Create Prediction

cURL

curl --request POST \
  --url https://api.cradl.ai/{basePath}/predictions \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: <content-type>' \
  --data '
{
  "modelId": "<string>",
  "documentId": "<string>",
  "metadata": {},
  "trainingId": "<string>",
  "postprocessConfig": {
    "strategy": "BEST_FIRST"
  },
  "description": "<string>",
  "agentRunId": "<string>",
  "async": true,
  "preprocessConfig": {
    "trimMargins": true,
    "useGhostScript": true,
    "startPage": 123,
    "pages": [
      123
    ],
    "usePoppler": true,
    "padToFit": true,
    "useTextDetection": true,
    "maxPages": 50,
    "deskewImage": true,
    "autoRotate": true
  },
  "maxPages": 50,
  "name": "<string>",
  "autoRotate": true
}
'

import requests

url = "https://api.cradl.ai/{basePath}/predictions"

payload = {
    "modelId": "<string>",
    "documentId": "<string>",
    "metadata": {},
    "trainingId": "<string>",
    "postprocessConfig": { "strategy": "BEST_FIRST" },
    "description": "<string>",
    "agentRunId": "<string>",
    "async": True,
    "preprocessConfig": {
        "trimMargins": True,
        "useGhostScript": True,
        "startPage": 123,
        "pages": [123],
        "usePoppler": True,
        "padToFit": True,
        "useTextDetection": True,
        "maxPages": 50,
        "deskewImage": True,
        "autoRotate": True
    },
    "maxPages": 50,
    "name": "<string>",
    "autoRotate": True
}
headers = {
    "Content-Type": "<content-type>",
    "Authorization": "Bearer <token>"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': '<content-type>', Authorization: 'Bearer <token>'},
  body: JSON.stringify({
    modelId: '<string>',
    documentId: '<string>',
    metadata: {},
    trainingId: '<string>',
    postprocessConfig: {strategy: 'BEST_FIRST'},
    description: '<string>',
    agentRunId: '<string>',
    async: true,
    preprocessConfig: {
      trimMargins: true,
      useGhostScript: true,
      startPage: 123,
      pages: [123],
      usePoppler: true,
      padToFit: true,
      useTextDetection: true,
      maxPages: 50,
      deskewImage: true,
      autoRotate: true
    },
    maxPages: 50,
    name: '<string>',
    autoRotate: true
  })
};

fetch('https://api.cradl.ai/{basePath}/predictions', 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.cradl.ai/{basePath}/predictions",
  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([
    'modelId' => '<string>',
    'documentId' => '<string>',
    'metadata' => [
        
    ],
    'trainingId' => '<string>',
    'postprocessConfig' => [
        'strategy' => 'BEST_FIRST'
    ],
    'description' => '<string>',
    'agentRunId' => '<string>',
    'async' => true,
    'preprocessConfig' => [
        'trimMargins' => true,
        'useGhostScript' => true,
        'startPage' => 123,
        'pages' => [
                123
        ],
        'usePoppler' => true,
        'padToFit' => true,
        'useTextDetection' => true,
        'maxPages' => 50,
        'deskewImage' => true,
        'autoRotate' => true
    ],
    'maxPages' => 50,
    'name' => '<string>',
    'autoRotate' => true
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: <content-type>"
  ],
]);

$response = curl_exec($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.cradl.ai/{basePath}/predictions"

	payload := strings.NewReader("{\n  \"modelId\": \"<string>\",\n  \"documentId\": \"<string>\",\n  \"metadata\": {},\n  \"trainingId\": \"<string>\",\n  \"postprocessConfig\": {\n    \"strategy\": \"BEST_FIRST\"\n  },\n  \"description\": \"<string>\",\n  \"agentRunId\": \"<string>\",\n  \"async\": true,\n  \"preprocessConfig\": {\n    \"trimMargins\": true,\n    \"useGhostScript\": true,\n    \"startPage\": 123,\n    \"pages\": [\n      123\n    ],\n    \"usePoppler\": true,\n    \"padToFit\": true,\n    \"useTextDetection\": true,\n    \"maxPages\": 50,\n    \"deskewImage\": true,\n    \"autoRotate\": true\n  },\n  \"maxPages\": 50,\n  \"name\": \"<string>\",\n  \"autoRotate\": true\n}")

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

	req.Header.Add("Content-Type", "<content-type>")
	req.Header.Add("Authorization", "Bearer <token>")

	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.cradl.ai/{basePath}/predictions")
  .header("Content-Type", "<content-type>")
  .header("Authorization", "Bearer <token>")
  .body("{\n  \"modelId\": \"<string>\",\n  \"documentId\": \"<string>\",\n  \"metadata\": {},\n  \"trainingId\": \"<string>\",\n  \"postprocessConfig\": {\n    \"strategy\": \"BEST_FIRST\"\n  },\n  \"description\": \"<string>\",\n  \"agentRunId\": \"<string>\",\n  \"async\": true,\n  \"preprocessConfig\": {\n    \"trimMargins\": true,\n    \"useGhostScript\": true,\n    \"startPage\": 123,\n    \"pages\": [\n      123\n    ],\n    \"usePoppler\": true,\n    \"padToFit\": true,\n    \"useTextDetection\": true,\n    \"maxPages\": 50,\n    \"deskewImage\": true,\n    \"autoRotate\": true\n  },\n  \"maxPages\": 50,\n  \"name\": \"<string>\",\n  \"autoRotate\": true\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.cradl.ai/{basePath}/predictions")

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

request = Net::HTTP::Post.new(url)
request["Content-Type"] = '<content-type>'
request["Authorization"] = 'Bearer <token>'
request.body = "{\n  \"modelId\": \"<string>\",\n  \"documentId\": \"<string>\",\n  \"metadata\": {},\n  \"trainingId\": \"<string>\",\n  \"postprocessConfig\": {\n    \"strategy\": \"BEST_FIRST\"\n  },\n  \"description\": \"<string>\",\n  \"agentRunId\": \"<string>\",\n  \"async\": true,\n  \"preprocessConfig\": {\n    \"trimMargins\": true,\n    \"useGhostScript\": true,\n    \"startPage\": 123,\n    \"pages\": [\n      123\n    ],\n    \"usePoppler\": true,\n    \"padToFit\": true,\n    \"useTextDetection\": true,\n    \"maxPages\": 50,\n    \"deskewImage\": true,\n    \"autoRotate\": true\n  },\n  \"maxPages\": 50,\n  \"name\": \"<string>\",\n  \"autoRotate\": true\n}"

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

{
  "updatedTime": "<string>",
  "metadata": {},
  "updatedBy": "<string>",
  "modelId": "<string>",
  "description": "<string>",
  "predictions": [
    {
      "confidence": 0.5,
      "label": "<string>",
      "value": "<string>",
      "validators": "<array>",
      "warnings": [
        "<string>"
      ],
      "rotation": 123,
      "formatters": "<array>",
      "rawValue": "<string>",
      "attentionMap": [
        [
          123
        ]
      ],
      "location": [
        0.5
      ],
      "page": 123,
      "errors": [
        "<string>"
      ]
    }
  ],
  "createdBy": "<string>",
  "name": "<string>",
  "createdTime": "<string>",
  "documentId": "<string>",
  "predictionId": "<string>",
  "trainingId": "<string>",
  "nextPage": 123,
  "postprocessConfig": {
    "strategy": "BEST_FIRST"
  },
  "warnings": [
    "<string>"
  ],
  "inferenceTime": 1,
  "error": "<string>",
  "agentRunId": "<string>",
  "preprocessConfig": {
    "trimMargins": true,
    "useGhostScript": true,
    "startPage": 123,
    "pages": [
      123
    ],
    "usePoppler": true,
    "padToFit": true,
    "useTextDetection": true,
    "maxPages": 50,
    "deskewImage": true,
    "autoRotate": true
  },
  "fileUrl": "<string>",
  "errors": [
    "<string>"
  ]
}

{
  "message": "<string>"
}

{
  "message": "<string>"
}

{
  "message": "<string>"
}

{
  "message": "<string>"
}

{
  "message": "<string>"
}

POST

predictions

cURL

curl --request POST \
  --url https://api.cradl.ai/{basePath}/predictions \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: <content-type>' \
  --data '
{
  "modelId": "<string>",
  "documentId": "<string>",
  "metadata": {},
  "trainingId": "<string>",
  "postprocessConfig": {
    "strategy": "BEST_FIRST"
  },
  "description": "<string>",
  "agentRunId": "<string>",
  "async": true,
  "preprocessConfig": {
    "trimMargins": true,
    "useGhostScript": true,
    "startPage": 123,
    "pages": [
      123
    ],
    "usePoppler": true,
    "padToFit": true,
    "useTextDetection": true,
    "maxPages": 50,
    "deskewImage": true,
    "autoRotate": true
  },
  "maxPages": 50,
  "name": "<string>",
  "autoRotate": true
}
'

import requests

url = "https://api.cradl.ai/{basePath}/predictions"

payload = {
    "modelId": "<string>",
    "documentId": "<string>",
    "metadata": {},
    "trainingId": "<string>",
    "postprocessConfig": { "strategy": "BEST_FIRST" },
    "description": "<string>",
    "agentRunId": "<string>",
    "async": True,
    "preprocessConfig": {
        "trimMargins": True,
        "useGhostScript": True,
        "startPage": 123,
        "pages": [123],
        "usePoppler": True,
        "padToFit": True,
        "useTextDetection": True,
        "maxPages": 50,
        "deskewImage": True,
        "autoRotate": True
    },
    "maxPages": 50,
    "name": "<string>",
    "autoRotate": True
}
headers = {
    "Content-Type": "<content-type>",
    "Authorization": "Bearer <token>"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {'Content-Type': '<content-type>', Authorization: 'Bearer <token>'},
  body: JSON.stringify({
    modelId: '<string>',
    documentId: '<string>',
    metadata: {},
    trainingId: '<string>',
    postprocessConfig: {strategy: 'BEST_FIRST'},
    description: '<string>',
    agentRunId: '<string>',
    async: true,
    preprocessConfig: {
      trimMargins: true,
      useGhostScript: true,
      startPage: 123,
      pages: [123],
      usePoppler: true,
      padToFit: true,
      useTextDetection: true,
      maxPages: 50,
      deskewImage: true,
      autoRotate: true
    },
    maxPages: 50,
    name: '<string>',
    autoRotate: true
  })
};

fetch('https://api.cradl.ai/{basePath}/predictions', 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.cradl.ai/{basePath}/predictions",
  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([
    'modelId' => '<string>',
    'documentId' => '<string>',
    'metadata' => [
        
    ],
    'trainingId' => '<string>',
    'postprocessConfig' => [
        'strategy' => 'BEST_FIRST'
    ],
    'description' => '<string>',
    'agentRunId' => '<string>',
    'async' => true,
    'preprocessConfig' => [
        'trimMargins' => true,
        'useGhostScript' => true,
        'startPage' => 123,
        'pages' => [
                123
        ],
        'usePoppler' => true,
        'padToFit' => true,
        'useTextDetection' => true,
        'maxPages' => 50,
        'deskewImage' => true,
        'autoRotate' => true
    ],
    'maxPages' => 50,
    'name' => '<string>',
    'autoRotate' => true
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: <content-type>"
  ],
]);

$response = curl_exec($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.cradl.ai/{basePath}/predictions"

	payload := strings.NewReader("{\n  \"modelId\": \"<string>\",\n  \"documentId\": \"<string>\",\n  \"metadata\": {},\n  \"trainingId\": \"<string>\",\n  \"postprocessConfig\": {\n    \"strategy\": \"BEST_FIRST\"\n  },\n  \"description\": \"<string>\",\n  \"agentRunId\": \"<string>\",\n  \"async\": true,\n  \"preprocessConfig\": {\n    \"trimMargins\": true,\n    \"useGhostScript\": true,\n    \"startPage\": 123,\n    \"pages\": [\n      123\n    ],\n    \"usePoppler\": true,\n    \"padToFit\": true,\n    \"useTextDetection\": true,\n    \"maxPages\": 50,\n    \"deskewImage\": true,\n    \"autoRotate\": true\n  },\n  \"maxPages\": 50,\n  \"name\": \"<string>\",\n  \"autoRotate\": true\n}")

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

	req.Header.Add("Content-Type", "<content-type>")
	req.Header.Add("Authorization", "Bearer <token>")

	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.cradl.ai/{basePath}/predictions")
  .header("Content-Type", "<content-type>")
  .header("Authorization", "Bearer <token>")
  .body("{\n  \"modelId\": \"<string>\",\n  \"documentId\": \"<string>\",\n  \"metadata\": {},\n  \"trainingId\": \"<string>\",\n  \"postprocessConfig\": {\n    \"strategy\": \"BEST_FIRST\"\n  },\n  \"description\": \"<string>\",\n  \"agentRunId\": \"<string>\",\n  \"async\": true,\n  \"preprocessConfig\": {\n    \"trimMargins\": true,\n    \"useGhostScript\": true,\n    \"startPage\": 123,\n    \"pages\": [\n      123\n    ],\n    \"usePoppler\": true,\n    \"padToFit\": true,\n    \"useTextDetection\": true,\n    \"maxPages\": 50,\n    \"deskewImage\": true,\n    \"autoRotate\": true\n  },\n  \"maxPages\": 50,\n  \"name\": \"<string>\",\n  \"autoRotate\": true\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.cradl.ai/{basePath}/predictions")

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

request = Net::HTTP::Post.new(url)
request["Content-Type"] = '<content-type>'
request["Authorization"] = 'Bearer <token>'
request.body = "{\n  \"modelId\": \"<string>\",\n  \"documentId\": \"<string>\",\n  \"metadata\": {},\n  \"trainingId\": \"<string>\",\n  \"postprocessConfig\": {\n    \"strategy\": \"BEST_FIRST\"\n  },\n  \"description\": \"<string>\",\n  \"agentRunId\": \"<string>\",\n  \"async\": true,\n  \"preprocessConfig\": {\n    \"trimMargins\": true,\n    \"useGhostScript\": true,\n    \"startPage\": 123,\n    \"pages\": [\n      123\n    ],\n    \"usePoppler\": true,\n    \"padToFit\": true,\n    \"useTextDetection\": true,\n    \"maxPages\": 50,\n    \"deskewImage\": true,\n    \"autoRotate\": true\n  },\n  \"maxPages\": 50,\n  \"name\": \"<string>\",\n  \"autoRotate\": true\n}"

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

{
  "updatedTime": "<string>",
  "metadata": {},
  "updatedBy": "<string>",
  "modelId": "<string>",
  "description": "<string>",
  "predictions": [
    {
      "confidence": 0.5,
      "label": "<string>",
      "value": "<string>",
      "validators": "<array>",
      "warnings": [
        "<string>"
      ],
      "rotation": 123,
      "formatters": "<array>",
      "rawValue": "<string>",
      "attentionMap": [
        [
          123
        ]
      ],
      "location": [
        0.5
      ],
      "page": 123,
      "errors": [
        "<string>"
      ]
    }
  ],
  "createdBy": "<string>",
  "name": "<string>",
  "createdTime": "<string>",
  "documentId": "<string>",
  "predictionId": "<string>",
  "trainingId": "<string>",
  "nextPage": 123,
  "postprocessConfig": {
    "strategy": "BEST_FIRST"
  },
  "warnings": [
    "<string>"
  ],
  "inferenceTime": 1,
  "error": "<string>",
  "agentRunId": "<string>",
  "preprocessConfig": {
    "trimMargins": true,
    "useGhostScript": true,
    "startPage": 123,
    "pages": [
      123
    ],
    "usePoppler": true,
    "padToFit": true,
    "useTextDetection": true,
    "maxPages": 50,
    "deskewImage": true,
    "autoRotate": true
  },
  "fileUrl": "<string>",
  "errors": [
    "<string>"
  ]
}

{
  "message": "<string>"
}

{
  "message": "<string>"
}

{
  "message": "<string>"
}

{
  "message": "<string>"
}

{
  "message": "<string>"
}

Authorizations

Authorization

string

header

required

The access token received from the authorization server in the OAuth 2.0 flow.

Headers

Content-Type

string

required

Body

application/json

modelId

string

required

Pattern: ^(|(cradl|las):organization:[a-z0-9-_]+/)(cradl|las):model:[a-z0-9-_]+$

documentId

string

required

Pattern: ^(cradl|las):document:[a-f0-9]{32}$

metadata

object

trainingId

string

Pattern: ^las:model-training:[a-z0-9-_]+$

postprocessConfig

object

Option 1
Option 2

Show child attributes

rotation

enum<integer>

Available options:

0,

90,

180,

270

description

string | null

Maximum string length: 4096

agentRunId

string

Pattern: ^((cradl|las):organization:[a-z0-9-_]+/)?cradl:agent:[a-z0-9-_]+/cradl:run:[a-z0-9-_]+$

async

boolean

preprocessConfig

object

Show child attributes

maxPages

integer

Required range: 1 <= x <= 100

name

string | null

Maximum string length: 4096

autoRotate

boolean

imageQuality

enum<string>

Available options:

LOW,

HIGH

Response

200 response

updatedTime

string | null

required

Pattern: ^[0-9]{4}-?[0-9]{2}-?[0-9]{2}( |T)?[0-9]{2}:?[0-9]{2}:?[0-9]{2}(.[0-9]{1,6})?(Z|[+][0-9]{2}(:|)[0-9]{2})$

metadata

object | null

required

updatedBy

string | null

required

Maximum string length: 4096

modelId

string

required

Pattern: ^(|(cradl|las):organization:[a-z0-9-_]+/)(cradl|las):model:[a-z0-9-_]+$

description

string | null

required

Maximum string length: 4096

predictions

required

Option 1
Option 2

Show child attributes

createdBy

string

required

Maximum string length: 4096

name

string | null

required

Maximum string length: 4096

createdTime

string

required

Pattern: ^[0-9]{4}-?[0-9]{2}-?[0-9]{2}( |T)?[0-9]{2}:?[0-9]{2}:?[0-9]{2}(.[0-9]{1,6})?(Z|[+][0-9]{2}(:|)[0-9]{2})$

documentId

string

required

Pattern: ^(cradl|las):document:[a-f0-9]{32}$

predictionId

string

required

Pattern: ^(cradl|las):prediction:[a-f0-9]{32}$

trainingId

string | null

Pattern: ^las:model-training:[a-z0-9-_]+$

integer | null

postprocessConfig

object

Option 1
Option 2

Show child attributes

warnings

string[] | null

inferenceTime

number | null

Required range: x >= 0

error

string | null

Maximum string length: 4096

agentRunId

string | null

Pattern: ^((cradl|las):organization:[a-z0-9-_]+/)?cradl:agent:[a-z0-9-_]+/cradl:run:[a-z0-9-_]+$

preprocessConfig

object | null

Show child attributes

fileUrl

string | null

Pattern: ^http://localhost.*|^https://.*

errors

string[] | null

status

enum<string>

Available options:

pending,

succeeded,

failed

List Predictions Get Prediction