoogle\nFacebook\nNSA\nMicrosoft";
break;
case "habilidade":
case "skills":
messageContent = "Principais habilidades: \n\nComunicador\nExtrovertido\nGosta de trabalhar em equipe\nProgramação Android";
break;
default:
//default message if user send a unknow command
messageContent = "Oi, eu sou o chat bot do Jonh :) \nPosso lhe passar várias informações profissionais sobre ele. \n\nSe quiser saber mais me mande um dos comandos abaixo: \n\nabout\neducation\nexperience\nskills!";
break;
}
var replyMessage = new
{
id = Guid.NewGuid(),
to = from,
type = "text/plain",
content = messageContent
};
await ReplyMessageAsync(replyMessage);
return Ok();
}
public async Task InvokeRequestResponseService(string URLText, string APIKey, string MLColumns, string MLRows)
{
using (var client = new HttpClient())
{
JavaScriptSerializer js = new JavaScriptSerializer();
string[] columns = js.Deserialize<string[]>(MLColumns);
string[,] rows = JsonConvert.DeserializeObject<string[,]>(MLRows);
var scoreRequest = new
{
Inputs = new Dictionary<string, StringTable>() {
{
"input1",
new StringTable()
{
//ColumnNames = new string[] {"age", "workclass", "fnlwgt", "education", "education-num", "marital-status", "occupation", "relationship", "race", "sex", "capital-gain", "capital-loss", "hours-per-week", "native-country", "income"},
ColumnNames =columns,
//Values = new string[,] { { "40", "Private", "155594", "Masters", "9", "Married-civ-spouse", "Sales", "Husband", "White", "Male", "0", "0", "40", "United-States", "<=50K" }, { "80", "Private", "331474", "Masters", "9", "Married-civ-spouse", "Adm-clerical", "Wife", "White", "Female", "0", "0", "40", "United-States", "<=50K" }, }
Values = rows
}
},
},
GlobalParameters = new Dictionary<string, string>()
{
}
};
//Testing
//APIKey = "D1N/VNr+2BYidVeuH+2rSmFHCuid4QHw4MoLgrV7cxKAsG2spnOrjByRNGaE5oTIt4RohlSL5O/I26+UeKc1mw=="; // Replace this with the API key for the web service
client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("Bearer", APIKey);
//Testing
//client.BaseAddress = new Uri("https://ussouthcentral.services.azureml.net/workspaces/f8e2d18f739148248b1c73fbfae07fe2/services/28070a209e724b0c9a1b634c32d287f4/execute?api-version=2.0&details=true");
client.BaseAddress = new Uri(URLText);
// WARNING: The 'await' statement below can result in a deadlock if you are calling this code from the UI thread of an ASP.Net application.
// One way to address this would be to call ConfigureAwait(false) so that the execution does not attempt to resume on the original context.
// For instance, replace code such as:
// result = await DoSomeTask()
// with the following:
// result = await DoSomeTask().ConfigureAwait(false)
HttpResponseMessage response = await client.PostAsJsonAsync("", scoreRequest).ConfigureAwait(false);
if (response.IsSuccessStatusCode)
{
this.MLResult = await response.Content.ReadAsStringAsync();
}
else
{
this.MLResult = await response.Content.ReadAsStringAsync();
// Get the headers - they include the requert ID and the timestamp, which are useful for debugging the failure
//return string.Format("The request failed with status code: {0}", response.StatusCode) + response.Headers.ToString() + responseContent;
}
}
}
