curl --request POST \
  --url https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: <api-key>' \
  --data '
{
  "imageFileBase64": "<string>",
  "input": {
    "resizeMode": "squash"
  },
  "model": {
    "modelType": "classification",
    "labels": [
      "<string>"
    ]
  }
}
'

import requests

url = "https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image"

payload = {
    "imageFileBase64": "<string>",
    "input": { "resizeMode": "squash" },
    "model": {
        "modelType": "classification",
        "labels": ["<string>"]
    }
}
headers = {
    "x-api-key": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'x-api-key': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    imageFileBase64: '<string>',
    input: {resizeMode: 'squash'},
    model: {modelType: 'classification', labels: ['<string>']}
  })
};

fetch('https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'imageFileBase64' => '<string>',
    'input' => [
        'resizeMode' => 'squash'
    ],
    'model' => [
        'modelType' => 'classification',
        'labels' => [
                '<string>'
        ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "x-api-key: <api-key>"
  ],
]);

$response = curl_exec($curl);
$err = curl_error($curl);

curl_close($curl);

if ($err) {
  echo "cURL Error #:" . $err;
} else {
  echo $response;
}

package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image"

	payload := strings.NewReader("{\n  \"imageFileBase64\": \"<string>\",\n  \"input\": {\n    \"resizeMode\": \"squash\"\n  },\n  \"model\": {\n    \"modelType\": \"classification\",\n    \"labels\": [\n      \"<string>\"\n    ]\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("x-api-key", "<api-key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image")
  .header("x-api-key", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"imageFileBase64\": \"<string>\",\n  \"input\": {\n    \"resizeMode\": \"squash\"\n  },\n  \"model\": {\n    \"modelType\": \"classification\",\n    \"labels\": [\n      \"<string>\"\n    ]\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://studio.edgeimpulse.com/v1/api/{projectId}/pretrained-model/test-image")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["x-api-key"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"imageFileBase64\": \"<string>\",\n  \"input\": {\n    \"resizeMode\": \"squash\"\n  },\n  \"model\": {\n    \"modelType\": \"classification\",\n    \"labels\": [\n      \"<string>\"\n    ]\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "success": true,
  "error": "<string>",
  "result": {},
  "boundingBoxes": [
    {
      "label": "<string>",
      "x": 123,
      "y": 123,
      "width": 123,
      "height": 123,
      "score": 123
    }
  ],
  "freeformResult": {
    "outputTensors": [
      {
        "shape": [
          123
        ],
        "data": [
          123
        ]
      }
    ]
  },
  "anomalyResult": [
    {
      "boxes": [
        {
          "label": "<string>",
          "x": 123,
          "y": 123,
          "width": 123,
          "height": 123,
          "score": 123
        }
      ],
      "scores": [
        [
          123
        ]
      ],
      "meanScore": 123,
      "maxScore": 123
    }
  ]
}