curl --request POST \
  --url https://api.priorlabs.ai/tabpfn/predict \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "test_set_upload_id": "33333333-3333-4333-8333-333333333333",
  "fitted_train_set_id": "22222222-2222-4222-8222-222222222222",
  "task_config": {
    "task": "classification",
    "predict_params": {
      "output_type": "probas"
    }
  }
}
'

import requests

url = "https://api.priorlabs.ai/tabpfn/predict"

payload = {
    "test_set_upload_id": "33333333-3333-4333-8333-333333333333",
    "fitted_train_set_id": "22222222-2222-4222-8222-222222222222",
    "task_config": {
        "task": "classification",
        "predict_params": { "output_type": "probas" }
    }
}
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    test_set_upload_id: '33333333-3333-4333-8333-333333333333',
    fitted_train_set_id: '22222222-2222-4222-8222-222222222222',
    task_config: {task: 'classification', predict_params: {output_type: 'probas'}}
  })
};

fetch('https://api.priorlabs.ai/tabpfn/predict', 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://api.priorlabs.ai/tabpfn/predict",
  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([
    'test_set_upload_id' => '33333333-3333-4333-8333-333333333333',
    'fitted_train_set_id' => '22222222-2222-4222-8222-222222222222',
    'task_config' => [
        'task' => 'classification',
        'predict_params' => [
                'output_type' => 'probas'
        ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

$response = curl_exec($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://api.priorlabs.ai/tabpfn/predict"

	payload := strings.NewReader("{\n  \"test_set_upload_id\": \"33333333-3333-4333-8333-333333333333\",\n  \"fitted_train_set_id\": \"22222222-2222-4222-8222-222222222222\",\n  \"task_config\": {\n    \"task\": \"classification\",\n    \"predict_params\": {\n      \"output_type\": \"probas\"\n    }\n  }\n}")

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

	req.Header.Add("Authorization", "Bearer <token>")
	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://api.priorlabs.ai/tabpfn/predict")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"test_set_upload_id\": \"33333333-3333-4333-8333-333333333333\",\n  \"fitted_train_set_id\": \"22222222-2222-4222-8222-222222222222\",\n  \"task_config\": {\n    \"task\": \"classification\",\n    \"predict_params\": {\n      \"output_type\": \"probas\"\n    }\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.priorlabs.ai/tabpfn/predict")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"test_set_upload_id\": \"33333333-3333-4333-8333-333333333333\",\n  \"fitted_train_set_id\": \"22222222-2222-4222-8222-222222222222\",\n  \"task_config\": {\n    \"task\": \"classification\",\n    \"predict_params\": {\n      \"output_type\": \"probas\"\n    }\n  }\n}"

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

{
  "prediction": [
    "<unknown>"
  ],
  "metadata": {
    "test_set_num_rows": 123,
    "test_set_num_cols": 123,
    "package_version": "<string>",
    "tabpfn_config": {
      "n_estimators": 4,
      "categorical_features_indices": [
        123
      ],
      "softmax_temperature": 123,
      "average_before_softmax": true,
      "random_state": 123,
      "inference_config": {},
      "inference_precision": "autocast",
      "ignore_pretraining_limits": true,
      "n_preprocessing_jobs": 4,
      "fit_mode": "fit_preprocessors",
      "device": [
        "<string>"
      ],
      "memory_saving_mode": true,
      "model_path": "<string>",
      "balance_probabilities": true
    },
    "n_estimators": 123,
    "billing_model_version": "<string>",
    "execution_mode": "standard",
    "cache_outcome": "not_requested",
    "task": "classification",
    "classes": [
      "<string>"
    ],
    "top_k": 123
  },
  "timings": {
    "test_set_transform_queue_wait_s": 123,
    "test_set_transform_s": 123,
    "predict_queue_wait_s": 123,
    "predict_s": 123
  },
  "fitted_test_set_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a"
}

{
  "message": "<string>",
  "error_code": "NOT_FOUND",
  "trace_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a"
}

{
  "message": "<string>",
  "fitted_test_set_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "error_code": "GENERAL_ERROR",
  "trace_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a"
}

{
  "message": "<string>",
  "fitted_test_set_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
  "error_code": "GENERAL_ERROR",
  "trace_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a"
}