> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.labric.co/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.labric.co/_mcp/server.

# Predict

POST https://platform.labric.co/api/v1/tools/predict
Content-Type: application/json

Run predictions with a trained ML model.

Identify the model by ml_model_id, or by ml_model_name (the name of a
non-archived model). Each row in data maps the model's feature columns to
values -- use the ml-models tool to discover models and the columns each
expects. Returns one prediction per input row, plus per-class
probabilities for classifiers.

Reference: https://docs.labric.co/api-reference/labric-api/tools/predict

## OpenAPI Specification

```yaml
openapi: 3.1.0
info:
  title: openapi-tools
  version: 1.0.0
paths:
  /api/v1/tools/predict:
    post:
      operationId: predict
      summary: Predict
      description: >-
        Run predictions with a trained ML model.


        Identify the model by ml_model_id, or by ml_model_name (the name of a

        non-archived model). Each row in data maps the model's feature columns
        to

        values -- use the ml-models tool to discover models and the columns each

        expects. Returns one prediction per input row, plus per-class

        probabilities for classifiers.
      tags:
        - tools
      parameters:
        - name: Authorization
          in: header
          description: Bearer authentication
          required: true
          schema:
            type: string
      responses:
        '200':
          description: OK
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/PredictResponseSchema'
        '400':
          description: Bad Request
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '401':
          description: Unauthorized
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '403':
          description: Forbidden
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '404':
          description: Not Found
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '422':
          description: Unprocessable Content
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ValidationErrorSchema'
        '500':
          description: Internal Server Error
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
        '502':
          description: Bad Gateway
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/ErrorSchema'
      requestBody:
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/ToolsPredictRequestSchema'
servers:
  - url: https://platform.labric.co
    description: https://platform.labric.co
components:
  schemas:
    ToolsPredictRequestSchema:
      type: object
      properties:
        ml_model_id:
          type:
            - string
            - 'null'
          format: uuid
        ml_model_name:
          type:
            - string
            - 'null'
        data:
          type: array
          items:
            type: object
            additionalProperties:
              description: Any type
      required:
        - data
      description: >-
        Predict request for the SDK/MCP tools surface.


        The model is identified by ml_model_id or by ml_model_name (the name of
        a

        non-archived model).
      title: ToolsPredictRequestSchema
    PredictedAnnotationSchema:
      type: object
      properties:
        file_id:
          type: string
          format: uuid
        label:
          type: string
        mask:
          type: string
        width:
          type: integer
        height:
          type: integer
      required:
        - file_id
        - label
        - mask
        - width
        - height
      description: |-
        A predicted mask shaped as an annotation payload, tied back to the
        input row's image file. Nothing is persisted by predict: to store it,
        pass label/mask/width/height to POST /images/{file_id}/annotations with
        is_autogenerated=true.
      title: PredictedAnnotationSchema
    PredictResponseSchema:
      type: object
      properties:
        predictions:
          type: array
          items:
            description: Any type
        probabilities:
          type:
            - array
            - 'null'
          items:
            type: object
            additionalProperties:
              description: Any type
        model_id:
          type: string
          format: uuid
        model_name:
          type: string
        annotations:
          type:
            - array
            - 'null'
          items:
            $ref: '#/components/schemas/PredictedAnnotationSchema'
      required:
        - predictions
        - model_id
        - model_name
      title: PredictResponseSchema
    ErrorSchema:
      type: object
      properties:
        detail:
          type: string
      required:
        - detail
      title: ErrorSchema
    ValidationErrorSchema:
      type: object
      properties:
        detail:
          type: array
          items:
            type: object
            additionalProperties:
              description: Any type
      required:
        - detail
      description: Shape of Ninja's built-in 422 request-validation error response.
      title: ValidationErrorSchema
  securitySchemes:
    ApiKeyAuth:
      type: http
      scheme: bearer

```

## Examples



**Request**

```json
{
  "data": [
    {}
  ]
}
```

**Response**

```json
{
  "predictions": [
    "cat"
  ],
  "model_id": "3fa85f64-5717-4562-b3fc-2c963f66afa6",
  "model_name": "ImageClassifierV2",
  "probabilities": [
    {}
  ],
  "annotations": [
    {
      "file_id": "7c9e6679-7425-40de-944b-e07fc1f90ae7",
      "label": "cat",
      "mask": "R0lGODlhAQABAIAAAAUEBA==",
      "width": 640,
      "height": 480
    }
  ]
}
```

**SDK Code**

```python
import requests

url = "https://platform.labric.co/api/v1/tools/predict"

payload = { "data": [{}] }
headers = {
    "Authorization": "Bearer <api_key>",
    "Content-Type": "application/json"
}

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

print(response.json())
```

```javascript
const url = 'https://platform.labric.co/api/v1/tools/predict';
const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <api_key>', 'Content-Type': 'application/json'},
  body: '{"data":[{}]}'
};

try {
  const response = await fetch(url, options);
  const data = await response.json();
  console.log(data);
} catch (error) {
  console.error(error);
}
```

```go
package main

import (
	"fmt"
	"strings"
	"net/http"
	"io"
)

func main() {

	url := "https://platform.labric.co/api/v1/tools/predict"

	payload := strings.NewReader("{\n  \"data\": [\n    {}\n  ]\n}")

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

	req.Header.Add("Authorization", "Bearer <api_key>")
	req.Header.Add("Content-Type", "application/json")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(res)
	fmt.Println(string(body))

}
```

```ruby
require 'uri'
require 'net/http'

url = URI("https://platform.labric.co/api/v1/tools/predict")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <api_key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"data\": [\n    {}\n  ]\n}"

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

```java
import com.mashape.unirest.http.HttpResponse;
import com.mashape.unirest.http.Unirest;

HttpResponse<String> response = Unirest.post("https://platform.labric.co/api/v1/tools/predict")
  .header("Authorization", "Bearer <api_key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"data\": [\n    {}\n  ]\n}")
  .asString();
```

```php
<?php
require_once('vendor/autoload.php');

$client = new \GuzzleHttp\Client();

$response = $client->request('POST', 'https://platform.labric.co/api/v1/tools/predict', [
  'body' => '{
  "data": [
    {}
  ]
}',
  'headers' => [
    'Authorization' => 'Bearer <api_key>',
    'Content-Type' => 'application/json',
  ],
]);

echo $response->getBody();
```

```csharp
using RestSharp;

var client = new RestClient("https://platform.labric.co/api/v1/tools/predict");
var request = new RestRequest(Method.POST);
request.AddHeader("Authorization", "Bearer <api_key>");
request.AddHeader("Content-Type", "application/json");
request.AddParameter("application/json", "{\n  \"data\": [\n    {}\n  ]\n}", ParameterType.RequestBody);
IRestResponse response = client.Execute(request);
```

```swift
import Foundation

let headers = [
  "Authorization": "Bearer <api_key>",
  "Content-Type": "application/json"
]
let parameters = ["data": [[]]] as [String : Any]

let postData = JSONSerialization.data(withJSONObject: parameters, options: [])

let request = NSMutableURLRequest(url: NSURL(string: "https://platform.labric.co/api/v1/tools/predict")! as URL,
                                        cachePolicy: .useProtocolCachePolicy,
                                    timeoutInterval: 10.0)
request.httpMethod = "POST"
request.allHTTPHeaderFields = headers
request.httpBody = postData as Data

let session = URLSession.shared
let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in
  if (error != nil) {
    print(error as Any)
  } else {
    let httpResponse = response as? HTTPURLResponse
    print(httpResponse)
  }
})

dataTask.resume()
```