🔮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
{{MODEL_ID}}*	UUID	Model ID
{{PROJECT_ID}}*	UUID	Project ID

Headers

Name	Type	Description
TOKEN*	JWT	Bearer TOKEN

200: OK

{
    "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

Python

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)

Javascript

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

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

---

Create public user:

This is important if you want to make inferences on projects that are enabled as public with users not registered in the app. If an already created email is sent, it will return the existing one.

POST https://api.arkangel.ai/api/public-users

The body must be in JSON format

Request Body

Name	Type	Description
email*	String	user email

200: OK

{
    "status": 201,
    "result": {
        "id": "6cc146ed-ecd5-4ba9-9547-51a453fc826c",
        "email": "[e-mail]",
        "createdAt": "2024-05-17T18:43:07.560Z",
        "updatedAt": "2024-05-17T18:43:07.560Z"
    }
}

Example request

Python

import requests
import json

url = "https://apidev.arkangel.ai/api/public-users"

payload = json.dumps({
  "email": "[e-mail]"
})
headers = {
  'Content-Type': 'application/json'
}

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

print(response.text)

Javascript

const myHeaders = new Headers();
myHeaders.append("Content-Type", "application/json");

const raw = JSON.stringify({
  "email": "[e-mail]"
});

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

fetch("{{API}}/api/public-users", requestOptions)
  .then((response) => response.text())
  .then((result) => console.log(result))
  .catch((error) => console.error(error));

cURL

curl --location --globoff 'https://apidev.arkangel.ai/api/public-users' \
--header 'Content-Type: application/json' \
--data-raw '{
    "email": "[e-mail]"
}'

---

Create prediction:

Create a prediction

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

The body must be in JSON format

Headers

Name	Type	Description
TOKEN*	JWT	Bearer TOKEN (Not necessary if an inference is to be made with a public user)

If the project is public, you can make inferences without sending the token, but you must create a public user and send the id of the created user.

Request Body

Name	Type	Description
projectId*	UUID	PROJECT_ID
modelId*	UUID	MODEL_ID
inputs*	Array<Inputs>
clientId	String	Name of inference (Not necessary if an inference is to be made with a public user)
publicUserId	UUID	PUBLIC_USER_ID

If you want to make an inference without providing the token, the publicUserId property is required.

201: Created Response for tabulated classification data

{
    "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": []
}

201: Created Response for tabulated regression data

{
    "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:

Python

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)

Javascript

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

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

Python

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)

Javascript

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

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
TOKEN*	JWT	Bearer TOKEN

Request Body

Name	Type	Description
modelIds*	Array	[ MODEL_IDS ]
projectId*	String	PROJECT_ID
batch*	File	This will be the .csv containing the predictions

201: OK

{
    "status": 201,
    "result": "Multi-inference started"
}

231B

example_file.csv

Example of use for example_file.csv

clientId	27a9d20a-9c89-42b6	9c89-42b6-27a9d20a	42b6-27a9d20a-9c89
	Name	Year	Age
Example 1	Carlos	1999	23
Example 2	Marina	1995	27

The CSV file should have a maximum of 50.000 cells.

Example request

Python

import requests

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

payload = {
  'projectId': '{{PROJECT_ID}}',
  'modelIds': [ '{{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)

Javascript

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("modelIds", [ 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

curl --location --globoff 'https://api.arkangel.ai/api/inference/batch' \
--header 'Authorization: Bearer {{TOKEN}}' \
--form 'projectId="{{PROJECT_ID}}"' \
--form 'modelIds="[\"{{MODEL_ID}}\"]"' \
--form 'batch=@"/home/user/documents/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
{{PROJECT_ID}}*	UUID	Project ID
{{MODEL_ID}}*	UUID	Model ID

Query Parameters

Name	Type	Description
limit*	int	{{LIMIT}}
offset*	int	{{OFFSET}}

200: OK

{
    "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

Python

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)

Javascript

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

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
Inference Id*	UUID	{{INFERENCE_ID}}

200: OK Data Classfication

{
    "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"
                    }
                ]
            }
        ]
    }
}

200: OK Data Regression

{
    "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"
                }
            ]
        }
    ]
}

200: OK Vision

{
    "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"
                    }
                ]
            }
        ]
    }
}

200: OK Object Detection

{
    "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

Python

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)

Javascript

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

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
MODEL_ID*	UUID	{{MODEL_ID}}

200: OK Regression

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

200: OK Classification

{
    "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

Python

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)

Javascript

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

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
inferenceId*	UUID	INFERENCE_ID
annotations*	Array<object>	Annotations by Target - Objects for each Target of the project
annotated*	Boolean	Type of annotation - true: valid annotation | false: invalid annotation

200: OK Classification

{
    "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"
    }
}

200: OK Regression

{
    "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

Python

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)

Javascript

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

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"
        }
    ]
}'