Access Authenticated Using a Token

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.

• 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.

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.
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.

   • 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).

     • 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: The field values in the return result vary with the AI application.
   • Inference result using text input: 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.

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.

   • 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 value of base64_data in body is of the string type.