Accessing a Real-Time Service Through Token-based Authentication

Constraints

Prerequisites

You have obtained a user token, local path to the inference file, URL of the real-time service, and input parameters of the real-time service.

• For details about how to obtain a user token, see Token-based Authentication. The real-time service APIs generated by ModelArts do not support tokens whose scope is domain. Therefore, you need to obtain the token whose scope is project.
• The local path to the inference file can be an absolute path (for example, D:/test.png for Windows and /opt/data/test.png for Linux) or a relative path (for example, ./test.png).
• You can obtain the service URL and input parameters of a real-time service on the Usage Guides tab page of its service details page.

  The API URL is the service URL of the real-time service. If a path is defined for apis in the model configuration file, the URL must be followed by the user-defined path, for example, {URL of the real-time service}/predictions/poetry.

  Figure 1 Obtaining the API URL and file prediction input parameters of a real-time service
  

Method 1: Use GUI-based Software for Inference (Postman)

1. Download Postman and install it, or install the Postman Chrome extension. Alternatively, use other software that can send POST requests. Postman 7.24.0 is recommended.
2. Open Postman. Figure 2 shows the Postman interface.
   Figure 2 Postman interface
   
3. Set parameters on Postman. The following uses image classification as an example.
   • Select a POST task and copy the API URL to the POST text box. On the Headers tab page, set Key to X-Auth-Token and Value to the user token.
     

     You can also use the AK and SK to encrypt API calling requests. For details, see Overview of Session Authentication.

     Figure 3 Parameter settings
     
   • On the Body tab page, file input and text input are available.
     • File input

       Select form-data. Set KEY to the input parameter of the AI application, which must be the same as the input parameter of the real-time service. In this example, the KEY is images. Set VALUE to an image to be inferred (only one image can be inferred). See Figure 4.

       Figure 4 Setting parameters on the Body tab page
       
     • Text input

       Select raw and then JSON(application/json). Enter the request body in the text box below. An example request body is as follows:

       {
         "meta": {
           "uuid": "10eb0091-887f-4839-9929-cbc884f1e20e"
         },
         "data": {
           "req_data": [
             {
               "sepal_length": 3,
               "sepal_width": 1,
               "petal_length": 2.2,
               "petal_width": 4
             }
           ]
         }
       }

       meta can carry a universally unique identifier (UUID). When the inference result is returned after API calling, the UUID is returned to trace the request. If you do not need this function, leave meta blank. data contains a req_data array for one or multiple pieces of input data. The parameters of each piece of data, such as sepal_length and sepal_width in this example are determined by the AI application.

4. After setting the parameters, click send to send the request. The result will be displayed in Response.
   • Inference result using file input: Figure 5 shows an example. The field values in the return result vary with the AI application.
   • Inference result using text input: Figure 6 shows an example. The request body contains meta and data. If the request contains uuid, uuid will be returned in the response. Otherwise, uuid is left blank. data contains a resp_data array for the inference results of one or multiple pieces of input data. The parameters of each result are determined by the AI application, for example, sepal_length and predictresult in this example.
     Figure 5 File inference result
     
     Figure 6 Text inference result
     

Method 2: Run the cURL Command to Send an Inference Request

The command for sending inference requests can be input as a file or text.

Method 3: Use Python to Send an Inference Request

1. Download the Python SDK and configure it in the development tool. For details, see Integrating the Python SDK for API request signing.
2. Create a request body for inference.
   • File input

     # coding=utf-8
     
     import requests
     
     if __name__ == '__main__':
         # Config url, token and file path.
         url = "URL of the real-time service"
         token = "User token"
         file_path = "Local path to the inference file"
     
         # Send request.
         headers = {
             'X-Auth-Token': token
         }
         files = {
             'images': open(file_path, 'rb')
         }
         resp = requests.post(url, headers=headers, files=files)
     
         # Print result.
         print(resp.status_code)
         print(resp.text)

     The files name is determined by the input parameter of the real-time service. The parameter name must be the same as that of the input parameter of the file type. The input parameter images obtained in Prerequisites is an example.

   • Text input (JSON)

     The following is an example of the request body for reading the local inference file and performing Base64 encoding:

     # coding=utf-8
     
     import base64
     import requests
     
     if __name__ == '__main__':
         # Config url, token and file path
         url = "URL of the real-time service"
         token = "User token"
         file_path = "Local path to the inference file"
         with open(file_path, "rb") as file:
             base64_data = base64.b64encode(file.read()).decode("utf-8")
     
         # Set body,then send request
         headers = {
             'Content-Type': 'application/json',
             'X-Auth-Token': token
         }
         body = {
             'image': base64_data
         }
         resp = requests.post(url, headers=headers, json=body)
     
         # Print result
         print(resp.status_code)
         print(resp.text)

     The body name is determined by the input parameter of the real-time service. The parameter name must be the same as that of the input parameter of the string type. The input parameter images obtained in Prerequisites is an example. The value of base64_data in body is of the string type.

Method 4: Use Java to Send an Inference Request

1. Download the Java SDK and configure it in the development tool. For details, see Integrating the Java SDK for API request signing.
2. (Optional) If the input of the inference request is in the file format, the Java project depends on the httpmime module.
   1. Add httpmime-x.x.x.jar to the libs folder. Figure 7 shows a complete Java dependency library.

      You are advised to use httpmime-x.x.x.jar 4.5 or later. Download httpmime-x.x.x.jar from https://mvnrepository.com/artifact/org.apache.httpcomponents/httpmime.

      Figure 7 Java dependency library
      
   2. After httpmime-x.x.x.jar is added, add httpmime information to the .classpath file of the Java project as follows:

      <?xml version="1.0" encoding="UTF-8"?>
      <classpath>
      <classpathentry kind="con" path="org.eclipse.jdt.launching.JRE_CONTAINER"/>
      <classpathentry kind="src" path="src"/>
      <classpathentry kind="lib" path="libs/commons-codec-1.11.jar"/>
      <classpathentry kind="lib" path="libs/commons-logging-1.2.jar"/>
      <classpathentry kind="lib" path="libs/httpclient-4.5.13.jar"/>
      <classpathentry kind="lib" path="libs/httpcore-4.4.13.jar"/>
      <classpathentry kind="lib" path="libs/httpmime-x.x.x.jar"/>
      <classpathentry kind="lib" path="libs/java-sdk-core-3.1.2.jar"/>
      <classpathentry kind="lib" path="libs/okhttp-3.14.9.jar"/>
      <classpathentry kind="lib" path="libs/okio-1.17.2.jar"/>
      <classpathentry kind="output" path="bin"/>
      </classpath>

3. Create a Java request body for inference.
   • File input
     A sample Java request body is as follows:

     // Package name of the demo.
     package com.apig.sdk.demo;
     
     import org.apache.http.Consts;
     import org.apache.http.HttpEntity;
     import org.apache.http.client.methods.CloseableHttpResponse;
     import org.apache.http.client.methods.HttpPost;
     import org.apache.http.entity.ContentType;
     import org.apache.http.entity.mime.MultipartEntityBuilder;
     import org.apache.http.impl.client.HttpClients;
     import org.apache.http.util.EntityUtils;
     
     import java.io.File;
     
     public class MyTokenFile {
     
         public static void main(String[] args) {
             // Config url, token and filePath
             String url = "URL of the real-time service";
             String token = "User token";
             String filePath = "Local path to the inference file";
     
             try {
                 // Create post
                 HttpPost httpPost = new HttpPost(url);
     
                 // Add header parameters
                 httpPost.setHeader("X-Auth-Token", token);
     
                 // Add a body if you have specified the PUT or POST method. Special characters, such as the double quotation mark ("), contained in the body must be escaped.
                 File file = new File(filePath);
                 HttpEntity entity = MultipartEntityBuilder.create().addBinaryBody("images", file).setContentType(ContentType.MULTIPART_FORM_DATA).setCharset(Consts.UTF_8).build();
                 httpPost.setEntity(entity);
     
                 // Send post
                 CloseableHttpResponse response = HttpClients.createDefault().execute(httpPost);
     
                 // Print result
                 System.out.println(response.getStatusLine().getStatusCode());
                 System.out.println(EntityUtils.toString(response.getEntity()));
             } catch (Exception e) {
                 e.printStackTrace();
             }
         }
     }

     The addBinaryBody name is determined by the input parameter of the real-time service. The parameter name must be the same as that of the input parameter of the file type. The file images obtained in Prerequisites is used as an example.

   • Text input (JSON)

     The following is an example of the request body for reading the local inference file and performing Base64 encoding:

     // Package name of the demo.
     package com.apig.sdk.demo;
     
     import org.apache.http.HttpHeaders;
     import org.apache.http.client.methods.CloseableHttpResponse;
     import org.apache.http.client.methods.HttpPost;
     import org.apache.http.entity.StringEntity;
     import org.apache.http.impl.client.HttpClients;
     import org.apache.http.util.EntityUtils;
     
     public class MyTokenTest {
     
         public static void main(String[] args) {
             // Config url, token and body
             String url = "URL of the real-time service";
             String token = "User token";
             String body = "{}";
     
             try {
                 // Create post
                 HttpPost httpPost = new HttpPost(url);
     
                 // Add header parameters
                 httpPost.setHeader(HttpHeaders.CONTENT_TYPE, "application/json");
                 httpPost.setHeader("X-Auth-Token", token);
     
                 // Special characters, such as the double quotation mark ("), contained in the body must be escaped.
                 httpPost.setEntity(new StringEntity(body));
     
                 // Send post.
                 CloseableHttpResponse response = HttpClients.createDefault().execute(httpPost);
     
                 // Print result
                 System.out.println(response.getStatusLine().getStatusCode());
                 System.out.println(EntityUtils.toString(response.getEntity()));
             } catch (Exception e) {
                 e.printStackTrace();
             }
         }
     }

     body is determined by the text format. JSON is used as an example.