🔮Predictions

These methods are necessary in order to use some of the endpoints in this section.

to obtain a valid token use the following method: Authentication
To get the id of your projects you must go to the following method: List projects.
To get the models of your project you must go to the following method: Project information.

List Inputs for prediction:

GET https://api.arkangel.ai/api/experiments/io/project/{{PROJECT_ID}}/model/{{MODEL_ID}}

Path Parameters

Name

Type

Description

Headers

Name

Type

Description

{
    "status": 200,
    "result": {
        "id": "45a129d6-f421-44c2-8950-cde9fe82bffg",
        "status": "done",
        "createdAt": "2023-04-19T16:15:00.216Z",
        "updatedAt": "2023-04-19T16:15:00.216Z",
        "experimentsIO": [
            {
                "id": "5b255639-d3a8-418f-824d-92cc4627e690",
                "io": true,
                "alias": null,
                "description": null,
                "name": "id",
                "dataType": "number_int",
                "range": [
                    "3",
                    "399"
                ],
                "createdAt": "2023-04-19T17:12:24.992Z",
                "updatedAt": "2023-04-19T17:12:24.992Z"
            },
            {
                "id": "bc4bc70a-a558-47d5-bcfb-9c3e46322e24",
                "io": true,
                "alias": null,
                "description": null,
                "name": "rc",
                "dataType": "number_float",
                "range": [
                    "2.1",
                    "8"
                ],
                "createdAt": "2023-04-19T17:12:25.003Z",
                "updatedAt": "2023-04-19T17:12:25.003Z"
            },
            {
                "id": "ddc1868a-2c0c-44fb-abb2-47b1c75dbd92",
                "io": true,
                "alias": null,
                "description": null,
                "name": "rbc",
                "dataType": "categorical",
                "range": [
                    "normal",
                    "abnormal"
                ],
                "createdAt": "2023-04-19T17:12:25.003Z",
                "updatedAt": "2023-04-19T17:12:25.003Z"
            }
        ]
    }
}

Example request

import requests

url = "https://apidev.arkangel.ai/api/experiments/io/project/{{PROJECT_ID}}/model/{{MODEL_ID}}"

payload = ""
headers = {
  'Authorization': 'Bearer {{TOKEN}}'
}

response = requests.request("GET", url, headers=headers, data=payload)

print(response.text)

const token = "{{TOKEN}}"
const projectId = "{{PROJECT_ID}}"
const modelId = "{{MODEL_ID}}"

const myHeaders = new Headers();
myHeaders.append("Authorization", `Bearer ${token}`);

const raw = "";

const requestOptions = {
  method: 'GET',
  headers: myHeaders,
  body: raw,
  redirect: 'follow'
};

fetch(`https://api.arkangel.ai/api/experiments/io/project/${projectId}/model/${modelId}`, requestOptions)
  .then(response => response.json())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

curl --location --globoff 'https://apidev.arkangel.ai/api/experiments/io/project/{{PROJECT_ID}}/model/{{MODEL_ID}}' \
--header 'Authorization: Bearer {{TOKEN}}' \
--data ''

Create prediction:

Create a prediction

POST https://api.arkangel.ai/api/inference

The body must be in JSON format

Headers

Name

Type

Description

Request Body

Name

Type

Description

{
    "id": "8ee23c6a-441e-4d8c-9110-3ab2fefc6b79",
    "comment": null,
    "annotated": null,
    "clientId": null,
    "startInference": "2023-08-31T15:19:46.000Z",
    "endInference": "2023-08-31T15:19:52.000Z",
    "inputs": [
        {
            "name": "rc",
            "value": "4.2",
            "inferenceInputId": "b6c12361-d462-4be9-846f-ca73d8f7ffbf"
        },
        {
            "name": "rbc",
            "value": "normal",
            "inferenceInputId": "463b4eb4-d892-46f3-9c09-aa86d8a2eb20"
        },
        {
            "name": "pc",
            "value": "abnormal",
            "inferenceInputId": "b2100bc0-2e50-4efa-926a-6dc5979098cf"
        }
    ],
    "labels": [
        {
            "id": "fbe86179-c98c-493c-a8ec-b7f21d7f3b3f",
            "name": "classification",
            "classes": [
                {
                    "explainabilities": [
                        {
                            "id": "0c463083-23a1-4748-a30c-f8abea40bffc",
                            "inferenceInputId": "b6c12361-d462-4be9-846f-ca73d8f7ffbf",
                            "value": "0.1166613112549300"
                        },
                        {
                            "id": "96d46890-8d98-41f9-a17a-35162ea91d62",
                            "inferenceInputId": "463b4eb4-d892-46f3-9c09-aa86d8a2eb20",
                            "value": "0.2366151378308200"
                        },
                        {
                            "id": "33a30934-b45c-4fa3-88ed-3469387e8287",
                            "inferenceInputId": "b2100bc0-2e50-4efa-926a-6dc5979098cf",
                            "value": "0.0025483931053800"
                        }
                    ],
                    "result": "0.72316533327103",
                    "labelStr": "ckd"
                },
                {
                    "explainabilities": [
                        {
                            "id": "c4a91a27-5f58-497c-b3be-cdfb531da359",
                            "inferenceInputId": "b6c12361-d462-4be9-846f-ca73d8f7ffbf",
                            "value": "0.0555980612992000"
                        },
                        {
                            "id": "01ee0681-7fb7-45c2-b322-59d1302baa6d",
                            "inferenceInputId": "463b4eb4-d892-46f3-9c09-aa86d8a2eb20",
                            "value": "-0.2253670386386000"
                        },
                        {
                            "id": "148935bc-a4ad-43e0-9bb0-9cd0473f8368",
                            "inferenceInputId": "b2100bc0-2e50-4efa-926a-6dc5979098cf",
                            "value": "-0.1166613119150600"
                        }
                    ],
                    "result": "0.27683460712433",
                    "labelStr": "notckd"
                }
            ]
        }
    ],
    "annotations": []
}

{
    "id": "9d7870ng-c8fb-4934-9d37-0a4a4d12b577",
    "comment": null,
    "annotated": null,
    "clientId": null,
    "startInference": "2023-08-31T15:31:52.000Z",
    "endInference": "2023-08-31T15:31:53.000Z",
    "inputs": [
        {
            "name": "Year",
            "value": "2004",
            "inferenceInputId": "a6be6219-c7d8-4053-ac97-d29ee633a9bb"
        },
        {
            "name": "AdultMortality",
            "value": "338.5",
            "inferenceInputId": "70c94d78-d677-4db8-ba39-87eb71be2ff4"
        },
        {
            "name": "infantdeaths",
            "value": "653.6",
            "inferenceInputId": "80da716f-78c0-4b22-814d-1fed6972ad6f"
        }
    ],
    "labels": [
        {
            "id": "3c04d653-61bf-436b-aadc-6b9e25ae257f",
            "name": "example_name",
            "result": "0.67905431985855",
            "explainabilities": [
                {
                    "id": "cd9d4315-90d2-4002-9387-942df9334c39",
                    "inferenceInputId": "a6be6219-c7d8-4053-ac97-d29ee633a9bb",
                    "value": "0.0080298891355800"
                },
                {
                    "id": "f100c6b1-3bdf-4c37-863b-43e2a8175b07",
                    "inferenceInputId": "70c94d78-d677-4db8-ba39-87eb71be2ff4",
                    "value": "0.0206342157852200"
                },
                {
                    "id": "614d41d9-f452-4363-923f-564fdd95a9d8",
                    "inferenceInputId": "80da716f-78c0-4b22-814d-1fed6972ad6f",
                    "value": "0.0384177504005600"
                }
            ]
        }
    ],
    "annotations": []
}

Example request for tabulated classification data:

import requests
import json

url = "https://api.arkangel.ai//api/inference"

predict_dic = {}
predict_dic['projectId'] = "{{PROJECT_ID}}"
predict_dic['modelId'] = "{{MODEL_ID}}"
# The number of inputs are given by the number of columns or variables
# used to train the experiment.
# Value can be any number, or if the column is categorical, variable
# must be a string that the experiment has already seen.
# For example:
# the experiment has been trained with the inputs ['red','green','blue']
# then value must be 'red', 'green', or 'blue' it can not be 'yellow'
predict_dic['inputs'] =[
    {
      "id": "870fc9cb-6c8e-4d7c-9e75-11631a67a137",
      "input": 2015
    },
    {
      "id": "27a9d20a-9c89-42b6-a91c-99ab1c315850",
      "name": "AdultMortality",
      "input": 263
    }
  ]

# Transform dictionary to json response
payload = json.dumps(predict_dic, indent=4)

headers = {
  'Authorization': 'Bearer {{TOKEN}}',
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

const token = "{{TOKEN}}";
const projectId = "{{PROJECT_ID}}";
const modelId = "{{MODEL_ID}}";

const myHeaders = new Headers();
myHeaders.append("Authorization", `Bearer ${token}`);
myHeaders.append("Content-Type", "application/json");

const raw = JSON.stringify({
  "projectId": projectId,
  "modelId": modelId,
  "inputs": [
    {
      "id": "27a9d20a-9c89-42b6-a91c-99ab1c315850",
      "input": 2015
    },
    {
      "id": "27a9d20a-9c89-42b6-a91c-99ab1c315851",
      "input": 263
    }
   ]
});

const requestOptions = {
  method: 'POST',
  headers: myHeaders,
  body: raw,
  redirect: 'follow'
};

fetch("https://api.arkangel.ai/api/inference", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

curl --location --request POST 'https://api.arkangel.ai/api/inference' \
--header 'Authorization: Bearer {{TOKEN}}' \
--header 'Content-Type: application/json' \
--data-raw '{
  "projectId": "{{PROJECT_ID}}",
  "modelId": "{{MODEL_ID}}",
  "inputs": [
    {
      "id": "27a9d20a-9c89-42b6-a91c-99ab1c315850",
      "input": 2015
    },
    {
      "id": "27a9d20a-9c89-42b6-a91c-99ab1c315851",
      "input": 263
    }
  ] 
}'

Example request for image data classification

import requests
import json


# For this method it is not necessary to use List Inputs method
# It automatically obtains the image ID since only one is needed.
url = "https://api.arkangel.ai/api/experiments/io/{{MODEL_ID}}"

payload = ""
headers = {
  'Authorization': 'Bearer {{TOKEN}}'
}

img_id = requests.request("GET", url, headers=headers, data=payload)
img_id = json.loads(img_id.text)
img_id["result"]["experimentsIO"][0]["id"]

url = "https://api.arkangel.ai/api/inference"

# The image that is going to be used for the prediction must 
# Be in base 64 format
payload = json.dumps({
  "userId": "{{USER_ID}}",
  "projectId": "{{PROJECT_ID}}",
  "modelId": "{{MODEL_ID}}",
  "inputs": [
    {
      "id": img_index,
      "input": "{{IMAGE_TRANSFORMED_INTO_BASE64}}"
    }
  ]
})
headers = {
  'Authorization': 'Bearer {{TOKEN}}',
  'Content-Type': 'application/json'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

const token = "{{TOKEN}}";
const projectId = "{{PROJECT_ID}}";
const modelId = "{{MODEL_ID}}";

const myHeaders = new Headers();
myHeaders.append("Authorization", `Bearer ${token}`);
myHeaders.append("Content-Type", "application/json");

const raw = JSON.stringify({
  "projectId": projectId,
  "modelId": modelId,
  "inputs": [
    // The images have to be converted to Base64.
    {
      "id": "27a9d20a-9c89-42b6-a91c-99ab1c315852",
      "input": "image in base64"
    }
   ]
});

const requestOptions = {
  method: 'POST',
  headers: myHeaders,
  body: raw,
  redirect: 'follow'
};

fetch("https://api.arkangel.ai/api/inference", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

curl --location --request POST 'https://api.arkangel.ai/api/inference' \
--header 'Authorization: Bearer {{TOKEN}}' \
--header 'Content-Type: application/json' \
--data-raw '{
  "projectId": "{{PROJECT_ID}}",
  "modelId": "{{MODEL_ID}}",
  "inputs": [
    {
      "id": "27a9d20a-9c89-42b6-a91c-99ab1c315852",
      "input": "image in base64"
    }
  ] 
}'

Create multiple predictions:

POST https://api.arkangel.ai/api/inference/batch

This method will allow you to make more than one prediction at a time, you will have to send a .csv file, it will respond with an equal file, which contains the errors of the predictions if it found any error, if the file is empty no inferences with typing errors or similar were found. The correct predictions will be made and when they are complete, the file with the results will be sent to the email, and the results can also be obtained through the endpoint of list predictions.

Headers

Name

Type

Description

Request Body

Name

Type

Description

231B

example_file.csv

Example of use for example_file.csv

clientId

27a9d20a-9c89-42b6

9c89-42b6-27a9d20a

42b6-27a9d20a-9c89

The CSV file should have a maximum of 1,000,000 cells.

Example request

import requests

url = "https://apidev.arkangel.ai/api/inference/batch"

payload = {
  'projectId': '{{PROJECT_ID}}',
  'modelId': '{{MODEL_ID}}'
}
files=[
  ('batch',('example_file.csv', open('/home/user/Downloads/example_file.csv','rb'), 'text/csv'))
]
headers = {
  'Authorization': 'Bearer {{TOKEN}}'
}

response = requests.request("POST", url, headers=headers, data=payload, files=files)

print(response.text)

const token = "{{TOKEN}}";
const projectId = "{{PROJECT_ID}}";
const modelId = "{{MODEL_ID}}";

const myHeaders = new Headers();
myHeaders.append("Authorization", `Bearer ${token}`);

const formdata = new FormData();
formdata.append("projectId", projectId);
formdata.append("modelId", modelId);
formdata.append("batch", file); // "example_file.csv"

const requestOptions = {
  method: 'POST',
  headers: myHeaders,
  body: formdata,
  redirect: 'follow'
};

fetch("https://apidev.arkangel.ai/api/inference/batch", requestOptions)
  .then(response => response.blob({ type: "text/csv;charset=utf-8;" }))
  .then(result=> console.log(result))
  .catch(error => console.log('error', error));

curl --location 'https://apidev.arkangel.ai/api/inference/batch' \
--header 'Authorization: Bearer {{TOKEN}}' \
--form 'projectId="{{PROJECT_ID}}"' \
--form 'modelId="{{MODEL_ID}}"' \
--form 'batch=@"/home/luis/Downloads/example_file.csv"'

List predictions:

List a predictions

GET https://api.arkangel.ai/api/inference/project/{{PROJECT_ID}}/model/{{MODEL_ID}}

Path Parameters

Name

Type

Description

Query Parameters

Name

Type

Description

{
    "total": 3,
    "data": [
        {
            "id": "8ee23c6a-881e-4d8c-9110-3ab2fefc6b88",
            "prediction": "ckd",
            "result": "0.72316533327103",
            "modelId": "c3c7ca91-c3f1-4982-a54c-25253ab36c78",
            "comment": null,
            "clientId": null,
            "createdAt": "2023-08-31T15:19:52.151Z",
            "updatedAt": "2023-08-31T15:19:52.151Z"
        },
        {
            "id": "113d2c1f-79fe-4665-b02e-4313e1b9c896",
            "prediction": "ckd",
            "result": "0.75037169456482",
            "modelId": "c3c7ca91-c3f1-4982-a54c-25253ab36c78",
            "comment": null,
            "clientId": null,
            "createdAt": "2023-08-29T16:12:48.223Z",
            "updatedAt": "2023-08-29T16:12:48.223Z"
        },
        {
            "id": "5bd0dac0-61c3-46a3-b5eb-f1aeb484cb35",
            "prediction": "notckd",
            "result": "0.67639052867889",
            "modelId": "c3c7ca91-c3f1-4982-a54c-25253ab36c78",
            "comment": null,
            "clientId": null,
            "createdAt": "2023-05-17T20:47:57.489Z",
            "updatedAt": "2023-05-17T20:47:57.489Z"
        }
    ]
}

Example request

import requests

# Replace this with the corresponding information
PROJECT_ID = '{{PROJECT_ID}}'
MODEL_ID = '{{MODEL_ID}}'
LIMIT = '{{LIMIT}}'
OFFSET = '{{OFFSET}}'

url = f"https://api.arkangel.ai/api/inference/project/{PROJECT_ID}/model/{MODEL_ID}?limit={LIMIT}&offset={OFFSET}"

payload={}
# Obtain user's web token
headers = {
  'Authorization': 'Bearer {{TOKEN}}'
}

# Configure the information created before of the project and obtain response
response = requests.request("GET", url, headers=headers, data=payload)

print(response.text)

const token = "{{TOKEN}}";
const projectId = "{{PROJECT_ID}}";
const modelId = "{{MODEL_ID}}";
const limit = 10;
const offset = 0;

const myHeaders = new Headers();
myHeaders.append("Authorization", `Bearer ${token}`);

const requestOptions = {
  method: 'GET',
  headers: myHeaders,
  redirect: 'follow'
};

fetch(`https://api.arkangel.ai/api/inference/project/${projectId}/model/${modelId}?limit=${limit}&offset=${offset}`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

curl --location --request GET 'https://api.arkangel.ai/api/inference/project/{{PROJECT_ID}}/model/{{MODEL_ID}}?limit={{LIMIT}}&offset={{OFFSET}}' \
--header 'Authorization: Bearer {{TOKEN}}'

List Prediction:

GET https://api.arkangel.ai/api/inference/{{INFERENCE_ID}}

Path Parameters

Name

Type

Description

{
    "status": 200,
    "result": {
        "id": "{{INFERENCE_ID}}",
        "startInference": "2022-10-12T22:53:02.000Z",
        "endInference": "2022-10-12T22:53:05.000Z",
        "inputs": [
            {
                "name": "BMI",
                "value": "16.6",
                "inferenceInputId": "ca7581c2-5bc0-42de-9ee1-b9550f78cace"
            },
            {
                "name": "Smoking",
                "value": "1",
                "inferenceInputId": "605f426d-dde2-4e40-903c-b1a905ab4d30"
            }
        ],
        "labels": [
            {
                "id": "870fcf8e-07d7-4932-9528-220a144bbe2b",
                "name": "Target",
                "classes": [
                    {
                        "explainabilities": [
                            {
                                "id": "8201cc2a-8f93-4f16-90eb-a1bff5058208",
                                "inferenceInputId": "ca7581c2-5bc0-42de-9ee1-b9550f78cace",
                                "value": "0.0104488377621390"
                            },
                            {
                                "id": "a6c01a95-08c2-4e78-aeeb-54294807a9d6",
                                "inferenceInputId": "605f426d-dde2-4e40-903c-b1a905ab4d30",
                                "value": "0.0052993741086582"
                            }

                           
                        ],
                        "result": "0.00000000001003",
                        "labelStr": "Yes"
                    },
                    {
                        "explainabilities": [
                            {
                                "id": "a7035d95-b7f2-42c9-b659-0d51d340829b",
                                "inferenceInputId": "ca7581c2-5bc0-42de-9ee1-b9550f78cace",
                                "value": "-0.0107978574070894"
                            },
                            {
                                "id": "8032c322-7be6-48d1-a7ba-888db1c0c51f",
                                "inferenceInputId": "605f426d-dde2-4e40-903c-b1a905ab4d30",
                                "value": "-0.0053141740812033"
                            }      
                        ],
                        "result": "1.00000000000000",
                        "labelStr": "No"
                    }
                ]
            }
        ]
    }
}

{
    "id": "{{INFERENCE_ID}}",
    "startInference": "2022-12-16T21:49:36.000Z",
    "endInference": "2022-12-16T21:49:37.000Z",
    "inputs": [
        {
            "name": "Year",
            "value": "2010",
            "inferenceInputId": "9f809662-f849-4c2f-87a3-32b3ca2fd870"
        },
        {
            "name": "Mortality",
            "value": "70",
            "inferenceInputId": "d735dbb6-831f-467c-8ad8-014bfb399e3d"
        }
    ],
    "labels": [
        {
            "id": "09bd62cc-e78f-4598-91c6-b621dde15f7b",
            "name": "death",
            "result": "175.33518570661545",
            "explainabilities": [
                {
                    "id": "36434ee5-def3-4911-9db9-f0882626602e",
                    "inferenceInputId": "9d009ea9-2537-45c0-a830-78088bbe1ca2",
                    "value": "-0.0570819394940800"
                },
                {
                    "id": "078aa033-5687-43f6-9c83-29a9153118d0",
                    "inferenceInputId": "886d3954-c9a4-454b-b7ee-9bea90bdd286",
                    "value": "0.0290308273450400"
                }
            ]
        }
    ]
}

{
    "status": 200,
    "result": {
        "id": "{{INFERENCE_ID}}",
        "startInference": "2022-09-17T23:44:03.000Z",
        "endInference": "2022-09-17T23:44:08.000Z",
        "inputs": "https://api.arkangel.ai/images/d3e0ee5be7eb4a7e683e7a4605d5310082bd.png",
        "labels": [
            {
                "id": "9e48bbdb-3583-45ea-afc9-89b727bcf65c",
                "name": "Animal",
                "classes": [
                    {
                        "result": "0.00000000000011",
                        "labelStr": "Perro"
                    },
                    {
                        "result": "0.99999988079071",
                        "labelStr": "Gato"
                    }
                ]
            }
        ]
    }
}

{
    "status": 200,
    "result": {
        "id": "{{INFERENCE_ID}}",
        "startInference": "2022-10-07T14:39:12.000Z",
        "endInference": "2022-10-07T14:39:20.000Z",
        "inputs": "https://api.arkangel.ai/images/1c51a8c8246b25dcac0bcc7df179c56ddd29.jpeg",
        "confidencePrediction": [
            {
                "labelStr": "Carro",
                "result": "0.86016124486923",
                "boundingBox": [
                    "0.422422856092453",
                    "0.44568416476249695",
                    "0.6098942756652832",
                    "0.5190061330795288"
                ]
            },
            {
                "labelStr": "Persona",
                "result": "0.32046729326248",
                "boundingBox": [
                    "0.5168230533599854",
                    "0.4071381092071533",
                    "0.707597017288208",
                    "0.4853692650794983"
                ]
            },
            {
                "labelStr": "Perro",
                "result": "0.25262472033501",
                "boundingBox": [
                    "0.5129784345626831",
                    "0.4877077639102936",
                    "0.6648222208023071",
                    "0.6258601546287537"
                ]
            }
        ]
    }
}

Example request

import requests

# Replace this with the inference ID
INFERENCE_ID= '{{INFERENCE_ID}}'
url = "https://api.arkangel.ai/api/inference/{{INFERENCE_ID}}"

payload={}
# Obtain user's web token
headers = {
  'Authorization': 'Bearer {{TOKEN}}'
}

# Configure the information created before of the project and obtain response
response = requests.request("GET", url, headers=headers, data=payload)

print(response.text)

const token = "{{TOKEN}}";
const inferenceId = "{{INFERENCE_ID}}"

const myHeaders = new Headers();
myHeaders.append("Authorization", `Bearer ${token}`);

const requestOptions = {
  method: 'GET',
  headers: myHeaders,
  redirect: 'follow'
};

fetch(`https://api.arkangel.ai/api/inference/${inferenceId}`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

curl --location --request GET 'https://api.arkangel.ai/api/inference/{{INFERENCE_ID}} \
--header 'Authorization: Bearer {{TOKEN}}'

List Targets of the model:

GET https://api.arkangel.ai/api/models/{{MODEL_ID}}/labels

Path Parameters

Name

Type

Description

{
    "status": 200,
    "result": [
        {
            "id": "UUID",
            "nameStr": "target name",
            "createdAt": "2023-02-24T17:50:31.614Z",
            "updatedAt": "2023-02-24T17:50:31.614Z",
            "classes": []
        }
    ]
}

{
    "status" 200,
    "result": [
        {
            "id": "UUID",
            "nameStr": "Target name",
            "createdAt": "2022-11-25T17:22:13.664Z",
            "updatedAt": "2022-11-25T17:22:13.664Z",
            "classes": [
                {
                    "id": "UUID",
                    "labelStr": "name option 1",
                    "createdAt": "2022-11-25T17:22:13.664Z",
                    "updatedAt": "2022-11-25T17:22:13.664Z"
                },
                {
                    "id": "UUID",
                    "labelStr": "name option 2",
                    "createdAt": "2022-11-25T17:22:13.664Z",
                    "updatedAt": "2022-11-25T17:22:13.664Z"
                }
            ]
        }
    ]
}

Example request

import requests

# Replace this with the model ID
MODEL_ID= '{{MODEL_ID}}'
url = "https://api.arkangel.ai/api/models/{{MODEL_ID}}/labels"

payload={}
# Obtain user's web token
headers = {
  'Authorization': 'Bearer {{TOKEN}}'
}

# Configure the information created before of the project and obtain response
response = requests.request("GET", url, headers=headers, data=payload)

print(response.text)

const token = "{{TOKEN}}";
const modelId = "{{MODEL_ID}}";

const myHeaders = new Headers();
myHeaders.append("Authorization", `Bearer ${token}`);

const requestOptions = {
  method: 'GET',
  headers: myHeaders,
  redirect: 'follow'
};

fetch(`https://api.arkangel.ai/api/models/${modelId}/labels`, requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

curl --location 'https://apidev.arkangel.ai/api/models/{{MODEL_ID}}/labels' \
--header 'Authorization: Bearer {{TOKEN}}'

Annotate prediction:

GET https://api.arkangel.ai/api/inference/annotations

Request Body

Name

Type

Description

{
    "status": 200,
    "result": {
        "annotated": false,
        "annotations" : [
            {
               "labelId": "UUID",
               "labelStr": "Label of the target",
               "annotated": "value of the annotation",
               "createdAt": "2023-03-27T15:29:30.088Z",
               "updatedAt": "2023-03-27T15:29:30.088Z"
            }
        ],
        "clientId": "",
        "comment": "",
        "endInference": "2023-03-27T15:29:24.000Z",
        "id": "INFERENCE_ID",
        "inputs": [],
        "labels": [],
        "startInference": "2023-03-27T15:29:22.000Z"
    }
}

{
    "status": 200,
    "result": {
        "annotated": false,
        "annotations" : [
            {
               "labelId": "UUID",
               "labelStr": "Label of the target",
               "annotated": "value of the annotation",
               "createdAt": "2023-03-27T15:29:30.088Z",
               "updatedAt": "2023-03-27T15:29:30.088Z"
            }
        ],
        "clientId": "",
        "comment": "",
        "endInference": "2023-03-27T15:29:24.000Z",
        "id": "INFERENCE_ID",
        "inputs": [],
        "labels": [],
        "startInference": "2023-03-27T15:29:22.000Z"
    }
}

Example request

import requests

# Replace this with the inference ID
INFERENCE_ID= '{{INFERENCE_ID}}'
url = "https://apidev.arkangel.ai/api/inference/annotations"

payload={
  'inferenceId': INFERENCE_ID,
  "annotated" : true | false,
  # Objects for each Target of the project
  # This field is only sent if the property "annotated" is true
  'annotations': [
    # Example if the project is of type Classification
    {
        "labelId": "TARGET_ID",
        "classId": "CLASS_ID"
    },
    # Example if the project is of type Regression
    {
        "labelId": "TARGET_ID",
        "result": "Number value"
    }
  ]
}
# Obtain user's web token
headers = {
  'Authorization': 'Bearer {{TOKEN}}'
}

# Configure the information created before of the project and obtain response
response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

const token = "{{TOKEN}}";
const inferenceId = "{{INFERENCE_ID}}"
const modelId = "{{MODEL_ID}}";

const myHeaders = new Headers();
myHeaders.append("Authorization", `Bearer ${token}`);

const data = {
  'inferenceId': inferenceId,
  "annotated" : true | false,
  // Objects for each Target of the project
  // This field is only sent if the property "annotated" is true
  'annotations': [
    // Example if the project is of type Classification
    {
        "labelId": "TARGET_ID",
        "classId": "CLASS_ID"
    },
    // Example if the project is of type Regression
    {
        "labelId": "TARGET_ID",
        "result": "Number value"
    }
  ]
}

const requestOptions = {
  method: 'GET',
  headers: myHeaders,
  redirect: 'follow'
  body: data
};

fetch("https://apidev.arkangel.ai/api/inference/annotations", requestOptions)
  .then(response => response.text())
  .then(result => console.log(result))
  .catch(error => console.log('error', error));

curl --location 'https://apidev.arkangel.ai/api/inference/annotations' \
--data '{
    "inferenceId": "",
    "annotated" : true | false,
    "annotations": [
        {
            "labelId": "TARGET_ID",
            "result": "CLASS_ID"
        },
        {
            "labelId": "TARGET_ID",
            "result": "Number value"
        }
    ]
}'

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