Create Image Annotation Task with Scale AI API on New Requests (Payload Only) from HTTP / Webhook API

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New Requests (Payload Only) from the HTTP / Webhook API

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Create Image Annotation Task with the Scale AI API

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Getting Started#

This integration creates a workflow with a HTTP / Webhook trigger and Scale AI action. When you configure and deploy the workflow, it will run on Pipedream's servers 24x7 for free.

Select this integration
Configure the New Requests (Payload Only) trigger
1. Connect your HTTP / Webhook account
Configure the Create Image Annotation Task action
1. Connect your Scale AI account
2. Optional- Select a Project
3. Optional- Configure Batch
4. Optional- Configure Instruction
5. Optional- Configure Callback URL
6. Configure Attachment
7. Optional- Configure Context Attachments
8. Optional- Configure Box
9. Optional- Configure Polygon
10. Optional- Configure Line
11. Optional- Configure Point
12. Optional- Configure Cuboid
13. Optional- Configure Ellipse
14. Optional- Configure Padding
15. Optional- Configure Base Annotations
16. Optional- Configure Can Add Base Annotations
17. Optional- Configure Can Edit Base Annotations
18. Optional- Configure Can Edit Base Annotation Labels
19. Optional- Configure Can Delete Base Annotations
20. Optional- Configure Padding X
21. Optional- Configure Padding Y
22. Optional- Configure Priority
23. Optional- Configure Unique ID
24. Optional- Configure Clear Unique ID On Error
Deploy the workflow
Send a test event to validate your setup
Turn on the trigger

Details#

This integration uses pre-built, source-available components from Pipedream's GitHub repo. These components are developed by Pipedream and the community, and verified and maintained by Pipedream.

To contribute an update to an existing component or create a new component, create a PR on GitHub. If you're new to Pipedream component development, you can start with quickstarts for trigger span and action development, and then review the component API reference.

Trigger#

New Requests (Payload Only) on HTTP / Webhook

Description:Get a URL and emit the HTTP body as an event on every request

Version:0.1.1

Key:http-new-requests-payload-only

View on GitHub

HTTP / Webhook Overview#

Build, test, and send HTTP requests without code using your Pipedream workflows. The HTTP / Webhook action is a tool to build HTTP requests with a Postman-like graphical interface.

Point and click HTTP requests

Define the target URL, HTTP verb, headers, query parameters, and payload body without writing custom code.

$A screenshot of Pipedream's HTTP Request Configuration interface with a GET request type selected. The request URL is set to 'https://api.openai.com/v1/models'. The 'Auth' tab is highlighted, indicating that authentication is required for this request. In the headers section, there are two headers configured: 'User-Agent' is set to 'pipedream/1', and 'Authorization' is set to 'Bearer {{openai_api_key}}', showing how the OpenAI account's API key is dynamically inserted into the headers to handle authentication automatically.$

Here's an example workflow that uses the HTTP / Webhook action to send an authenticated API request to OpenAI.

Focus on integrating, not authenticating

This action can also use your connected accounts with third-party APIs. Selecting an integrated app will automatically update the request’s headers to authenticate with the app properly, and even inject your token dynamically.

Pipedream integrates with thousands of APIs, but if you can’t find a Pipedream integration simply use Environment Variables in your request headers to authenticate with.

Compatible with no code actions or Node.js and Python

The HTTP/Webhook action exports HTTP response data for use in subsequent workflow steps, enabling easy data transformation, further API calls, database storage, and more.

Response data is available for both coded (Node.js, Python) and no-code steps within your workflow.

Trigger Code#

import http from "../../http.app.mjs";

// Core HTTP component
// Returns a 200 OK response, emits the HTTP payload as an event
export default {
  key: "http-new-requests-payload-only",
  name: "New Requests (Payload Only)",
  // eslint-disable-next-line
  description: "Get a URL and emit the HTTP body as an event on every request",
  version: "0.1.1",
  type: "source",
  props: {
    // eslint-disable-next-line
    httpInterface: {
      type: "$.interface.http",
      customResponse: true,
    },
    http,
  },
  async run(event) {
    const { body } = event;
    this.httpInterface.respond({
      status: 200,
      body,
    });
    // Emit the HTTP payload
    this.$emit({
      body,
    });
  },
};

Trigger Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI and CLI.

Label	Prop	Type	Description
N/A	`httpInterface`	`$.interface.http`	This component uses `$.interface.http` to generate a unique URL when the component is first instantiated. Each request to the URL will trigger the `run()` method of the component.
HTTP / Webhook	`http`	`app`	This component uses the HTTP / Webhook app.

Trigger Authentication#

The HTTP / Webhook API does not require authentication.

About HTTP / Webhook#

Get a unique URL where you can send HTTP or webhook requests

Action#

Create Image Annotation Task on Scale AI

Description:Create an image annotation task. [See the documentation](https://docs.scale.com/reference/general-image-annotation)

Version:0.0.2

Key:scale_ai-create-image-annotation-task

View on GitHub

Scale AI Overview#

Scale AI offers an API to automate and streamline data labeling for machine learning applications, providing access to a global workforce and sophisticated tools. With Scale AI's API on Pipedream, you can integrate scalable data annotation workflows directly into your apps. Trigger tasks, manage datasets, and receive annotated data, all within Pipedream's serverless platform. This enables seamless automation of labeling tasks, integration with machine learning pipelines, and real-time updates on annotations.

Action Code#

import app from "../../scale_ai.app.mjs";
import utils from "../../common/utils.mjs";

export default {
  key: "scale_ai-create-image-annotation-task",
  name: "Create Image Annotation Task",
  description: "Create an image annotation task. [See the documentation](https://docs.scale.com/reference/general-image-annotation)",
  type: "action",
  version: "0.0.2",
  props: {
    app,
    project: {
      propDefinition: [
        app,
        "project",
      ],
    },
    batch: {
      propDefinition: [
        app,
        "batch",
      ],
    },
    instruction: {
      propDefinition: [
        app,
        "instruction",
      ],
    },
    callbackUrl: {
      propDefinition: [
        app,
        "callbackUrl",
      ],
    },
    attachment: {
      propDefinition: [
        app,
        "attachment",
      ],
      optional: false,
    },
    contextAttachments: {
      type: "string[]",
      label: "Context Attachments",
      description: "An array of strings with links to actual attachments (A URI pointing to an attachment that provides additional context. Will be shown to the user below the main attachment, and can be made full screen.) to show to taskers as a reference. Context images themselves can not be labeled. Context images will appear [like this](https://i.imgur.com/MJ7ZbMt.mp4) in the UI. You cannot use the task's attachment url as a context attachment's url.",
      optional: true,
    },
    box: {
      type: "string",
      label: "Box",
      description: "Parameters for box geometries. See [Boxes](https://docs.scale.com/reference/boxes) for details about the parameter and response fields. Eg. `{\"min_height\": 10, \"min_width\": 10, \"can_rotate\": true, \"integer_pixels\": true, \"objects_to_annotate\": [\"car\", \"truck\"]}`",
      optional: true,
    },
    polygon: {
      type: "string",
      label: "Polygon",
      description: "Parameters for polygon geometries. See [Polygons](https://docs.scale.com/reference/polygons) for details about the parameter and response fields. Eg. `{\"min_vertices\": 3, \"max_vertices\": 5, \"objects_to_annotate\": [\"car\", \"truck\"]}`",
      optional: true,
    },
    line: {
      type: "string",
      label: "Line",
      description: "Parameters for line geometries. See [Lines](https://docs.scale.com/reference/lines) for details about the parameter and response fields. Eg. `{\"min_vertices\": 2, \"max_vertices\": 4, \"objects_to_annotate\": [\"car\", \"truck\"]}`",
      optional: true,
    },
    point: {
      type: "string",
      label: "Point",
      description: "Parameters for point geometries. See [Points](https://docs.scale.com/reference/points) for details about the parameter and response fields. Eg. `{\"objects_to_annotate\": [\"car\", \"truck\"]}`",
      optional: true,
    },
    cuboid: {
      type: "string",
      label: "Cuboid",
      description: "Parameters for cuboid geometries. See [Cuboids](https://docs.scale.com/reference/cuboids) for details about the parameter and response fields. Eg. `{\"min_height\": 10, \"min_width\": 10, \"camera_intrinsics\": {\"fx\": 100, \"fy\": 100, \"cx\": 100, \"cy\": 100, \"skew\": 0, \"scalefactor\": 1}, \"camera_rotation_quaternion\": {\"w\": 1, \"x\": 0, \"y\": 0, \"z\": 0}, \"camera_height\": 100, \"objects_to_annotate\": [\"car\", \"truck\"]}`",
      optional: true,
    },
    ellipse: {
      type: "string",
      label: "Ellipse",
      description: "Parameters for ellipse geometries. See [Ellipse](https://docs.scale.com/reference/ellipses) for details about the parameter and response fields. Eg. `{\"objects_to_annotate\": [\"car\", \"truck\"]}`",
      optional: true,
    },
    padding: {
      type: "integer",
      label: "Padding",
      description: "The amount of padding in pixels added to the top, bottom, left, and right of the image. This allows labelers to extend annotations outside of the image. When using padding, annotation coordinates can be a negative value or greater than the width/height of the image. See [visual example](https://i.imgur.com/OMJv9gi.png).",
      optional: true,
    },
    baseAnnotations: {
      type: "object",
      label: "Base Annotations",
      description: "Editable annotations, with the option to be **locked**, that a task should be initialized with. This is useful when you've run a model to prelabel the task and want annotators to refine those prelabels. Must contain the annotations field, which has the same format as the annotations field in the response.",
      optional: true,
    },
    canAddBaseAnnotations: {
      type: "boolean",
      label: "Can Add Base Annotations",
      description: "Whether or not the tasker can add base annotations.",
      optional: true,
    },
    canEditBaseAnnotations: {
      type: "boolean",
      label: "Can Edit Base Annotations",
      description: "Whether or not the tasker can edit base annotations.",
      optional: true,
    },
    canEditBaseAnnotationLabels: {
      type: "boolean",
      label: "Can Edit Base Annotation Labels",
      description: "Whether or not the tasker can edit base annotation labels.",
      optional: true,
    },
    canDeleteBaseAnnotations: {
      type: "boolean",
      label: "Can Delete Base Annotations",
      description: "Whether or not base_annotations can be removed from the task. If set to true, base_annotations can be deleted from the task. If set to false, base_annotations cannot be deleted from the task.",
      optional: true,
    },
    paddingX: {
      type: "integer",
      label: "Padding X",
      description: "The amount of padding in pixels added to the left and right of the image. Overrides *Padding* if set.",
      optional: true,
    },
    paddingY: {
      type: "integer",
      label: "Padding Y",
      description: "The amount of padding in pixels added to the top and bottom of the image. Overrides *Padding* if set.",
      optional: true,
    },
    priority: {
      propDefinition: [
        app,
        "priority",
      ],
    },
    uniqueId: {
      propDefinition: [
        app,
        "uniqueId",
      ],
    },
    clearUniqueIdOnError: {
      type: "boolean",
      label: "Clear Unique ID On Error",
      description: "If set to be true, if a task errors out after being submitted, the *Unique ID* on the task will be unset. This param allows workflows where you can re-submit the same unique id to recover from errors automatically.",
      optional: true,
    },
  },
  methods: {
    createImageAnnotationTask(args = {}) {
      return this.app.post({
        path: "/task/imageannotation",
        ...args,
      });
    },
  },
  async run({ $: step }) {
    const {
      createImageAnnotationTask,
      project,
      batch,
      instruction,
      callbackUrl,
      attachment,
      contextAttachments,
      box,
      polygon,
      line,
      point,
      cuboid,
      ellipse,
      padding,
      paddingX,
      paddingY,
      priority,
      baseAnnotations,
      canAddBaseAnnotations,
      canEditBaseAnnotations,
      canEditBaseAnnotationLabels,
      canDeleteBaseAnnotations,
      uniqueId,
      clearUniqueIdOnError,
    } = this;

    const response = await createImageAnnotationTask({
      step,
      data: {
        project,
        batch,
        instruction,
        callback_url: callbackUrl,
        attachment,
        context_attachments: contextAttachments?.map((attachment) => ({
          attachment,
        })),
        geometries: {
          box: utils.parse(box),
          polygon: utils.parse(polygon),
          line: utils.parse(line),
          point: utils.parse(point),
          cuboid: utils.parse(cuboid),
          ellipse: utils.parse(ellipse),
        },
        padding,
        paddingX,
        paddingY,
        priority,
        base_annotations: baseAnnotations,
        can_add_base_annotations: canAddBaseAnnotations,
        can_edit_base_annotations: canEditBaseAnnotations,
        can_edit_base_annotation_labels: canEditBaseAnnotationLabels,
        can_delete_base_annotations: canDeleteBaseAnnotations,
        unique_id: uniqueId,
        clear_unique_id_on_error: clearUniqueIdOnError,
      },
    });

    step.export("$summary", `Successfully created image annotation task with ID \`${response.task_id}\``);

    return response;
  },
};

Action Configuration#

This component may be configured based on the props defined in the component code. Pipedream automatically prompts for input values in the UI.

Label	Prop	Type	Description
Scale AI	`app`	`app`	This component uses the Scale AI app.
Project	`project`	`string`	Select a value from the drop down menu.
Batch	`batch`	`string`	The name of the batch to associate this task with. Note that if a batch is specified, you need not specify the project, as the task will automatically be associated with the batch's project. For Scale Rapid projects specifying a batch is required. See Batches section for more details.
Instruction	`instruction`	`string`	A markdown-enabled string or iframe embedded Google Doc explaining how to do the task. You can use markdown to show example images, give structure to your instructions, and more. See our instruction best practices for more details. For Scale Rapid projects, DO NOT set this field unless you specifically want to override the project level instructions.
Callback URL	`callbackUrl`	`string`	The full url (including the scheme `http://` or `https://`) or email address of the callback that will be used when the task is completed.
Attachment	`attachment`	`string`	A URL to the image you'd like to be annotated.
Context Attachments	`contextAttachments`	`string[]`	An array of strings with links to actual attachments (A URI pointing to an attachment that provides additional context. Will be shown to the user below the main attachment, and can be made full screen.) to show to taskers as a reference. Context images themselves can not be labeled. Context images will appear like this in the UI. You cannot use the task's attachment url as a context attachment's url.
Box	`box`	`string`	Parameters for box geometries. See Boxes for details about the parameter and response fields. Eg. `{"min_height": 10, "min_width": 10, "can_rotate": true, "integer_pixels": true, "objects_to_annotate": ["car", "truck"]}`
Polygon	`polygon`	`string`	Parameters for polygon geometries. See Polygons for details about the parameter and response fields. Eg. `{"min_vertices": 3, "max_vertices": 5, "objects_to_annotate": ["car", "truck"]}`
Line	`line`	`string`	Parameters for line geometries. See Lines for details about the parameter and response fields. Eg. `{"min_vertices": 2, "max_vertices": 4, "objects_to_annotate": ["car", "truck"]}`
Point	`point`	`string`	Parameters for point geometries. See Points for details about the parameter and response fields. Eg. `{"objects_to_annotate": ["car", "truck"]}`
Cuboid	`cuboid`	`string`	Parameters for cuboid geometries. See Cuboids for details about the parameter and response fields. Eg. `{"min_height": 10, "min_width": 10, "camera_intrinsics": {"fx": 100, "fy": 100, "cx": 100, "cy": 100, "skew": 0, "scalefactor": 1}, "camera_rotation_quaternion": {"w": 1, "x": 0, "y": 0, "z": 0}, "camera_height": 100, "objects_to_annotate": ["car", "truck"]}`
Ellipse	`ellipse`	`string`	Parameters for ellipse geometries. See Ellipse for details about the parameter and response fields. Eg. `{"objects_to_annotate": ["car", "truck"]}`
Padding	`padding`	`integer`	The amount of padding in pixels added to the top, bottom, left, and right of the image. This allows labelers to extend annotations outside of the image. When using padding, annotation coordinates can be a negative value or greater than the width/height of the image. See visual example.
Base Annotations	`baseAnnotations`	`object`	Editable annotations, with the option to be locked, that a task should be initialized with. This is useful when you've run a model to prelabel the task and want annotators to refine those prelabels. Must contain the annotations field, which has the same format as the annotations field in the response.
Can Add Base Annotations	`canAddBaseAnnotations`	`boolean`	Whether or not the tasker can add base annotations.
Can Edit Base Annotations	`canEditBaseAnnotations`	`boolean`	Whether or not the tasker can edit base annotations.
Can Edit Base Annotation Labels	`canEditBaseAnnotationLabels`	`boolean`	Whether or not the tasker can edit base annotation labels.
Can Delete Base Annotations	`canDeleteBaseAnnotations`	`boolean`	Whether or not base_annotations can be removed from the task. If set to true, base_annotations can be deleted from the task. If set to false, base_annotations cannot be deleted from the task.
Padding X	`paddingX`	`integer`	The amount of padding in pixels added to the left and right of the image. Overrides Padding if set.
Padding Y	`paddingY`	`integer`	The amount of padding in pixels added to the top and bottom of the image. Overrides Padding if set.
Priority	`priority`	`integer`	A value of 10, 20, or 30 that defines the priority of a task within a project. The higher the number, the higher the priority.
Unique ID	`uniqueId`	`string`	A arbitrary ID that you can assign to a task and then query for later. This ID must be unique across all projects under your account, otherwise the task submission will be rejected. See Avoiding Duplicate Tasks for more details.
Clear Unique ID On Error	`clearUniqueIdOnError`	`boolean`	If set to be true, if a task errors out after being submitted, the Unique ID on the task will be unset. This param allows workflows where you can re-submit the same unique id to recover from errors automatically.

Action Authentication#

Scale AI uses API keys for authentication. When you connect your Scale AI account, Pipedream securely stores the keys so you can easily authenticate to Scale AI APIs in both code and no-code steps.

About Scale AI#

Make the best models with the best data. Scale Data Engine leverages your enterprise data, and with Scale Generative AI Platform, safely unlocks the value of AI.

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