Quickstart: Use the Translator Text API to detect text language using C#

  • 5 minutes to read
  • Contributors

In this quickstart, you'll learn how to detect the language of provided text using .NET Core, C# 7.1 or later, and the Translator Text REST API.

This quickstart requires an Azure Cognitive Services account with a Translator Text resource. If you don't have an account, you can use the free trial to get a subscription key.

Prerequisites

Create a .NET Core project

Open a new command prompt (or terminal session) and run these commands:

dotnet new console -o detect-sample
cd detect-sample

The first command does two things. It creates a new .NET console application, and creates a directory named detect-sample. The second command changes to the directory for your project.

Next, you'll need to install Json.Net. From your project's directory, run:

dotnet add package Newtonsoft.Json --version 11.0.2

Select the C# language version

This quickstart requires C# 7.1 or later. There are a few ways to change the C# version for your project. In this guide, we'll show you how to adjust the detect-sample.csproj file. For all available options, such as changing the language in Visual Studio, see Select the C# language version.

Open your project, then open detect-sample.csproj. Make sure that LangVersion is set to 7.1 or later. If there isn't a property group for the language version, add these lines:

<PropertyGroup>
   <LangVersion>7.1</LangVersion>
</PropertyGroup>

Add required namespaces to your project

The dotnet new console command that you ran earlier created a project, including Program.cs. This file is where you'll put your application code. Open Program.cs, and replace the existing using statements. These statements ensure that you have access to all the types required to build and run the sample app.

using System;
using System.Net.Http;
using System.Text;
using System.Threading.Tasks;
// Install Newtonsoft.Json with NuGet
using Newtonsoft.Json;

Create classes for the JSON response

Next, we're going to create a class that's used when deserializing the JSON response returned by the Translator Text API.

/// <summary>
/// The C# classes that represents the JSON returned by the Translator Text API.
/// </summary>
public class DetectResult
{
    public string Language { get; set; }
    public float Score { get; set; }
    public bool IsTranslationSupported { get; set; }
    public bool IsTransliterationSupported { get; set; }
    public AltTranslations[] Alternatives { get; set; }
}
public class AltTranslations
{
    public string Language { get; set; }
    public float Score { get; set; }
    public bool IsTranslationSupported { get; set; }
    public bool IsTransliterationSupported { get; set; }
}

Create a function to detect the source text's language

Within the Program class, create a function called DetectTextRequest(). This class encapsulates the code used to call the Detect resource and prints the result to console.

static public async Task DetectTextRequest(string subscriptionKey, string host, string route, string inputText)
{
  /*
   * The code for your call to the translation service will be added to this
   * function in the next few sections.
   */
}

Serialize the detect request

Next, we need to create and serialize the JSON object that includes the text that will undergo language detection.

System.Object[] body = new System.Object[] { new { Text = inputText } };
var requestBody = JsonConvert.SerializeObject(body);

Instantiate the client and make a request

These lines instantiate the HttpClient and the HttpRequestMessage:

using (var client = new HttpClient())
using (var request = new HttpRequestMessage())
{
  // In the next few sections you'll add code to construct the request.
}

Construct the request and print the response

Inside the HttpRequestMessage you'll:

  • Declare the HTTP method
  • Construct the request URI
  • Insert the request body (serialized JSON object)
  • Add required headers
  • Make an asynchronous request
  • Print the response

Add this code to the HttpRequestMessage:

// Build the request.
request.Method = HttpMethod.Post;
// Construct the URI and add headers.
request.RequestUri = new Uri(host + route);
request.Content = new StringContent(requestBody, Encoding.UTF8, "application/json");
request.Headers.Add("Ocp-Apim-Subscription-Key", subscriptionKey);

// Send the request and get response.
HttpResponseMessage response = await client.SendAsync(request).ConfigureAwait(false);
// Read response as a string.
string result = await response.Content.ReadAsStringAsync();
// Deserialize the response using the classes created earlier.
DetectResult[] deserializedOutput = JsonConvert.DeserializeObject<DetectResult[]>(result);
// Iterate over the deserialized response.
foreach (DetectResult o in deserializedOutput)
{
    Console.WriteLine("The detected language is '{0}'. Confidence is: {1}.\nTranslation supported: {2}.\nTransliteration supported: {3}.\n",
        o.Language, o.Score, o.IsTranslationSupported, o.IsTransliterationSupported);
    // Create a counter
    int counter = 0;
    // Iterate over alternate translations.
    foreach (AltTranslations a in o.Alternatives)
    {
        counter++;
        Console.WriteLine("Alternative {0}", counter);
        Console.WriteLine("The detected language is '{0}'. Confidence is: {1}.\nTranslation supported: {2}.\nTransliteration supported: {3}.\n",
            a.Language, a.Score, a.IsTranslationSupported, a.IsTransliterationSupported);
    }
}

If you are using a Cognitive Services multi-service subscription, you must also include the Ocp-Apim-Subscription-Region in your request parameters. Learn more about authenticating with the multi-service subscription.

Put it all together

The last step is to call DetectTextRequest() in the Main function. Locate static void Main(string[] args) and replace it with this code:

static async Task Main(string[] args)
{
    // This is our main function.
    // Output languages are defined in the route.
    // For a complete list of options, see API reference.
    string subscriptionKey = "YOUR_TRANSLATOR_TEXT_KEY_GOES_HERE";
    string host = "https://api.cognitive.microsofttranslator.com";
    string route = "/detect?api-version=3.0";
    string breakSentenceText = @"How are you doing today? The weather is pretty pleasant. Have you been to the movies lately?";
    await DetectTextRequest(subscriptionKey, host, route, breakSentenceText);
}

Run the sample app

That's it, you're ready to run your sample app. From the command line (or terminal session), navigate to your project directory and run:

dotnet run

Sample response

After you run the sample, you should see the following printed to terminal:

Note

Find the country/region abbreviation in this list of languages.

The detected language is 'en'. Confidence is: 1.
Translation supported: True.
Transliteration supported: False.

Alternative 1
The detected language is 'fil'. Confidence is: 0.82.
Translation supported: True.
Transliteration supported: False.

Alternative 2
The detected language is 'ro'. Confidence is: 1.
Translation supported: True.
Transliteration supported: False.

This message is built from the raw JSON, which will look like this:

[  
    {  
        "language":"en",
        "score":1.0,
        "isTranslationSupported":true,
        "isTransliterationSupported":false,
        "alternatives":[  
            {  
                "language":"fil",
                "score":0.82,
                "isTranslationSupported":true,
                "isTransliterationSupported":false
            },
            {  
                "language":"ro",
                "score":1.0,
                "isTranslationSupported":true,
                "isTransliterationSupported":false
            }
        ]
    }
]

Clean up resources

Make sure to remove any confidential information from your sample app's source code, like subscription keys.

Next steps

Explore the sample code for this quickstart and others, including translation and transliteration, as well as other sample Translator Text projects on GitHub.

Explore C# examples on GitHub

See also