curl --location --request POST 'https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality' --header 'Authorization: Bearer YOUR_API_KEY' 

--data-raw '[
  {
    "id": 0,
    "text": "This is an example:\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model."
  }
]'

    

import okhttp3.OkHttpClient;
import okhttp3.Request;
import okhttp3.Response;
import java.io.IOException;

public class Main {
    public static void main(String[] args) {
        OkHttpClient client = new OkHttpClient();

        // Build the GET request
        Request request = new Request.Builder()
            .url("https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality")
            .get() // Use GET method without a body
            .addHeader("Authorization", "Bearer YOUR_API_KEY")
            .build();

        // Execute the request
        try (Response response = client.newCall(request).execute()) {
            if (response.isSuccessful()) {
                // Handle the response in case of a successful request
                String responseData = response.body().string();
                System.out.println(responseData);
            } else {
                // Handle errors
                System.out.println("Request error: " + response.code());
            }
        } catch (IOException e) {
            // Handle I/O exceptions
            e.printStackTrace();
        }
    }
}

var settings = {
  "url": "https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality",
  "method": "POST",
  "timeout": 0,
  "headers": {
    "Authorization": "Bearer YOUR_API_KEY"
  },
};

$.ajax(settings).done(function (response) {
  console.log(response);
});

var data = "[\r\n  {\r\n    \"id\": 0,\r\n    \"text\": \"This is an example:\\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model.\"\r\n  }\r\n]";
var xhr = new XMLHttpRequest();
xhr.withCredentials = true;
    
xhr.addEventListener("readystatechange", function() {
if(this.readyState === 4) {
    console.log(this.responseText);
    }
});
    
xhr.open("POST", "https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality");
xhr.setRequestHeader("Authorization", "Bearer YOUR_API_KEY");
xhr.send(data);

var axios = require('axios');
var data = "[\r\n  {\r\n    \"id\": 0,\r\n    \"text\": \"This is an example:\\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model.\"\r\n  }\r\n]";
var config = {
method: 'POST',
url: 'https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality',
headers: { 
    'Authorization': 'Bearer YOUR_API_KEY'
    },
data : data
};
axios(config).then(function (response) {
console.log(JSON.stringify(response.data));
})
.catch(function (error) {
console.log(error);
});

$curl = curl_init();
    
curl_setopt_array($curl, array(
CURLOPT_URL => 'https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality',
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => '',
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 0,
CURLOPT_FOLLOWLOCATION => true,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => 'POST',
CURLOPT_POSTFIELDS => '[
  {
    "id": 0,
    "text": "This is an example:\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model."
  }
]',
CURLOPT_HTTPHEADER => array(
    'Authorization: Bearer YOUR_API_KEY'
    ),
));
    
$response = curl_exec($curl);
    
curl_close($curl);
echo $response;

import requests
    
url = "https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality"
    
payload = "[\r\n  {\r\n    \"id\": 0,\r\n    \"text\": \"This is an example:\\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model.\"\r\n  }\r\n]" 
headers = {
    'Authorization': 'Bearer YOUR_API_KEY'
}
    
response = requests.request("POST", url, headers=headers, data=payload)
    
print(response.text)
    

require "uri"
require "net/http"
    
url = URI("https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality")
    
http = Net::HTTP.new(url.host, url.port);
request = Net::HTTP::POST.new(url)
request["Authorization"] = "Bearer YOUR_API_KEY"
request.body = "[\r\n  {\r\n    \"id\": 0,\r\n    \"text\": \"This is an example:\\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model.\"\r\n  }\r\n]"    
response = http.request(request)
puts response.read_body

printf '[
  {
    "id": 0,
    "text": "This is an example:\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model."
  }
]'| 
http --follow --timeout 3600 POST 'https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality' \
Authorization:'Bearer YOUR_API_KEY'

var client = new RestClient("https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality");
client.Timeout = -1;
var request = new RestRequest(Method.POST);
request.AddHeader("Authorization", "Bearer YOUR_API_KEY");
var body = "[\r\n  {\r\n    \"id\": 0,\r\n    \"text\": \"This is an example:\\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model.\"\r\n  }\r\n]";
request.AddParameter("text/plain", body, ParameterType.RequestBody);
IRestResponse response = client.Execute(request);
Console.WriteLine(response.Content);

package main
import (
    "fmt"
    "net/http"
    "io/ioutil"
)
func main() {
    url := "https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality"
    method := "POST"
    payload := strings.NewReader("[
  {
    "id": 0,
    "text": "This is an example:\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model."
  }
]")
    client := &http.Client {
    }
    req, err := http.NewRequest(method, url, nil)
    if err != nil {
    fmt.Println(err)
    return
    }
    req.Header.Add("Authorization", "Bearer YOUR_API_KEY")
    res, err := client.Do(req)
    if err != nil {
    fmt.Println(err)
    return
    }
    defer res.Body.Close()

    body, err := ioutil.ReadAll(res.Body)
    if err != nil {
    fmt.Println(err)
    return
    }
    fmt.Println(string(body))
}

import Foundation

let url = URL(string: "https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality")!
var request = URLRequest(url: url)
request.httpMethod = "POST"
request.setValue("Bearer YOUR_API_KEY", forHTTPHeaderField: "Authorization")
request.httpBody = "[
  {
    "id": 0,
    "text": "This is an example:\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model."
  }
]".data(using: .utf8)
request.setValue("application/json", forHTTPHeaderField: "Content-Type")

let task = URLSession.shared.dataTask(with: request) { data, _, error in
    if let error = error {
        print(error.localizedDescription)
        return
    }
    if let data = data {
        print(String(decoding: data, as: UTF8.self))
    }
}
task.resume()

import okhttp3.MediaType.Companion.toMediaType
import okhttp3.OkHttpClient
import okhttp3.Request
import okhttp3.RequestBody
import okhttp3.RequestBody.Companion.toRequestBody

fun main() {
    val client = OkHttpClient()
    var body: RequestBody? = null
    body = "[
  {
    "id": 0,
    "text": "This is an example:\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model."
  }
]".toRequestBody("application/json".toMediaType())

    val requestBuilder = Request.Builder()
        .url("https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality")
        .addHeader("Authorization", "Bearer YOUR_API_KEY")

    val request = requestBuilder.method("POST", body).build()

    client.newCall(request).execute().use { response ->
        println(response.body?.string())
    }
}

import 'package:http/http.dart' as http;

void main() async {
  var payload = '';
  payload = "[
  {
    "id": 0,
    "text": "This is an example:\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model."
  }
]";

  final request = http.Request(
    "POST",
    Uri.parse("https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality"),
  );
  request.headers['Authorization'] = 'Bearer YOUR_API_KEY';
  if (payload.isNotEmpty) {
    request.headers['Content-Type'] = 'application/json';
    request.body = payload;
  }

  final client = http.Client();
  final response = await client.send(request);
  print(await response.stream.bytesToString());
  client.close();
}

use reqwest::blocking::Client;
use reqwest::Method;
use std::error::Error;

fn main() -> Result<(), Box> {
    let method = Method::from_bytes(b"POST")?;
    let client = Client::new();
    let mut body = String::new();
    body = r#"[
  {
    "id": 0,
    "text": "This is an example:\nThe rise of capable Generative AI has ushered in a new era of AI-generated content bringing with it countless positive use-cases, but also potential perils in the form of widespread automated misinformation and disinformation, fake reviews, reputational damage, and so forth.\nThis has brought with it a pressing need to automatically detect human content, detect AI-generated content, and determine the boundaries between the two. Explainability and transparency are also needed in the form of attribution of AI-generated text to the underlying Large Language model."
  }
]"#.to_string();

    let mut request = client
        .request(method, "https://pr213-testing.zylalabs.com/api/5799/content+integrity+checker+api/7559/check+text+originality")
        .header("Authorization", "Bearer YOUR_API_KEY");

    request = request.body(body.clone());

    let response = request.send()?;
    println!("{}", response.text()?);
    Ok(())
}